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
Performance optimization of hybrid fusion cluster based cooperative spectrum ...Ayman El-Saleh
This presentation shows performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks. For more details, send an email to ayman.elsaleh@gmail.com
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
An analtical analysis of w cdma smart antennamarwaeng
The document analyzes the signal-to-interference-noise ratio (SINR) performance of two beamforming methods, complex conjugate (CC) and direction-of-arrival (DOA), used in Wideband Code Division Multiple Access (W-CDMA) smart antenna systems. It derives analytic SINR evaluation equations for both methods under different channel conditions. The results show that the CC and DOA methods provide the same mean SINR performance but the CC method is more robust to channel estimation errors. Simulation results are presented to verify the analytic performance and robustness analyses.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...Polytechnique Montreal
Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.
Optimal Synthesis of Array Pattern for Concentric Circular Antenna Array Usin...IDES Editor
In this paper one optimization heuristic search
technique, Hybrid Evolutionary Programming (HEP) is
applied to the process of synthesizing three-ring Concentric
Circular Antenna Array (CCAA) focused on maximum
sidelobe-level reduction. This paper assumes non-uniform
excitations and uniform spacing of excitation elements in each
three-ring CCAA design. Experimental results reveal that the
design of non-uniformly excited CCAAs with optimal current
excitations using the method of HEP provides a considerable
sidelobe level reduction with respect to the uniform current
excitation with d=λ/2 element-to-element spacing. Among the
various CCAA designs, the design containing central element
and 4, 6 and 8 elements in three successive concentric rings
proves to be such global optimal design with global minimum
SLL (-40.22 dB) as determined by HEP.
This document summarizes a new algorithm called MewDC-NMF for unsupervised unmixing of hyperspectral images. MewDC-NMF stands for Minimum endmember-wise Distance Constrained Nonnegative Matrix Factorization. It simultaneously extracts endmembers and estimates abundance fractions without requiring pure pixels. This is accomplished by imposing a distance constraint between endmembers to make their spectra more compact during optimization. Experiments on synthetic and real AVIRIS data show MewDC-NMF outperforms other constrained NMF methods in extracting more accurate endmembers and estimating abundances.
1) The document discusses robust adaptive beamforming techniques to improve robustness against uncertainties in array manifold, such as direction-of-arrival mismatch.
2) It proposes an alternative realization of robust linearly constrained minimum variance beamforming that uses an ellipsoidal uncertainty constraint on the steering vector.
3) A key contribution is integrating diagonal loading techniques by deriving an optimum variable loading level, providing "loading-on-demand" instead of fixed loading. This allows accurate computation of the diagonal loading level based on the uncertainty constraint.
Performance optimization of hybrid fusion cluster based cooperative spectrum ...Ayman El-Saleh
This presentation shows performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks. For more details, send an email to ayman.elsaleh@gmail.com
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
An analtical analysis of w cdma smart antennamarwaeng
The document analyzes the signal-to-interference-noise ratio (SINR) performance of two beamforming methods, complex conjugate (CC) and direction-of-arrival (DOA), used in Wideband Code Division Multiple Access (W-CDMA) smart antenna systems. It derives analytic SINR evaluation equations for both methods under different channel conditions. The results show that the CC and DOA methods provide the same mean SINR performance but the CC method is more robust to channel estimation errors. Simulation results are presented to verify the analytic performance and robustness analyses.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...Polytechnique Montreal
Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.
Optimal Synthesis of Array Pattern for Concentric Circular Antenna Array Usin...IDES Editor
In this paper one optimization heuristic search
technique, Hybrid Evolutionary Programming (HEP) is
applied to the process of synthesizing three-ring Concentric
Circular Antenna Array (CCAA) focused on maximum
sidelobe-level reduction. This paper assumes non-uniform
excitations and uniform spacing of excitation elements in each
three-ring CCAA design. Experimental results reveal that the
design of non-uniformly excited CCAAs with optimal current
excitations using the method of HEP provides a considerable
sidelobe level reduction with respect to the uniform current
excitation with d=λ/2 element-to-element spacing. Among the
various CCAA designs, the design containing central element
and 4, 6 and 8 elements in three successive concentric rings
proves to be such global optimal design with global minimum
SLL (-40.22 dB) as determined by HEP.
