To enhance the performance of the intelligent optimization algorithm, a new model of performing search on the Bloch sphere is proposed. Then, by integrating the model into the artificial fish swarm optimization, we present a quantum-inspired artificial fish swarm optimization algorithm. In proposed method, the fishes are encoded with the qubits described on the Bloch sphere. The vector product theory is adopted to establish the axis of rotation, and the Pauli matrices are used to construct the rotation matrices. The four fish behaviors, such as moving, tracking, capturing, aggregating, are achieved by rotating the current qubit about the rotation axis to the target qubit on the Bloch sphere. The Bloch coordinates of qubit can be obtained by measuring with the Pauli matrices, and the optimization solutions can be presented through the solution space transformation. The highlight advantages of this method are the ability to simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method obviously outperforms the classical one in convergence speed and achieves better levels for some benchmark functions.
Utlization Cat Swarm Optimization Algorithm for Selected Harmonic Elemination...IJPEDS-IAES
The voltage source inverter (VSI) and Current source inverter (CSI) are two
types of traditional power inverter topologies.In this paper selective
harmonic elimination (SHE) Algorithm was impelemented to CSI and results
has been investigated. Cat swarm (CSO) optimization is a new meta-heuristic
algorithm which has been used in order to tuning switching parameters in
optimized value.Objective fuction is reduction of total harmonic
distortion(THD) in inverters output currents.All of simulation has been
carried out in Matlab/Software.
What can we learn from molecular dynamics simulations of carbon nanotube and ...Stephan Irle
The document summarizes molecular dynamics simulations of carbon nanotube and graphene growth performed by the author and collaborators. It describes how density functional tight-binding molecular dynamics simulations were used to study: [1] acetylene decomposition on iron clusters, which led to polyacetylene formation and carbon cluster attachment; [2] cap nucleation by supplying carbon atoms to an iron cluster and annealing; and [3] sidewall growth through carbon atom insertion and ring formation. The simulations provided insights into carbon nanotube growth mechanisms at an atomic scale that are difficult to observe experimentally.
Density-Functional Tight-Binding (DFTB) as fast approximate DFT method - An i...Stephan Irle
This presentation was given April 27, 2013 at Ibaraki University in Mito, Japan (Professor Seiji Mori's group). The presentation does not claim to give a complete overview of the complex field of DFTB parameterization, but rather focuses on the method's central approximations and discusses its performance in various applications.
Improving initial generations in pso algorithm for transportation network des...ijcsit
Transportation Network Design Problem (TNDP) aims to select the best project sets among a number of new projects. Recently, metaheuristic methods are applied to solve TNDP in the sense of finding better solutions sooner. PSO as a metaheuristic method is based on stochastic optimization and is a parallel revolutionary computation technique. The PSO system initializes with a number of random solutions and seeks for optimal solution by improving generations. This paper studies the behavior of PSO on account of improving initial generation and fitness value domain to find better solutions in comparison with previous attempts.
Nondeterministic testing of Sequential Quantum Logic Propositions on a Quant...Matthew Leifer
Talk given at "Quantum Information, Computation and Logic" workshop at Perimeter Institute in 2005.
Based on the preprint http://arxiv.org/abs/quant-ph/0509193
Talk was recorded and is viewable online at http://pirsa.org/05070102/
I now regard these ideas as flawed, but they may be revisited in future work.
Towards Auto-tuning Facilities into Supercomputers in Operation - The FIBER a...Takahiro Katagiri
This document summarizes work on auto-tuning facilities for supercomputers in operation. It discusses the FIBER approach, which performs auto-tuning at install-time, before execution, and run-time to minimize software stack requirements. It applies ppOpen-AT, an auto-tuning language, to an explicit finite difference method application called Seism3D. Various loop optimizations are explored, including loop splitting, fusion into one-dimensional and two-dimensional loops, to improve parallelism and pre-fetching opportunities. Performance is then evaluated on the target application and optimizations.
In this second lecture, I will discuss how to calculate polarization in terms of Berry phase, how to include GW correction in the real-time dynamics and electron-hole interaction.
This document proposes a bacterial foraging algorithm-based polyclonal selection algorithm (BFA-PSA) to improve the global path planning abilities of coal mine rescue robots in complex environments. The BFA-PSA introduces bacterial foraging algorithms into traditional clonal selection algorithms to address issues like slow convergence and getting stuck in local minima. The BFA-PSA combines bacterial chemotaxis, reproduction, and elimination-dispersal operators with clonal selection operations. Test results on traveling salesman problems and grid-based path planning simulations for coal mine rescue robots show the BFA-PSA has better searching abilities, efficiency, robustness, and validity compared to genetic and clonal selection algorithms.
Utlization Cat Swarm Optimization Algorithm for Selected Harmonic Elemination...IJPEDS-IAES
The voltage source inverter (VSI) and Current source inverter (CSI) are two
types of traditional power inverter topologies.In this paper selective
harmonic elimination (SHE) Algorithm was impelemented to CSI and results
has been investigated. Cat swarm (CSO) optimization is a new meta-heuristic
algorithm which has been used in order to tuning switching parameters in
optimized value.Objective fuction is reduction of total harmonic
distortion(THD) in inverters output currents.All of simulation has been
carried out in Matlab/Software.
What can we learn from molecular dynamics simulations of carbon nanotube and ...Stephan Irle
The document summarizes molecular dynamics simulations of carbon nanotube and graphene growth performed by the author and collaborators. It describes how density functional tight-binding molecular dynamics simulations were used to study: [1] acetylene decomposition on iron clusters, which led to polyacetylene formation and carbon cluster attachment; [2] cap nucleation by supplying carbon atoms to an iron cluster and annealing; and [3] sidewall growth through carbon atom insertion and ring formation. The simulations provided insights into carbon nanotube growth mechanisms at an atomic scale that are difficult to observe experimentally.
Density-Functional Tight-Binding (DFTB) as fast approximate DFT method - An i...Stephan Irle
This presentation was given April 27, 2013 at Ibaraki University in Mito, Japan (Professor Seiji Mori's group). The presentation does not claim to give a complete overview of the complex field of DFTB parameterization, but rather focuses on the method's central approximations and discusses its performance in various applications.
Improving initial generations in pso algorithm for transportation network des...ijcsit
Transportation Network Design Problem (TNDP) aims to select the best project sets among a number of new projects. Recently, metaheuristic methods are applied to solve TNDP in the sense of finding better solutions sooner. PSO as a metaheuristic method is based on stochastic optimization and is a parallel revolutionary computation technique. The PSO system initializes with a number of random solutions and seeks for optimal solution by improving generations. This paper studies the behavior of PSO on account of improving initial generation and fitness value domain to find better solutions in comparison with previous attempts.
Nondeterministic testing of Sequential Quantum Logic Propositions on a Quant...Matthew Leifer
Talk given at "Quantum Information, Computation and Logic" workshop at Perimeter Institute in 2005.
Based on the preprint http://arxiv.org/abs/quant-ph/0509193
Talk was recorded and is viewable online at http://pirsa.org/05070102/
I now regard these ideas as flawed, but they may be revisited in future work.
Towards Auto-tuning Facilities into Supercomputers in Operation - The FIBER a...Takahiro Katagiri
This document summarizes work on auto-tuning facilities for supercomputers in operation. It discusses the FIBER approach, which performs auto-tuning at install-time, before execution, and run-time to minimize software stack requirements. It applies ppOpen-AT, an auto-tuning language, to an explicit finite difference method application called Seism3D. Various loop optimizations are explored, including loop splitting, fusion into one-dimensional and two-dimensional loops, to improve parallelism and pre-fetching opportunities. Performance is then evaluated on the target application and optimizations.
In this second lecture, I will discuss how to calculate polarization in terms of Berry phase, how to include GW correction in the real-time dynamics and electron-hole interaction.
This document proposes a bacterial foraging algorithm-based polyclonal selection algorithm (BFA-PSA) to improve the global path planning abilities of coal mine rescue robots in complex environments. The BFA-PSA introduces bacterial foraging algorithms into traditional clonal selection algorithms to address issues like slow convergence and getting stuck in local minima. The BFA-PSA combines bacterial chemotaxis, reproduction, and elimination-dispersal operators with clonal selection operations. Test results on traveling salesman problems and grid-based path planning simulations for coal mine rescue robots show the BFA-PSA has better searching abilities, efficiency, robustness, and validity compared to genetic and clonal selection algorithms.