This document summarizes a new algorithm called MewDC-NMF for unsupervised unmixing of hyperspectral images. MewDC-NMF stands for Minimum endmember-wise Distance Constrained Nonnegative Matrix Factorization. It simultaneously extracts endmembers and estimates abundance fractions without requiring pure pixels. This is accomplished by imposing a distance constraint between endmembers to make their spectra more compact during optimization. Experiments on synthetic and real AVIRIS data show MewDC-NMF outperforms other constrained NMF methods in extracting more accurate endmembers and estimating abundances.
1) The document discusses robust adaptive beamforming techniques to improve robustness against uncertainties in array manifold, such as direction-of-arrival mismatch.
2) It proposes an alternative realization of robust linearly constrained minimum variance beamforming that uses an ellipsoidal uncertainty constraint on the steering vector.
3) A key contribution is integrating diagonal loading techniques by deriving an optimum variable loading level, providing "loading-on-demand" instead of fixed loading. This allows accurate computation of the diagonal loading level based on the uncertainty constraint.
This document summarizes a research paper about designing beampatterns for MIMO radar systems using a covariance-based method while accounting for the locations of transmitter antennas. It discusses how changing antenna locations is equivalent to changing carrier frequency. The paper proposes optimizing two cost functions: 1) Pushing sidelobes away from the main lobe to minimize interference, and 2) Maximizing power around target locations without extra sidelobes to improve target detection. It formulates these cost functions and outlines an algorithm to optimize them using the cross-correlation matrix and antenna locations as design variables.
Uav route planning for maximum target coveragecseij
Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil operations is getting popular. One of
the challenges in effectively tasking these expensive vehicles is planning the flight routes to monitor the
targets. In this work, we aim to develop an algorithm which produces routing plans for a limited number of
UAVs to cover maximum number of targets considering their flight range.
The proposed solution for this practical optimization problem is designed by modifying the Max-Min Ant
System (MMAS) algorithm. To evaluate the success of the proposed method, an alternative approach,
based on the Nearest Neighbour (NN) heuristic, has been developed as well. The results showed the success
of the proposed MMAS method by increasing the number of covered targets compared to the solution based
on the NN heuristic.
Biogeography Based Optimization Approach for Optimal Power Flow Problem Consi...IDES Editor
This paper presents a novel Biogeography Based
Optimization (BBO) algorithm for solving multi-objective
constrained optimal power flow problems in power system. In
this paper, the feasibility of the proposed algorithm is
demonstrated for IEEE 30-bus system with three different
objective functions and it is compared to other well
established population based optimization techniques. A
comparison of simulation results reveals better solution
quality and computation efficiency of the proposed algorithm
over particle swarm optimization (PSO), Real Coded Genetic
algorithm (RGA) for the global optimization of multiobjective
constrained OPF problems.
Performance of cognitive radio networks with maximal ratio combining over cor...Polytechnique Montreal
This document analyzes the performance of cognitive radio networks using maximal ratio combining over correlated Rayleigh fading channels. It presents a simple analytical method to derive closed-form expressions for the probabilities of detection and false alarm. The key findings are:
1) The detection probability is a monotonically increasing function of the number of antennas, as more antennas provides more diversity gain.
2) Antenna correlation degrades the sensing performance compared to independent antennas. Higher correlation results in lower detection probability.
3) Complementary receiver operating characteristic curves illustrate that both higher signal-to-noise ratio and lower antenna correlation improve detection performance by increasing the detection probability and decreasing the probability of miss at a given false alarm probability.
Airy Function Based Papr Reduction Method for Ofdm SystemsIJMER
This document proposes a new companding technique using the Airy function to reduce peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. The Airy function is used as the companding function to transform OFDM signals prior to amplification. Simulation results show the proposed technique reduces PAPR more effectively than exponential and μ-law companding, with up to 1.5dB lower complementary cumulative distribution function values and improved bit error rate performance compared to existing techniques under additive white Gaussian noise. The amount of PAPR reduction can be controlled by adjusting the α parameter in the Airy function.
This document summarizes research on designing the transmitted beampattern for non-uniform MIMO radar arrays. It first reviews covariance-based beampattern design methods for MIMO radar. It then analyzes how the transmitted beampattern is affected by the locations of transmitter antennas. Numerical examples demonstrate that optimizing the beampattern with respect to antenna locations is a non-convex problem that requires heuristic approaches like genetic algorithms. The document concludes that antenna position is an important parameter that can influence the transmitted beampattern in MIMO radar systems.
Implementation of D* Path Planning Algorithm with NXT LEGO Mindstorms Kit for...idescitation
Autonomous Robots use various Path Planning
algorithms to navigate, to the target point. In the real world
situation robot may not have a complete picture of the obstacles
in its environment. The classical path planning algorithms
such as A*, D* are cost based where the shortest path to the
target is calculated based on the distance to be travelled. In
order to provide real time shortest path solutions, cost
computation has to be redone whenever new obstacles are
identified. D* is a potential search algorithm, capable of
planning shortest path in unknown, partially known and
changing environments. This paper brings out the simulation
of D* algorithm in C++ and the results for different test cases.
It also elucidates the implementation of the algorithm with
NXT LEGO Mindstorms kit using RobotC language and
evaluation in real time scenario.
Eigen Subspace based Direction of Arrival Estimation for Coherent SourcesINFOGAIN PUBLICATION
Direction of arrival (DOA) estimation technology plays an important role in enhancing the performance of the adaptive arrays for mobile communication. In this paper comparative performance analysis of eigen subspace based DOA estimation for coherent sources is presented. A number of DOA estimation algorithms based on eigen subspace method have been developed. Among these MUSIC algorithm is considered to have exceptionally good results. The focus of this paper is to unveil the performance characteristics of MUSIC algorithm and its improved version for coherent sources. The simulation results show that the improved MUSIC algorithm is the best. Also it can be observed that the resolution of DOA estimation improves as the number of snapshots and signal to noise ratio increases.
Solving Unit Commitment Problem Using Chemo-tactic PSO–DE Optimization Algori...IDES Editor
This paper presents Chemo-tactic PSO-DE
(CPSO-DE) optimization algorithm combined with
Lagrange Relaxation method (LR) for solving Unit
Commitment (UC) problem. The proposed approach
employs Chemo-tactic PSO-DE algorithm for optimal
settings of Lagrange multipliers. It provides high-quality
performance and reaches global solution and is a hybrid
heuristic algorithm based on Bacterial Foraging
Optimization (BFO), Particle Swarm Optimization (PSO)
and Differential Evolution (DE). The feasibility of the
proposed method is demonstrated for 10-unit, 20-unit,
and 40-unit systems respectively. The test results are
compared with those obtained by Lagrangian relaxation
(LR), genetic algorithm (GA), evolutionary programming
(EP), and genetic algorithm based on unit characteristic
classification (GAUC), enhanced adaptive Lagrangian
relaxation (ELR), integer-coded genetic algorithm
(ICGA) and hybrid particle swarm optimization (HPSO)
in terms of solution quality. Simulation results show that
the proposed method can provide a better solution.
Sampling based positioning of unmanned aerial vehicles as communication relay...Inkonova AB
In the last years, the use of Unmanned Aerial Vehicles (UAVs, also known as “drones”) have found application in different environments that are dangerous or inaccessible by humans like inspection or mapping of underground mining stopes or shafts. During a drone mission it is often required to maintain connectivity with the ground station (referred hereinafter as GS). Even in autonomous flights, real-time communication provides several advantages like active operator supervision and eventual mission correction, in-flight mapping data transfer in case of drone crash inside an inaccessible area and others. In this context, we are interested in using a drone “leader” to explore unknown, dangerous and/or inaccessible underground areas, while keeping constant communication with the GS.
In this paper, we address the problem of using a swarm of autonomous drones, “repeaters”, as a relay chain to maintain communication between a GS and the drone leader responsible for exploration and data acquisition. We propose a sampling-based solution for dynamical positioning of the relay chain. Our method is fully decentralized, scalable and can deal with the case when the trajectory of the main drone is unknown. Simulation results are provided to show the performance of the proposed algorithm.
To simulate the behavior of the relay chain, we use a 2D simulation environment where the trajectory of the leader is predefined but not provided to the repeaters. The model used for the drone’s motion is based on a control signal that is provided as an acceleration and velocity that are bounded, and the drone is modeled as a point in space without orientation (also known as “headless” or “head-free”). In trivial situations, our algorithm can position the relay chain from the current and past mapping data from the leader. Further exploration and analysis of the utility functions to evaluate the sampled positions could drastically improve the performance. A higher level coordination for the whole drone repeaters’ chain could be achieved by using Behavior Trees, which would also increase the robustness and reliability of the whole system.