This document summarizes the bat algorithm, which is a metaheuristic optimization algorithm inspired by the echolocation behavior of microbats. It describes how bats use echolocation to locate prey and obstacles. The basic steps of the bat algorithm are outlined, including how bats emit calls and adjust properties like frequency and loudness. Variants of the bat algorithm are mentioned for solving multi-objective, fuzzy logic, and other problems. Applications discussed include engineering design, scheduling, data clustering, and image processing. Advantages include quick convergence and flexibility, while disadvantages include possible stagnation if parameters are adjusted too rapidly.
The document summarizes two nature-inspired metaheuristic algorithms: the Cuckoo Search algorithm and the Firefly algorithm.
The Cuckoo Search algorithm is based on the brood parasitism of some cuckoo species. It lays its eggs in the nests of other host birds. The algorithm uses Lévy flights for generating new solutions and considers the best solutions for the next generation.
The Firefly algorithm is based on the flashing patterns of fireflies to attract mates. It considers attractiveness that decreases with distance and movement of fireflies towards more attractive ones. The pseudo codes of both algorithms are provided along with some example applications.
Benchmark Calculations of Atomic Data for Modelling ApplicationsAstroAtom
This document summarizes benchmark calculations of atomic data for modeling applications. It discusses numerical methods like close-coupling and distorted-wave approaches for calculating atomic collision data. It provides selected results on energy levels, oscillator strengths, and electron-impact excitation cross sections. It also discusses applications to modeling neon discharges and takes a closer look at ionization calculations and examples. The document concludes by discussing the production and assessment of atomic data and outlines challenges in obtaining reliable data from both experiments and calculations.
A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTIONijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are
compared on twelve constrained nonlinear test functions. Generally, the results show that Differential
Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and
robustness.
Searches for new physics at LHC within the Higgs sector. Step 2: Defining the...Raquel Gomez Ambrosio
We discuss the Effective field theory bottom-up approach, and show some examples of its application for VH production at LHC. We find some interesting results regarding the applicability of the perturbative expansion. Finally we discuss the Pseudo Observable approach as a tool for New Physics searches at LHC.
Long Relaxation Times of a C-shunt Flux Qubit Coupled to a 3D CavityLeonid Abdurakhimov
1) Long relaxation times were observed in a c-shunt flux qubit coupled to a 3D microwave cavity. Relaxation times were longer than previous 2D qubit designs due to the cleaner electromagnetic environment and lower dielectric losses of the 3D cavity.
2) Qubit energy relaxation is due to quasiparticle tunneling or interactions with two-level system defects. Dephasing results from charge noise or critical current fluctuations near the optimal point and 1/f flux noise away from the optimal point.
3) Dynamical decoupling techniques such as CPMG pulse sequences can be used to reach T1-limited coherence by filtering out low-frequency noise sources.
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
Quantum ant colony optimization algorithm based onBloch spherical searchIJRES Journal
In the existing quantum-behaved optimization algorithms, almost all of the individuals are encoded by qubits described on plane unit circle. As qubits contain only a variable parameter, quantum properties have not been fully embodied, which limits the optimization ability rise further. In order to solve this problem, this paper proposes a quantum ant colony optimization algorithm based on Bloch sphere search. In the proposed algorithm, the positions of ants are encoded by qubits described on Bloch sphere. First, the destination to move is determined according to the select probability constructed by the pheromone and heuristic information, then, the rotation axis is established with Pauli matrixes, and the evolution search is realized with the rotation of qubits on Bloch sphere. In order to avoid premature convergence, the mutation is performed with Hadamard gates. Finally, the pheromone and the heuristic information are updated in the new positions of ants. As the optimization process is performed in n-dimensional hypercube space [−1, 1]n, which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. The simulation results show that the proposed algorithm is superior to other quantum-behaved optimization algorithms in both search ability and optimization efficiency.
Quantum ant colony optimization algorithm based onBloch spherical searchirjes
In the existing quantum-behaved optimization algorithms, almost all of the individuals are encoded by qubits described on plane unit circle. As qubits contain only a variable parameter, quantum properties have not been fully embodied, which limits the optimization ability rise further. In order to solve this problem, this paper proposes a quantum ant colony optimization algorithm based on Bloch sphere search. In the proposed algorithm, the positions of ants are encoded by qubits described on Bloch sphere. First, the destination to move is determined according to the select probability constructed by the pheromone and heuristic information, then, the rotation axis is established with Pauli matrixes, and the evolution search is realized with the rotation of qubits on Bloch sphere. In order to avoid premature convergence, the mutation is performed with Hadamard gates. Finally, the pheromone and the heuristic information are updated in the new positions of ants. As the optimization process is performed in n-dimensional hypercube space [−1, 1]n, which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. The simulation results show that the proposed algorithm is superior to other quantum-behaved optimization algorithms in both search ability and optimization efficiency.
Microscopic Image Analysis of Nanoparticles by Edge Detection Using Ant Colon...IOSR Journals
This document presents a method for analyzing nanoparticles using microscopic images through edge detection with Ant Colony Optimization (ACO). Initially, edges are extracted from images using various detectors and adaptive thresholding. Ants are placed at endpoint pixels and move between pixels probabilistically based on pheromone levels, connecting discontinuities. Results on cerium oxide and zinc oxide nanoparticle images show the ACO-improved edges better distinguish particles and determine sizes/shapes compared to conventional detectors alone.
The document describes an improved particle swarm optimization algorithm to solve vehicle routing problems. It introduces concepts of leptons and hadrons to particles in the algorithm. Leptons interact weakly based on individual and neighborhood best positions, while hadrons (local best particles) undergo strong interactions by colliding with the global best particle. When stagnation occurs, particle decay is used to increase diversity. Simulations show the improved algorithm avoids premature convergence and finds better solutions compared to the basic particle swarm optimization.
Edge detection algorithm based on quantum superposition principle and photons...IJECEIAES
The detection of object edges in images is a crucial step employed in a vast amount of computer vision applications, for which a series of different algorithms has been developed in the last decades. This paper proposes a new edge detection method based on quantum information, which is achieved in two main steps: (i) an image enhancement stage that employs the quantum superposition law and (ii) an edge detection stage based on the probability of photon arrival to the camera sensor. The proposed method has been tested on synthetic and real images devoted to agriculture applications, where Fram & Deutsh criterion has been adopted to evaluate its performance. The results show that the proposed method gives better results in terms of detection quality and computation time compared to classical edge detection algorithms such as Sobel, Kayyali, Canny and a more recent algorithm based on Shannon entropy.
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
This paper proposes a new methodology to
optimize trajectory of the path for multi-robots using
Improved particle swarm optimization Algorithm (IPSO) in
clutter Environment. IPSO technique is incorporated into
the multi-robot system in a dynamic framework, which will
provide robust performance, self-deterministic cooperation,
and coping with an inhospitable environment
an improver particle optmizacion plan de negociosCarlos Iza
This document proposes an improved particle swarm optimization (IPSO) algorithm to optimize trajectory paths for multiple robots in a cluttered environment. IPSO incorporates independent decision making, coordination, and cooperation between robots to accomplish a shared goal. A path planning scheme using IPSO was developed to determine optimal successive robot positions from initial to target locations. Simulation and experiment results show IPSO outperforms particle swarm optimization and differential evolution algorithms with respect to average total trajectory deviation and average uncovered target distance.
This document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO mimics the movement of bacteria in search of food to find optimal paths. ACO is inspired by how ants find food paths using pheromone trails; the algorithm uses heuristic information and state transition rules. PSO is based on the social behaviors of bird flocking and fish schooling; it represents solutions as particles that fly through the problem space following certain rules.
This document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO mimics the movement of bacteria in searching for nutrients to find optimal paths. ACO is inspired by how ants find food by leaving and following pheromone trails. PSO is based on the social behavior of bird flocking and fish schooling, with robots updating their movements based on their own experiences and those of nearby robots. The document reviews the mechanisms and applications of each algorithm for solving multi-robot path planning problems.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHMIAEME Publication
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that is inspired by the intelligent collective behaviour of certain animals, such as flocks of birds or schools of fish. It has undergone numerous improvements since its debut in 1995. As academics became more familiar with the technique, they produced additional versions aimed at different demands, created new applications in a variety of fields, published theoretical analyses of the impacts of various factors, and offered other variants of the algorithm. This paper discusses the PSO's origins and background, as well as its theory analysis. Then, we examine the current state of research and application in algorithm structure, parameter selection, topological structure, discrete and parallel PSO algorithms, multi-objective optimization PSO, and engineering applications. Finally, existing difficulties are discussed, and new study directions are proposed.
Identifying the Slider-Crank Mechanism System by the MPSO Methodijtsrd
This document summarizes a research paper that proposes a modified particle swarm optimization (MPSO) method to identify the parameters of a slider-crank mechanism driven by a field-oriented PM synchronous motor. The MPSO method considers a "distance" term in the fitness function to avoid local optima. Dynamic formulations of the slider-crank mechanism are presented with one independent variable. Experimental results validate that the MPSO identification method can accurately determine the slider-crank mechanism parameters.
This document presents a VLSI architecture design for particle filtering to enable real-time state estimation. The design aims to take advantage of data-level parallelism in the particle filtering algorithm by distributing particles across many processing elements that work in parallel. A key part of the design is allocating hardware resources for computationally-intensive but parallelizable steps globally to be shared across processing elements, reducing the area needed for each element. The document outlines the particle filtering algorithm and an example radio frequency localization application. It then describes the proposed architecture featuring processing clusters with resampling modules and arrays of processing elements, allowing more particles to be processed in parallel through hardware resource sharing.
The New Hybrid COAW Method for Solving Multi-Objective Problemsijfcstjournal
In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems
This document summarizes the bat algorithm, which is a metaheuristic optimization algorithm inspired by the echolocation behavior of microbats. It describes how bats use echolocation to locate prey and obstacles. The basic steps of the bat algorithm are outlined, including how bats emit calls and adjust properties like frequency and loudness. Variants of the bat algorithm are mentioned for solving multi-objective, fuzzy logic, and other problems. Applications discussed include engineering design, scheduling, data clustering, and image processing. Advantages include quick convergence and flexibility, while disadvantages include possible stagnation if parameters are adjusted too rapidly.
The document summarizes two nature-inspired metaheuristic algorithms: the Cuckoo Search algorithm and the Firefly algorithm.
The Cuckoo Search algorithm is based on the brood parasitism of some cuckoo species. It lays its eggs in the nests of other host birds. The algorithm uses Lévy flights for generating new solutions and considers the best solutions for the next generation.
The Firefly algorithm is based on the flashing patterns of fireflies to attract mates. It considers attractiveness that decreases with distance and movement of fireflies towards more attractive ones. The pseudo codes of both algorithms are provided along with some example applications.
Benchmark Calculations of Atomic Data for Modelling ApplicationsAstroAtom
This document summarizes benchmark calculations of atomic data for modeling applications. It discusses numerical methods like close-coupling and distorted-wave approaches for calculating atomic collision data. It provides selected results on energy levels, oscillator strengths, and electron-impact excitation cross sections. It also discusses applications to modeling neon discharges and takes a closer look at ionization calculations and examples. The document concludes by discussing the production and assessment of atomic data and outlines challenges in obtaining reliable data from both experiments and calculations.
A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTIONijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are
compared on twelve constrained nonlinear test functions. Generally, the results show that Differential
Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and
robustness.
Searches for new physics at LHC within the Higgs sector. Step 2: Defining the...Raquel Gomez Ambrosio
We discuss the Effective field theory bottom-up approach, and show some examples of its application for VH production at LHC. We find some interesting results regarding the applicability of the perturbative expansion. Finally we discuss the Pseudo Observable approach as a tool for New Physics searches at LHC.
Long Relaxation Times of a C-shunt Flux Qubit Coupled to a 3D CavityLeonid Abdurakhimov
1) Long relaxation times were observed in a c-shunt flux qubit coupled to a 3D microwave cavity. Relaxation times were longer than previous 2D qubit designs due to the cleaner electromagnetic environment and lower dielectric losses of the 3D cavity.
2) Qubit energy relaxation is due to quasiparticle tunneling or interactions with two-level system defects. Dephasing results from charge noise or critical current fluctuations near the optimal point and 1/f flux noise away from the optimal point.
3) Dynamical decoupling techniques such as CPMG pulse sequences can be used to reach T1-limited coherence by filtering out low-frequency noise sources.
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
Quantum ant colony optimization algorithm based onBloch spherical searchIJRES Journal
In the existing quantum-behaved optimization algorithms, almost all of the individuals are encoded by qubits described on plane unit circle. As qubits contain only a variable parameter, quantum properties have not been fully embodied, which limits the optimization ability rise further. In order to solve this problem, this paper proposes a quantum ant colony optimization algorithm based on Bloch sphere search. In the proposed algorithm, the positions of ants are encoded by qubits described on Bloch sphere. First, the destination to move is determined according to the select probability constructed by the pheromone and heuristic information, then, the rotation axis is established with Pauli matrixes, and the evolution search is realized with the rotation of qubits on Bloch sphere. In order to avoid premature convergence, the mutation is performed with Hadamard gates. Finally, the pheromone and the heuristic information are updated in the new positions of ants. As the optimization process is performed in n-dimensional hypercube space [−1, 1]n, which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. The simulation results show that the proposed algorithm is superior to other quantum-behaved optimization algorithms in both search ability and optimization efficiency.
Quantum ant colony optimization algorithm based onBloch spherical searchirjes
In the existing quantum-behaved optimization algorithms, almost all of the individuals are encoded by qubits described on plane unit circle. As qubits contain only a variable parameter, quantum properties have not been fully embodied, which limits the optimization ability rise further. In order to solve this problem, this paper proposes a quantum ant colony optimization algorithm based on Bloch sphere search. In the proposed algorithm, the positions of ants are encoded by qubits described on Bloch sphere. First, the destination to move is determined according to the select probability constructed by the pheromone and heuristic information, then, the rotation axis is established with Pauli matrixes, and the evolution search is realized with the rotation of qubits on Bloch sphere. In order to avoid premature convergence, the mutation is performed with Hadamard gates. Finally, the pheromone and the heuristic information are updated in the new positions of ants. As the optimization process is performed in n-dimensional hypercube space [−1, 1]n, which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. The simulation results show that the proposed algorithm is superior to other quantum-behaved optimization algorithms in both search ability and optimization efficiency.
Microscopic Image Analysis of Nanoparticles by Edge Detection Using Ant Colon...IOSR Journals
This document presents a method for analyzing nanoparticles using microscopic images through edge detection with Ant Colony Optimization (ACO). Initially, edges are extracted from images using various detectors and adaptive thresholding. Ants are placed at endpoint pixels and move between pixels probabilistically based on pheromone levels, connecting discontinuities. Results on cerium oxide and zinc oxide nanoparticle images show the ACO-improved edges better distinguish particles and determine sizes/shapes compared to conventional detectors alone.
The document describes an improved particle swarm optimization algorithm to solve vehicle routing problems. It introduces concepts of leptons and hadrons to particles in the algorithm. Leptons interact weakly based on individual and neighborhood best positions, while hadrons (local best particles) undergo strong interactions by colliding with the global best particle. When stagnation occurs, particle decay is used to increase diversity. Simulations show the improved algorithm avoids premature convergence and finds better solutions compared to the basic particle swarm optimization.
Edge detection algorithm based on quantum superposition principle and photons...IJECEIAES
The detection of object edges in images is a crucial step employed in a vast amount of computer vision applications, for which a series of different algorithms has been developed in the last decades. This paper proposes a new edge detection method based on quantum information, which is achieved in two main steps: (i) an image enhancement stage that employs the quantum superposition law and (ii) an edge detection stage based on the probability of photon arrival to the camera sensor. The proposed method has been tested on synthetic and real images devoted to agriculture applications, where Fram & Deutsh criterion has been adopted to evaluate its performance. The results show that the proposed method gives better results in terms of detection quality and computation time compared to classical edge detection algorithms such as Sobel, Kayyali, Canny and a more recent algorithm based on Shannon entropy.
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
This paper proposes a new methodology to
optimize trajectory of the path for multi-robots using
Improved particle swarm optimization Algorithm (IPSO) in
clutter Environment. IPSO technique is incorporated into
the multi-robot system in a dynamic framework, which will
provide robust performance, self-deterministic cooperation,
and coping with an inhospitable environment
an improver particle optmizacion plan de negociosCarlos Iza
This document proposes an improved particle swarm optimization (IPSO) algorithm to optimize trajectory paths for multiple robots in a cluttered environment. IPSO incorporates independent decision making, coordination, and cooperation between robots to accomplish a shared goal. A path planning scheme using IPSO was developed to determine optimal successive robot positions from initial to target locations. Simulation and experiment results show IPSO outperforms particle swarm optimization and differential evolution algorithms with respect to average total trajectory deviation and average uncovered target distance.
This document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO mimics the movement of bacteria in search of food to find optimal paths. ACO is inspired by how ants find food paths using pheromone trails; the algorithm uses heuristic information and state transition rules. PSO is based on the social behaviors of bird flocking and fish schooling; it represents solutions as particles that fly through the problem space following certain rules.