The document proposes a server-based Dynamic Optimal Random Access (DORA) algorithm for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications in vehicular ad hoc networks (VANETs). DORA aims to optimize uplink transmissions from vehicles to roadside access points (APs) by taking into account varying channel contention and data rates over time. For a single AP, DORA uses dynamic programming to compute the optimal access policy. For multiple APs, the Joint DORA algorithm jointly optimizes the policy across APs. Simulation results show DORA achieves up to 207% higher upload ratios than heuristic schemes, with
This document discusses using implicit training via the MUSIC algorithm to approximate the capacity of an MMSE equalizer in fading channels without full channel state information. It shows that for a slow fading channel, the MUSIC algorithm can approach the explicit MMSE training capacity of 5 bits/Hz within 1 dB. For a faster fading channel, implicit training via MUSIC can realize up to a 2 dB improvement in capacity compared to explicit training, achieving over 4 bits/Hz capacity. The document outlines the MMSE equalizer, MUSIC algorithm, and a proposed method to use MUSIC to implicitly train an MMSE equalizer without explicit training symbols.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Singular Value Decomposition: Principles and Applications in Multiple Input M...IJCNCJournal
The authors discuss the importance of using the singular value decomposition (SVD) in computing the capacity of multiple input multiple output (MIMO) and in estimation the channel gain from the transmitter to the receiver. Examples that show how the SVD simplifies computing the MIMO channel capacity are discussed. Numerical results that show what factors determine the performance of using SVD in channel
estimation are also discussed.
DESIGN OF DELAY COMPUTATION METHOD FOR CYCLOTOMIC FAST FOURIER TRANSFORMsipij
In this paper the Delay Computation method for Common Sub expression Elimination algorithm is being implemented on Cyclotomic Fast Fourier Transform. The Common Sub Expression Elimination algorithm is combined with the delay computing method and is known as Gate Level Delay Computation with Common Sub expression Elimination Algorithm. Common sub expression elimination is effective
optimization method used to reduce adders in cyclotomic Fourier transform. The delay computing method is based on delay matrix and suitable for implementation with computers. The Gate level delay computation method is used to find critical path delay and it is analyzed on various finite field elements. The presented algorithm is established through a case study in Cyclotomic Fast Fourier Transform over finite field. If Cyclotomic Fast Fourier Transform is implemented directly then the system will have high additive complexities. So by using GLDC-CSE algorithm on cyclotomic fast Fourier transform, the additive
complexities will be reduced and also the area and area delay product will be reduced.
The document discusses optimization of internet traffic routing. It presents three examples: (1) access control lists, which are not truly optimal due to duplication; (2) wireless network routing, which can be formulated as minimal spanning tree or relay problems; and (3) routing protocols, which are not inherently optimal as routers only know local topology, not global. True optimization requires considering traffic flows across an entire network or domain simultaneously.
This summarizes a fast re-route method to find an alternate path after a link failure, before the interior gateway protocol has reconverged. The method selects the next hop among a source node's neighbors based on which has the lowest number of visits (multiplicity) and shortest estimated distance to the destination. It is proven to always find an alternate path if one exists. The method improves over loop-free alternate approaches by not requiring tunnels. It can find paths for simple cases like a square topology where LFA fails.
The document proposes a successive overrelaxation (SOR)-based linear precoding scheme to reduce the complexity of matrix inversion required for regularized zero-forcing (RZF) precoding in massive MIMO systems. The SOR-based precoding approximates the matrix inversion using an iterative SOR method, which can reduce complexity by about one order of magnitude compared to RZF precoding. It is also shown to converge within a few iterations and achieve performance close to RZF precoding. An empirical formula is provided to choose the optimal relaxation parameter for the SOR method in practical massive MIMO configurations.
1) The document presents TIRI-DCT, a new video fingerprinting technique that aims to overcome limitations of existing methods.
2) TIRI-DCT extracts fingerprints from temporally informative representative images (TIRIs) of video segments, capturing both spatial and temporal information.
3) It is more efficient than previous 3D-DCT technique while maintaining good performance against distortions. TIRI-DCT reduces false matches through longer fingerprints.
This document summarizes an experiment to optimize the performance of an aluminum dross crusher using the Taguchi method of design of experiments. The researchers identified three parameters that affect the crusher's performance: blade profile, rotation speed, and duration. They conducted experiments using an L9 orthogonal array with these parameters set at different levels. Analysis of the results found that blade profile and duration were significant parameters, while rotation speed was not. Confirmation experiments at the optimized parameter settings increased the aluminum recovery rate from 60% to an average of 75%.