This document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO mimics the movement of bacteria in searching for nutrients to find optimal paths. ACO is inspired by how ants find food by leaving and following pheromone trails. PSO is based on the social behavior of bird flocking and fish schooling, with robots updating their movements based on their own experiences and those of nearby robots. The document reviews the mechanisms and applications of each algorithm for solving multi-robot path planning problems.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHMIAEME Publication
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that is inspired by the intelligent collective behaviour of certain animals, such as flocks of birds or schools of fish. It has undergone numerous improvements since its debut in 1995. As academics became more familiar with the technique, they produced additional versions aimed at different demands, created new applications in a variety of fields, published theoretical analyses of the impacts of various factors, and offered other variants of the algorithm. This paper discusses the PSO's origins and background, as well as its theory analysis. Then, we examine the current state of research and application in algorithm structure, parameter selection, topological structure, discrete and parallel PSO algorithms, multi-objective optimization PSO, and engineering applications. Finally, existing difficulties are discussed, and new study directions are proposed.
Identifying the Slider-Crank Mechanism System by the MPSO Methodijtsrd
This document summarizes a research paper that proposes a modified particle swarm optimization (MPSO) method to identify the parameters of a slider-crank mechanism driven by a field-oriented PM synchronous motor. The MPSO method considers a "distance" term in the fitness function to avoid local optima. Dynamic formulations of the slider-crank mechanism are presented with one independent variable. Experimental results validate that the MPSO identification method can accurately determine the slider-crank mechanism parameters.
This document presents a VLSI architecture design for particle filtering to enable real-time state estimation. The design aims to take advantage of data-level parallelism in the particle filtering algorithm by distributing particles across many processing elements that work in parallel. A key part of the design is allocating hardware resources for computationally-intensive but parallelizable steps globally to be shared across processing elements, reducing the area needed for each element. The document outlines the particle filtering algorithm and an example radio frequency localization application. It then describes the proposed architecture featuring processing clusters with resampling modules and arrays of processing elements, allowing more particles to be processed in parallel through hardware resource sharing.
The New Hybrid COAW Method for Solving Multi-Objective Problemsijfcstjournal
In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems
THE NEW HYBRID COAW METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the
Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
APPLICATION OF PARTICLE SWARM OPTIMIZATION FOR ENHANCED CYCLIC STEAM STIMULAT...Zac Darcy
Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and Lévy Bare-bone
PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of
these algorithms was compared in terms of convergence behaviour and the final optimization results. It is
found that, in general, all three types of PSO methods are able to improve the objective function. The best
objective function is found by using the Canonical PSO, while the other two methods give similar results.
The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The Lévy
Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization
problem investigated in this study, it is found that the temperature of the injection steam, CO2 composition
in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam
injection rate and the liquid production rate have less impact on the objective function.
Motion planning and controlling algorithm for grasping and manipulating movin...ijscai
Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving
objects, researchers have been using real time captured images to locate objects dynamically. However,
this approach of controlling the grasping process is quite costly, implying a lot of resources and image
processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are
going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp
and handle any moving objects in the 3-D environment in presence or not of obstacles and without using
the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the
Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an
extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation,
using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving
target, while avoiding possible encountered obstacles . We present a simulation of a scenario that starts
with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring
a maximum rate of success in a reasonable time.
Chaotic ANT System Optimization for Path Planning of the Mobile Robotscseij
This paper presents an improved ant system algorithm for path planning of the mobile robot under the complicated environment. To solve the drawback of the traditional ant colony system algorithm (ACS), which usually falls into the local optimum, we propose an improved ant colony system algorithm (IACS) based on chaos. Simulation experiments show that chaotic ant colony algorithm not only enhances the global search capability, but also has more effective than the traditional algorithm.
Similar to Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching (20)
This document describes an automatic safety door lock system for cars that uses infrared sensors and a hydraulic piston to prevent injuries caused by closing car doors. The system uses IR sensors placed along the door and outer panel connected to a microcontroller. When an object is detected between the closing door and outer panel, the sensors transmit a signal to the microcontroller which activates a relay driver to extend the hydraulic piston to stop the door from closing. The system aims to prevent the over 120,000 injuries that occur annually from unexpected car door closings.
Extrusion can be defined as the process of subjecting a material to compression so that it is forced to
flow through an opening of a die and takes the shape of the hole. Multi-hole extrusion is the process of
extruding the products through a die having more than one hole. Multi-hole extrusion increases the production
rate and reduces the cost of production. In this study the ram force has calculated experimentally for single hole
and multi-hole extrusion. The comparison of ram forces between the single hole and multi-hole extrusion
provides the inverse relation between the numbers of holes in a die and ram force. The experimental lengths of
the extruded products through the various holes of multi-hole die are different. It indicates that the flow pattern
is dependent on the material behavior. The micro-hardness test has done for the extruded products of lead
through multi-hole die. It is observed that the hardness of the extruded lead products from the central hole is
found to be more than that of the products extruded from other holes. The study suggests that multi-hole
extrusion can be used for obtaining the extruded products of lead with varying hardness. The micro-structure
study has done for the lead material before and after extrusion. It is observed that the size of grains of lead
material after extrusion is smaller than the original lead.
Analysis of Agile and Multi-Agent Based Process Scheduling Modelirjes
As an answer of long growing frustration of waterfall Software development life cycle concepts,
agile software development concept was evolved in 90’s. The most popular agile methodologies is the Extreme
Programming (XP). Most software companies nowadays aim to produce efficient, flexible and valuable
Software in short time period with minimal costs, and within unstable, changing environments. This complex
problem can be modeled as a multi-agent based system, where agents negotiate resources. Agents can be used to
represent projects and resources. Crucial for the multi-agent based system in project scheduling model, is the
availability of an effective algorithm for prioritizing and scheduling of task. To evaluate the models, simulations
were carried out with real life and several generated data sets. The developed model (Multi-agent based System)
provides an optimized and flexible agile process scheduling and reduces overheads in the software process as it
responds quickly to changing requirements without excessive work in project scheduling.
Effects of Cutting Tool Parameters on Surface Roughnessirjes
This paper presents of the influence on surface roughness of Co28Cr6Mo medical alloy machined
on a CNC lathe based on cutting parameters (rotational speed, feed rate, depth of cut and nose radius).The
influences of cutting parameters have been presented in graphical form for understanding. To achieve the
minimum surface roughness, the optimum values obtained for rpm, feed rate, depth of cut and nose radius were
respectively, 318 rpm, 0,1 mm/rev, 0,7 mm and 0,8 mm. Maximum surface roughness has been revealed the
values obtained for rpm, feed rate, depth of cut and nose radius were respectively, 318 rpm, 0,25 mm/rev, 0,9
mm and 0,4 mm.
Possible limits of accuracy in measurement of fundamental physical constantsirjes
The measurement uncertainties of Fundamental Physical Constants should take into account all
possible and most influencing factors. One from them is the finiteness of the model that causes the existence of
a-priori error. The proposed formula for calculation of this error provides a comparison of its value with the
actual experimental measurement error that cannot be done an arbitrarily small. According to the suggested
approach, the error of the researched Fundamental Physical Constant, measured in conventional field studies,
will always be higher than the error caused by the finite number of dimensional recorded variables of physicalmathematical
models. Examples of practical application of the considered concept for measurement of fine
structure constant, speed of light and Newtonian constant of gravitation are discussed.
Performance Comparison of Energy Detection Based Spectrum Sensing for Cogniti...irjes
With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing
demand for wireless radio spectrum. However, the policy of fixed spectrum assignment produces a bottleneck for more
efficient spectrum utilization, such that a great portion of the licensed spectrum is severely under-utilized. So the concept of
cognitive radio was introduced to address this issue.The inefficient usage of the limited spectrum necessitates the
development of dynamic spectrum access techniques, where users who have no spectrum licenses, also known as secondary
users, are allowed to use the temporarily unused licensed spectrum. For this purpose we have to know the presence or
absence of primary users for spectrum usage. So spectrums sensing is one of the major requirements of cognitive radio.Many
spectrum sensing techniques have been developed to sense the presence or absence of a licensed user. This paper evaluates
the performance of the energy detection based spectrum sensing technique in noisy and fading environments.The
performance of the energy detection technique will be evaluated by use of Receiver Operating Characteristics (ROC) curves
over additive white Gaussian noise (AWGN) and fading channels.
Comparative Study of Pre-Engineered and Conventional Steel Frames for Differe...irjes
In this paper, the conventional steel frames having triangular Pratt truss as a roofing system of 60 m
length, span 30m and varying bay spacing 4m, 5m and 6m respectively having eaves level for all the portals is at
10m and the EOT crane is supported at the height of 8m from ground level and pre-engineered steel frames of
same dimensions are analyzed and designed for wind zones (wind zone 2, wind zone 3, wind zone 4 and wind
zone 5) by using STAAD Pro V8i. The study deals with the comparative study of both conventional and preengineered
with respect to the amount of structural steel required, reduction in dead load of the structure.
Flip bifurcation and chaos control in discrete-time Prey-predator model irjes
The dynamics of discrete-time prey-predator model are investigated. The result indicates that the
model undergo a flip bifurcation which found by using center manifold theorem and bifurcation theory.
Numerical simulation not only illustrate our results, but also exhibit the complex dynamic behavior, such as the
periodic doubling in period-2, -4 -8, quasi- periodic orbits and chaotic set. Finally, the feedback control method
is used to stabilize chaotic orbits at an unstable interior point.
Energy Awareness and the Role of “Critical Mass” In Smart Citiesirjes
This document proposes a novel analytical model to define a new concept of critical mass in the context of spreading energy awareness in smart cities. The model incorporates centrality measures in both single-layer and multilayer social networks. Simulation results show that including centrality measures and a multilayer approach lowers the critical mass needed to trigger and spread good consumer habits. Specifically, the model calculates critical mass values using eigenvector centrality in single layers and a heterogeneous eigenvector-like centrality in multilayers. Considering network structure and central nodes' influence allows a smaller critical mass to foster diffusion compared to models that do not account for centrality. Extending the analysis to multilayers further reduces critical mass by increasing tie strength between nodes.
A Firefly Algorithm for Optimizing Spur Gear Parameters Under Non-Lubricated ...irjes
Firefly algorithm is one of the emerging evolutionary approaches for complex and non-linear
optimization problems. It is inspired by natural firefly‟s behavior such as movement of fireflies based on
brightness and by overcoming the constraints such as light absorption, obstacles, distance, etc. In this research,
firefly‟s movement had been simulated computationally to identify the best parameters for spur gear pair by
considering the design and manufacturing constraints. The proposed algorithm was tested with the traditional
design parameters and found the results are at par in less computational time by satisfying the constraints.
The Effect of Orientation of Vortex Generators on Aerodynamic Drag Reduction ...irjes
One of the main reasons for the aerodynamic drag in automotive vehicles is the flow separation
near the vehicle’s rear end. To delay this flow separation, vortex generators are used in recent vehicles. The
vortex generators are commonly used in aircrafts to prevent flow separation. Even though vortex generators
themselves create drag, but they also reduce drag by delaying flow separation at downstream. The overall effect
of vortex generators is more beneficial and proved by experimentation. The effect depends on the shape,size and
orientation of vortex generators. Hence optimized shape with proper orientation is essential for getting better
results.This paper presents the effect of vortex generators at different orientation to the flow field and the
mechanism by which these effects takes place.
An Assessment of The Relationship Between The Availability of Financial Resou...irjes
The availability of financial resources is an important element in impacting the success of a planning
process for an effective physical planning. The extent to which however, they are articulated in the process
remained elusive both in scholarly and public discourse. The objective of this study wastherefore, to examine
the extent to which financial resources affect physical planning. In doing so, the study examinedwhether
financial resources were adequate or not to facilitate planning processes in Paidha. According to the study
findings,budget prioritization and ceilings are still a challenge in Paidha Town Council. This is partly due
limited level of knowledge of physical planning among the officials of Paidha Town Council. As a result, there
were no dedicated budget line for routine inspection of physical development plan compliance and enforcement
tools in Paidha. In conclusion, in addressing uncoordinated patterns of physical development that characterize
Uganda‟s urban centres, a critical starting point ought to be the analysis of physical planning process. The
research of this kind is not only significant to other emerging urban centres facing poor a road network,
mushrooming informal settlements and poor social services including poor pattern of residential and commercial
developments but also to all institutions that are involved in planning these towns. Knowing the extent of need
for financial influences in planning may assist local authorities to take the processes of planning seriously which
will help enhance the sustainable development of emerging urban centres including Paidha.
The Choice of Antenatal Care and Delivery Place in Surabaya (Based on Prefere...irjes
This study analyzed factors affecting the utilization of antenatal care and delivery places in Surabaya, Indonesia based on preferences and choice theory. The study found that:
1) Nearly half of women chose healthcare for delivery based on information from others
2) Most women's main criteria for choosing a delivery place was that it was safe, comfortable and cheap
3) The majority of women's primary choice for a delivery place was one that was close, comfortable and cheap
Prediction of the daily global solar irradiance received on a horizontal surf...irjes
This document presents a new approach for predicting the daily global solar irradiance received on a horizontal surface as a function of local daytime and the maximum daily value. An exponential distribution function is suggested and compared to experimental data from several locations. The maximum daily value (qmax) is estimated theoretically in terms of the solar constant adjusted for earth-sun distance variation. Computed values using the new approach show good agreement with experimental data, within 16% error except for some extreme points.
HARMONIC ANALYSIS ASSOCIATED WITH A GENERALIZED BESSEL-STRUVE OPERATOR ON THE...irjes
This document summarizes a research paper that considers a generalized Bessel-Struve operator on the real line. It defines generalized Bessel-Struve and Weyl integral transforms, which are shown to be transmutation operators relating the generalized Bessel-Struve operator to derivatives. These tools are then used to develop a new harmonic analysis associated with the generalized Bessel-Struve operator, including generalized Sonine integral transforms. Key results proven include Paley-Wiener theorems and properties of the various integral transforms.
The Role of Community Participation in Planning Processes of Emerging Urban C...irjes
This document summarizes a research study examining the level of community participation in the physical planning process in Paidha Town, Uganda. The study found that community participation in planning is very low, limited mostly to a few mass meetings. Few community members are actually involved in planning. Even those involved do not understand their rights and roles. Physical planning has not been adequately prioritized or funded. To improve participation, the study recommends involving communities at different administrative levels from villages to the town council, and using a wider range of participatory methods beyond just meetings. Overall, the study finds that community participation in planning is still limited and needs to be strengthened for more sustainable urban development.
Understanding the Concept of Strategic Intentirjes
This document summarizes the concept of strategic intent in strategic management. It begins by discussing the origins and evolution of strategic management from Greek history to modern theories. It then defines strategic intent, comparing various authors' definitions. A key model by Hamel and Prahalad links strategic intent to other strategic components like foresight and core competencies. Strategic intent inspires long-term thinking beyond strategic planning alone. It provides direction and commitment to help organizations shape competitive priorities and capabilities for the future. Chief executives play a critical role in developing strategic intent to guide organizational progress over 10-20 years.
The (R, Q) Control of A Mixture Inventory Model with Backorders and Lost Sale...irjes
This document summarizes an inventory model that considers a mixture of backorders and lost sales when stockouts occur. The model has the following key features:
1) The set-up cost and lead time are controllable variables that can be optimized.
2) Instead of minimizing stockout costs, the model employs a service level constraint to bound the stockout level per cycle.
3) The model is solved using a genetic algorithm approach to find optimal values for ordering quantity, set-up cost, and lead time that minimize total costs while satisfying the service level constraint.
Relation Between Stress And Menstrual Cycle At 18-21 Years Of Ageirjes
The document summarizes a study that examined the relationship between stress and menstrual cycles in Indonesian nursing students ages 18-21. It found:
1) Nearly half (45.5%) of the 132 students studied experienced irregular menstrual cycles.
2) Slightly over half (50.8%) of students reported experiencing stress.
3) There was a significant relationship between stress and irregular menstrual cycles, with stressed students over 4 times more likely to have irregular cycles.
The study concluded that most of the nursing students experienced irregular menstrual cycles and stress, and that stress was strongly correlated with irregular menstrual periods.
Wave Transmission on Submerged Breakwater with Interlocking D-Block Armorirjes
1. The document summarizes a study on wave transmission through a submerged breakwater with interlocking D-block armor. Laboratory experiments were conducted to determine how water depth, wave period, structure height, and wave steepness influence the transmission coefficient.
2. Regression analysis showed that transmission coefficient (Kt) is most influenced by the ratio of wave length to top width, ratio of structure height to water depth, and ratio of wave height to wave period squared. A formula for Kt in terms of these parameters was developed.
3. Comparisons with previous studies show the same trend that higher wave steepness results in lower transmission coefficient, validating the results. The study provides useful insights into breakwater design using inter
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfTechgropse Pvt.Ltd.
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Climate Impact of Software Testing at Nordic Testing Days
Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
1. International Refereed Journal of Engineering and Science (IRJES)
ISSN (Online) 2319-183X, (Print) 2319-1821
Volume 4, Issue 4 (April 2015), PP.06-18
www.irjes.com 6 | Page
Quantum-inspired artificial fish swarm algorithm based on the
Bloch sphere searching
1
Jianping Li, 2
Siyuan Zhao, 3
Yunxia Xu
1
School of Computer and Information Technology, Northeast Petroleum University,
Daqing, Heilongjiang, China
2
School of Computer & Information Technology Northeast Petroleum University
Daqing, Heilongjiang, China
3
CNPC Materials Company, Beijing 100029, China
Abstract:- To enhance the performance of the intelligent optimization algorithm, a new model of performing
search on the Bloch sphere is proposed. Then, by integrating the model into the artificial fish swarm
optimization, we present a quantum-inspired artificial fish swarm optimization algorithm. In proposed method,
the fishes are encoded with the qubits described on the Bloch sphere. The vector product theory is adopted to
establish the axis of rotation, and the Pauli matrices are used to construct the rotation matrices. The four fish
behaviors, such as moving, tracking, capturing, aggregating, are achieved by rotating the current qubit about the
rotation axis to the target qubit on the Bloch sphere. The Bloch coordinates of qubit can be obtained by
measuring with the Pauli matrices, and the optimization solutions can be presented through the solution space
transformation. The highlight advantages of this method are the ability to simultaneously adjust two parameters
of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate
the optimization process. The experimental results show that the proposed method obviously outperforms the
classical one in convergence speed and achieves better levels for some benchmark functions.
Keywords:- quantum computing; fish swarm optimizing; Bloch sphere searching; algorithm designin
I. INTRODUCTION
In 2002,Dr. Li Xiaolei proposed about Artificial Fish Swarm,AFS[1] for the first time,The algorithm
simulates such several typical behaviors as aggregation, tracking, capture about feeding fish.Artificial Fish
Swarm is a typical optimization algorithm,it has the characteristics of simplicity and parallel.After the
algorithm proposed, caused wide attention of scholars at home and abroad,In this aspect of the improved
algorithm,has put forward the fuse with cultural algorithm early or late ,AFA[2], based on the
chaos search ,AFA[3-5]
,using Taboo Strategy,AFA[6],based on K-means clustering ,AFA[7,8]
and other new
improved model.In the aspect of application, AFA has been successfully used in Image compression[9]
, Task
scheduling[10]
,Vehicle routing optimization[11
],Intrusion detection[12]
, Image segmentation[13]
and so on.
Quantum computing is a new computing model,with its unique polymorphisms, superposition, parallelism has
attracted extensive attention of scholars at home and abroad.In the real quantum system , quantum bit is based
on Bloch spherical description, and contains two adjustable parameters.However, In the current quantum
intelligent optimization algorithm, individual coding using Quantum bit encoding described by unit circle, with
only one adjustable parameter.And the evolutionary mechanism utilize the quantum rotation gates and quantum
not-gate, In essence,Quantum bits are revolving around the center of rotation on the unit circle ,and only
change a parameter of quantum bits at the same time, therefore, Quantum properties has not been fully
reflected.Although the literature[14] put forward a kind of Quantum genetic algorithm based on Quantum
bit coordinate coding, and in the algorithm of quantum bit based on Bloch spherical description,also
the evolutionary operator also can adjust the two parameters of quantum bits at the same time, however, the
algorithm does not take into account two parameters adjustment quantity matching problem, which current bits
to the target bit approximation process is not along the shortest path, so, optimize performance is affected.This
paper proposes a new individual coding method and evolutionary.It is different from the literature[14], This
paper directly uses the Quantum bit coding based on Bloch spherical description(Not the coordinates of
quantum bits), using Quantum bit in Bloch sphere pivoting method to realize individual evolution quantity.With
the artificial fish swarm optimization as the breakthrough point, describes that the design and implement the
scheme of algorithm in detail.Using the standard-function-extremal optimization as an example,to
how that the method is very promising by contrast.
2. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 7 | Page
2 Basic operation of AFS
Fish do not have complex power of reasoning, However, through some simple individual behavior can make the
group highlights the strong intelligent .Artificial fish swarm algorithm including
foraging, cluster, collision, random such four basic operations,Its basic principle is as follows.
2.1 Foraging behavior
This behavior is a kind of basic behavior of artificial fish, The meaning is artificial fish in the current
position within the range of perception select a new location randomly. If the i -th artificial fish current
position is iX
,so the foraging behavior in the neighborhood of iX
(or sensing range) select location
jX
randomly can be described as
)1,0(Visual randij XX
(1)
In the formula Visualis the scope of vision, )1,0(rand is )1,0( within the random number.
As the minimum optimization as an example, make )(Xf as individual X ’s objective function value,
if
)()( ji ff XX
,According to the following formula, take a step in this direction
)1,0(Step
||||
1
randt
ij
t
ijt
i
t
i
XX
XX
XX
(2)
In the formula, Step is forward step.In the opposite,re-select location jX
randomly,to determine whether
satisfied the forward conditions ,After Try_number’s attempts ,If it still does not satisfied
forward conditions, then execute the random behavior.
2.2 Cluster behavior
If the artificial fish current position is iX
, the number of partners in the Visual neighborhood is fn
,and
Visual neighborhood heart is cX
.if
)()( ifc fnf XX
, as it has shown that the neighborhood center
has many food and not crowded, in this time according to the following formula ,go for a step forward cX ,
Otherwise execute foraging behavior.
)1,0(Step
||||
1
randt
ic
t
ict
i
t
i
XX
XX
XX
(3)
In the formula is crowding factor.
2.3 The following behavior
Suppose the artificial fish current position is iX
,in its Visual neighborhood,the minimum of fn
partners in
the target value is jX
.if
)()( ifj fnf XX
, according to the following formula go for a step
forward jX
, Otherwise execute Foraging behavior.
)1,0(Step
||||
1
randt
ij
t
ijt
i
t
i
XX
XX
XX
(4)
In the formula is crowding factor.
2.4 Random behavior
The description of the Rondom behavior is relatively simple,just to pick a random state in the field, and
then according to the following formula go for a step forward, it is a default behavior of Foraging behavior.
)1,0(Visual1
randt
i
t
i
XX
(5)
3 Quantum-inspired artificial fish swarm algorithm
This paper presents a new Quantum-inspired search mode, And integrate with the artificial fish swarm
algorithm, which we call the Quantum-inspired artificial fish swarm algorith, called QIAFS.
3.1Bloch spherical quantum bit description
3. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 8 | Page
In quantum computing,a quantum bit is in a two-level quantum system which can be described as
two-dimensional complex in Hilbert space . Any state of quantum bits can be written as
1|
2
sin0|
2
cos| i
eφ
(6)
In the formula , 0 , 20 .
So,quantum bit belongs to description of the continuous variable and in the vector space, And a
quantum bit can be described as many different state.Quantum bit can be described by a little Bloch sphere
embedded in three-dimensional Descartes coordinate system. As shown in Fig1 .In the
Fig1 sincosx , sinsinx , cosz .Such like this, Quantum state φ| can be written as
T
)1(2
i
,
2
1
|
z
yxz
φ
(7)
So, any point on the Bloch spherical surface ),,( zyxP corresponds to a quantum bit φ| one by one.
Fig.1 A quantum bit description on the Bloch sphere
3.2 The rotation of a quantum bit on the Bloch sphere
In this paper, we will build a search term in Bloch sphere,Even if quantum bits rotating around a
fixed axis toward the target bit on the Bloch sphere. We can change two parameters and of quantum bit by
rotation at the same time.And it also can achieve best matching of two adjustment automatically. So that we can
simulate quantum behavior better and improve the optimization ability obviously.the key point of rotation is the
design of rotating axis, The design method we presented can be stated as the following theorem.
Theorem1 Remember points P and Q are respectively corresponding vector P = [px, py, pz] and Q =
[qx, qy, qz] on the Bloch sphere , and the axis where quantum bit turn from point P to Q on Bloch sphere is the
vector product of P and Q, Raxis=P×Q,as shown in Fig2 .
Using the definition of vector product definition, the theorem is easy to prove.
4. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 9 | Page
Fig.2 The rotation axis of a quantum bit on the Bloch sphere
Make
)(| tijφ
and
)(| tbjφ
are respectively
corresponding vector
],,[ ijzijyijxij pppP
and ],,[ bjzbjybjxbj pppP
on the Bloch sphere,According to the
above theorem,the axis of
)(| tijφ
turn toward
)(| tbjφ
can be described as
|||| bjij
bjij
ij
PP
PP
R
(8)
According to the principles of quantum computing, on the Bloch sphere,the spin matrix that around the
axis of a vector quantity
],,[ zyx nnnn
and arc can be described as
)(
2
isin
2
cos)( σnIRn
(9)
In the formula , I is unit matrix,
],,[ zyx σσσσ
.so, the current quantum bit
)(| tijφ
is on the Bloch
sphere now, the spin matrix of the around axis ijR
turn to target bit
)(| tbjφ
is
)(
2
sini
2
cos)( σRM ij
ijij
ij I
(10)
The operating of
)(| tijφ
turn to
)(| tbjφ
is
)(|)()(| tt ijijij φMφ
(11)
In the formula t is iteration steps.
3.3 QIAFS Coding scheme
In the QIAFS, individual uses quantum bit coding based on the Bloch spherical description.Set the
population size is N,the space dimension is D,remember the t-th generation of the population is
T
21 )](,),(,)([)( tttt NpppP
, the i -th individuals can be encoding (initialization) as
])0(|,,)0(|,)0([|)0( 21 iDiii φφφp
(12)
5. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 10 | Page
In the formula,
1|)
2
sin(0|)
2
cos()0(|
i ijij
ij
ij
e
φ
,
)1,0(randij
,
2)1,0(randij
.
3.4 The projection measurement of quantum bits
According to the principles of quantum computing, the Bloch coordinate of a quantum bit φ| can
use the calculation of the Pauli matrices basis vectors that obtained by projection measure, the definition of
Pauli matrices is
10
01
,
0i
i0
,
01
10
zyx σσσ (13)
For the j -th quantum bit
ijφ|
of i -th individuals, its coordinates of projection measurement formula is,
ijzijij
ijyijij
ijxijij
z
y
x
φσφ
φσφ
φσφ
||
||
||
(14)
In the formula, DjNi ,,2,1,,,2,1 .
In the QIAFS, The three coordinates of quantum bit can be seen as three parallel gene, each individual
contains three parallel gene chains, each gene represents a chain optimization solution. Thus, each individual
represent as three optimization solution on of the search space.
3.5 The transformation in Solution space
In the QIAFS algorithm, each individual group contains three Bloch coordinates, each axis represents an
optimal solution. Because Bloch coordinates in the interval ]1,1[ , so we need to solute the space
transformation.Remember optimize issues for the j -th dimension range of variables is
]Max,[Min jj
, thus
the solution space transformation can be described as the three following type.
2/)]1(Max)1(Min[
2/)]1(Max)1(Min[
2/)]1(Max)1(Min[
ijjijjij
ijjijjij
ijjijjij
zzZ
yyY
xxX
(15)
In the formula, DjNi ,,2,1,,,2,1 .
3.6 The Optimal update
The three groups of individuals corresponding solution
),,( ijijij XXX
are substituted into the objective
function to calculate the individual target.The minimum target so far obtained is bestgf
, bestgp
is
corresponding individual.so
))(),(),(min()( iiii ZfYfXff p
,
))((min
1
i
Ni
best ff p
,if bestbest fgf
,thus bestbest fgf
, bestbest pgp
.
3.7 Four kinds of search behavior of
QIAFS
1) Foraging behavior
6. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 11 | Page
2) For the i -th artificial fish
t
ip
, use the global optimum for the target individual quantum bit establish the
rotation axis, with rotate angle
)1,0(0 rand (where 0 is initial value for the angle) around the axis of
rotation on
t
ip
,and after rotation denoted by
t
ipˆ
.
If
)ˆ()( t
i
t
i ff pp
, then stop; Otherwise, repeat the rotation process.After Try_number times try, if it still not
satisfied the formula
)ˆ()( t
i
t
i ff pp
, Then execute Random behavior.
2) Cluster behavior
For the i -th artificial fish
t
ip
,suppose the number of partners located in the neighborhood is fn
,and
the fish in the neighborhood centers seem as the virtual artificial fish cpˆ
.if
)()ˆ( t
ifc fnf pp
( is
crowding factor), it represent that neighborhood center with more food and less crowded,at this time use each
quantum bit of point cpˆ
as the target to establish rotation axis,and use the
)1,0(0 rand as the rotation
angle,therefore execute Foraging behavior.
3) Collision behavior
For the i -th artificial fish
t
ip
,suppose its min target within fn
partners in Visual neighborhood is
t
jp
.Use the each quantum bit establish the rotation axis, with rotate angle
)1,0(0 rand (where 0 is
initial value for the angle) around the axis of rotation on
t
ip
,therefore execute Foraging behavior.
4) Random behavior
For the i -th artificial fish
t
ip
,choose a partner randomly in its Visual neighborhood ,Then establish the
rotation axis based on each quantum bit, use the
)1,0(0 rand as the rotating angle to implement rotate
around the axis on
t
ip
.in the QIAFS,it also is the default of Foraging behavior.
3.8 QIAFS Embodiments
(1) Initialization,include: Population sizeN, Initial individual Nppp ,,, 21
, Initial angle 0 , Field of view
Visual, Retries times Try number, Crowding factor .
(2) Calculate the individual value of the objective function, save the global best individual.
(3) Individuals in the foraging, clusters, collision, random four behaviors, by comparison, and then select a best
practices to implemen, updating individual location information.
(4) If the termination condition is satisfied, then output the reason, otherwise turn(2).
4 The convergence of QIAFS
Suppose },,,{ 21
t
N
tt
t pppP is the t-th artificial and population of QIAFS,wherein the definition for the
i -th individual is
t
ip ,
)2/sin(
)2/cos(
)2/sin(
)2/cos(
i
1
i
1
1 t
iD
t
iD
t
i
t
it
i t
iD
t
i
ee
p (16)
About the convergence of QIAFS , we have the following conclusions.
7. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 12 | Page
Theorem 2 QIAFS is a convergent with probability 1.
Prove Set digits of and respectively is 1 and 2 , the state space of population is
ND
)( 21 ,as
known by population update strategy, 1tP is relevant with tP only, so iterative sequence }1,{ ttP is finite
homogeneous Markov chains.
remerber )}(min)(|{
1
t
i
Ni
ttt
ff pppbp
is the best individual of t -th population tP ,
*})(min|{*
211
ffs k
k
k
ND
bpbp
)(
is a global optimal solution set, *f is the minimum objective
function value for the overall situation.
Suppose *}|{ stT t
bp ,
k
tP represent the k -th stats of artificial fish after t times iterations in the
state space
ND
k
)(,,2,1 21 .
The following calculate the step transition transition probability )()( 1
j
t
i
tt PjiP PP of
stochastic process }1,{ ttP .
Because QIAFS use the elitist strategy, so )()( 1
i
t
j
t ff PP .When
TjTi , , 0)( jiPt ,when TjTi , , 0)( jiPt .
Suppose )(iPt is probability of in the state tP , remember
Ti
tt iPP )( , By the properties of
Markov chain,the probability 1tP of state Tj is
Ti Tj
tt
Ti Tj
ttt jiPiPjiPiPP )()()()(1
Owing to
Ti Tj
tt
Ti Tj
ttt jiPiPjiPiPP )()()()(
So
t
Ti Tj
tttt PjiPiPPP
)()(1
So 0lim
t
t
P
1lim1)(lim1
*))((lim
t
t
Ti
t
t
t
t
PiP
fbpfP
Scilicet QIAFS is Convergent with probability 1.
5The experimental results and analysis
In this section, take the optimization of function extreme value for example, and to verify the superiority of
QIAFS by the comparison among the common artificial fish algorithm, the Bloch quantum genetic algorithm in
literature 14 and the elite genetic algorithm with reserve strategy.
5.1 Test Function
(1)
D
i ixf 1
2
1 )5.0()(X ;
D
]100,100[ ; 0*
ix ; 0*)( Xf
(2)
D
i ii xx
D
f 12 )||sin(
1
418.98)(X
D
]500,500[ ; 420.97*
ix ; 0*)( Xf
(3) )
1
2.0exp(20)( 1
2
3
D
i ix
D
f X
ex
D
D
i i
20))2cos(
1
exp( 1
D
]32,32[ ; 0*
ix ; 0*)( Xf
8. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 13 | Page
(4)
D
i iii xxx
D
f 1
24
4 )516(
1
33.78)(X
D
]100,100[ ; 2.9*
ix ; 0*)( Xf
(5)
D
i i
D
i Dii
xu
yyy
y
D
f
1
1
1
2
1
22
1
2
5
)4,100,10,(
})1()](sin101[)1(
)(sin10{)(
X
)1(
4
1
1 ii xy ;
axaxk
axa
axaxk
mkaxu
i
m
i
i
i
m
i
i
,)(
,0
,)(
),,,( ;
D
]50,50[ ; 1*
ix ; 0*)( Xf
(6) )3({sin
10
1
)( 1
2
6 xf X
D
i i
DD
D
i ii
xu
xx
xx
1
22
1
1 1
22
)4,100,5,(
)]}2(sin1[)1(
)]3(sin1[)1(
axaxk
axa
axaxk
mkaxu
i
m
i
i
i
m
i
i
,)(
,0
,)(
),,,( ;
D
]50,50[ ; 1*
ix ;
(7)
1
1
1
2
1
2
7
)3.0)4cos()3cos(3.0
2(
)(
D
i
ii
ii
xx
xx
f
X
D
]100,100[ ; 0*
ix ; 0*)( Xf
(8)
D
k
D
j kj
jk
y
y
f 1 1 ,
2
8 )1)cos(
4000
()(X
222
)1()(100 jjkjk xxxy
D
]100,100[ ; 1*
ix ; 0*)( Xf
(9)
4
1
3
1
2
9 ))(exp(3.86)( i j ijjiji bxacf X
]1,0[ix ; ]853.0,556.0,115.0[* X
0*)( Xf
35100.1
30103.0
35100.1
30103.0
A ; ]3.23.01.21.0[C
9. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 14 | Page
0.88280.57430.03815
0.55470.87320.1091
0.74700.43870.4699
0.26730.11700.3689
B .
(10) 2
21310 )),(10[(100)( xxxf X
2
3
22
2
2
1 ])1( xxx
0,5.0tan
2
1
0,tan
2
1
),(
1
1
21
1
1
21
21
x
x
x
x
x
x
xx
]10,10[ix ; ]0,0,1[*X ; 0*)( Xf
5.2 (Parameter settings)
The ten functions above are all minimum optimization. The dimensions of the former eight
high-dimensional function are D = 30, and the latter two low-dimensional function are 3.For comparison; we set
the precision threshold for every function. As shown in table 1, when the optimization result is less than the
corresponding threshold, the algorithm is convergent. If the algorithm is convergent, the optimization steps of
the algorithm called the iteration steps; otherwise, the iteration steps are equal to the limited steps. Try number
setting is 10; visual setting is 0.01(Max-Min). The Max and Min are respectively the test function of the
independent variable of the maximum and the minimum. The congestion factor 10 .For QIAFS, the initial
value of the corner
05.00
; For AFS, the forward step is equal to 0.05(Max-Min); For BQGA, the corner
step 0 is equal to 0.05π, the mutation probability is
3
10
; For EGA, the crossover probability is 0.8; the
mutation probability is 0.05. The population sizes for the four algorithm are 100, so is the limited steps.
Tab.1 The threshold value of 10 benchmark functions
function f1 f2 f3 f4 f5 f6 f7 f8 f9 f10
Threshol
d
1.0 200 1.0 10 0.1 10−3
2.0 700
10−
9
10−
5
5.3 Results contrast
To enhance the objectivity of the comparison results, the statistics of the results contrast was made after 50
times isolated running of every algorithm. The average time per iteration comparison is shown as Table 2. The
convergence time (statistical difference convergence solution to X,Y solution, Z number of solution for both
QIAFS and BQGA), the average number of steps, the average results comparison is shown as Table 3.
Tab.2 The average running time contrasts for each iteration (Unit: Sec)
functio
n
QIAFS AFS BQGA EGA
f1
15.4589 2.108
3
0.0229 0.0009
f2
18.2018 1.919
2
0.0242 0.0012
f3
19.2819 2.162
7
0.0251 0.0014
11. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 16 | Page
f8
QIAFS 47 16 30 1 88.70 660.05
AFS 13 ― ― ― 95.74 738.39
BQGA 0 0 0 0 100 1.8e+1
3
EGA 0 ― ― ― 100 1.0e+1
6
f9
QIAFS 49 14 20 15 58.80 1.3e-1
0
AFS 10 ― ― ― 90.00 4.9e-0
9
BQGA 0 0 0 0 100 1.8e-0
3
EGA 0 ― ― ― 100 3.3e-0
2
f10
QIAFS 50 17 30 3 33.42 5.2e-0
8
AFS 28 ― ― ― 77.10 1.7e-0
5
BQGA 0 0 0 0 100 0.7259
EGA 0 ― ― ― 100 1.8623
Table 2 implied that on the running time, the four algorithms from long to short sort of QIAFS, AFS,
BQGA, EGA. The analysis of the result as follows. In QIAFS and AFS, each iteration of each artificial fish
have to perform four acts, but also perform foraging behavior Try number at most times, and then choose the
best kind of behavior as the current search results. Therefore, the behavior of a complex algorithm to calculate
the amount of fish is too large, leading to QIAFS and AFS running time significantly longer than BQGA and
EGA. Whereas, QIAFS has adopted the three chains quantum bit encoding mechanism, each artificial fish
simultaneously searching three new locations while iterating, and each search behavior must involve
measurement and projection bit rotation matrix structure and so on, which significantly increased the amount of
computation, leading to its running time is significantly longer than AFS. For BQGA, although which also
adopted the three-chain quantum bit encoding mechanism, it does not involve measuring the quantum bit
rotation matrix structure or operations, and the evolution process (only rotation and variation) is relatively
simply. Therefore, the running time is less than the QIAFS and AFS, while it’s longer than EGA because that
every iteration needs to update three gene chains.
Table 3 shows that in terms of optimizing ability, QIAFS obviously better than that of AFS, and
QIAFS and AFS are apparently better than BQGA and EGA. And four kinds of algorithm optimization ability
of the order from high to low are QIFAS, AFS, BQGA, and EGA. This comparison results can by analyzed as
follows. First, Quantum bit the introduction of the three chains encoding mechanism effectively improved the
algorithm for the solution space ergodicity. According to the geometric features of the Bloch sphere, this kind of
coding mechanism can increase the probability of access to the global optimal solution by expanse the number
of the global optimal solution. This is the direct reason why the two kinds of quantum derivative algorithm are
superior to the corresponding classical algorithm. Second, QIAFS and AFS effectively simulate foraging
behavior of fish, is the root cause of its high-performance optimization capabilities. Four kinds of behavior each
may find a good solution. Choose the best behavior as the search direction among four kinds of search direction,
which reflected the diversity of search direction and the complementary advantages among four behaviors. This
approach significantly increases the ability of searching optimization, which leads the two fish algorithms are
12. Quantum-inspired artificial fish swarm algorithm based on the Bloch sphere searching
www.irjes.com 17 | Page
superior to the two genetic algorithms. Third, besides QIAFS adopted the fish search mechanism, it also used a
new method that is quantum bit pivoting. This is the reason why the optimization ability of QIAFS is apparently
better than that of BQGA. Even though two algorithms both adopted the three chain quantum bit encoding, the
algorithm adjustment quantum bit uses different methods. BQGA using direct method, which directly adjusted
two parameters of the quantum bit. And also used the same adjustment amount ( ). This method
clearly cannot approach the target bit along the shortest path. Since if you want to approach the target bit along
the shortest path, and must meet a certain matching relationship which is very difficult to express it
clearly by a certain analytic formula. While in QIAFS, the adjustment of individual quantum bits adopted the
indirect method. That is to make the quantum bits directly along the Bloch sphere axis of rotation on the way to
the target bit of the great circle (at this time the shortest path exists). Although this method did not adjust the
two parameters of the quantum bit directly, it automatically achieved the precise match of and . So
this method has higher ability of optimization.
Comprehensive two aspects of funning time and optimization ability, QIAFS precisely at the expense
of the running time in exchange for improved optimization capabilities. This is consistent with no free lunch
theorem. Besides, though the running time of QIAFS is longer, it still has a broad application prospect to a large
number of offline optimization problems that not concerned about the running time.
VI. CONCLUSIONS
In this paper we propose a Quantum-inspired artificial fish swarm algorithm,using quantum bit inplement
Individual coding based on Bloch spherical description,using quantum bit rotating around the axis based on
Bloch sphere to implement four search behavior AFSA .The experimental results reveal the quantum bit coding
function based on Bloch sphere, and individual evolutionary roteting around the axis which make the adjustment
of quantum bit two parameters with best matches . it can effectively improve the ability of intelligent
optimization algorithms effectively.
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