This document summarizes a research paper about designing beampatterns for MIMO radar systems using a covariance-based method while accounting for the locations of transmitter antennas. It discusses how changing antenna locations is equivalent to changing carrier frequency. The paper proposes optimizing two cost functions: 1) Pushing sidelobes away from the main lobe to minimize interference, and 2) Maximizing power around target locations without extra sidelobes to improve target detection. It formulates these cost functions and outlines an algorithm to optimize them using the cross-correlation matrix and antenna locations as design variables.
Uav route planning for maximum target coveragecseij
Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil operations is getting popular. One of
the challenges in effectively tasking these expensive vehicles is planning the flight routes to monitor the
targets. In this work, we aim to develop an algorithm which produces routing plans for a limited number of
UAVs to cover maximum number of targets considering their flight range.
The proposed solution for this practical optimization problem is designed by modifying the Max-Min Ant
System (MMAS) algorithm. To evaluate the success of the proposed method, an alternative approach,
based on the Nearest Neighbour (NN) heuristic, has been developed as well. The results showed the success
of the proposed MMAS method by increasing the number of covered targets compared to the solution based
on the NN heuristic.
Biogeography Based Optimization Approach for Optimal Power Flow Problem Consi...IDES Editor
This paper presents a novel Biogeography Based
Optimization (BBO) algorithm for solving multi-objective
constrained optimal power flow problems in power system. In
this paper, the feasibility of the proposed algorithm is
demonstrated for IEEE 30-bus system with three different
objective functions and it is compared to other well
established population based optimization techniques. A
comparison of simulation results reveals better solution
quality and computation efficiency of the proposed algorithm
over particle swarm optimization (PSO), Real Coded Genetic
algorithm (RGA) for the global optimization of multiobjective
constrained OPF problems.
Performance of cognitive radio networks with maximal ratio combining over cor...Polytechnique Montreal
This document analyzes the performance of cognitive radio networks using maximal ratio combining over correlated Rayleigh fading channels. It presents a simple analytical method to derive closed-form expressions for the probabilities of detection and false alarm. The key findings are:
1) The detection probability is a monotonically increasing function of the number of antennas, as more antennas provides more diversity gain.
2) Antenna correlation degrades the sensing performance compared to independent antennas. Higher correlation results in lower detection probability.
3) Complementary receiver operating characteristic curves illustrate that both higher signal-to-noise ratio and lower antenna correlation improve detection performance by increasing the detection probability and decreasing the probability of miss at a given false alarm probability.
Airy Function Based Papr Reduction Method for Ofdm SystemsIJMER
This document proposes a new companding technique using the Airy function to reduce peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. The Airy function is used as the companding function to transform OFDM signals prior to amplification. Simulation results show the proposed technique reduces PAPR more effectively than exponential and μ-law companding, with up to 1.5dB lower complementary cumulative distribution function values and improved bit error rate performance compared to existing techniques under additive white Gaussian noise. The amount of PAPR reduction can be controlled by adjusting the α parameter in the Airy function.
This document summarizes research on designing the transmitted beampattern for non-uniform MIMO radar arrays. It first reviews covariance-based beampattern design methods for MIMO radar. It then analyzes how the transmitted beampattern is affected by the locations of transmitter antennas. Numerical examples demonstrate that optimizing the beampattern with respect to antenna locations is a non-convex problem that requires heuristic approaches like genetic algorithms. The document concludes that antenna position is an important parameter that can influence the transmitted beampattern in MIMO radar systems.
Implementation of D* Path Planning Algorithm with NXT LEGO Mindstorms Kit for...idescitation
Autonomous Robots use various Path Planning
algorithms to navigate, to the target point. In the real world
situation robot may not have a complete picture of the obstacles
in its environment. The classical path planning algorithms
such as A*, D* are cost based where the shortest path to the
target is calculated based on the distance to be travelled. In
order to provide real time shortest path solutions, cost
computation has to be redone whenever new obstacles are
identified. D* is a potential search algorithm, capable of
planning shortest path in unknown, partially known and
changing environments. This paper brings out the simulation
of D* algorithm in C++ and the results for different test cases.
It also elucidates the implementation of the algorithm with
NXT LEGO Mindstorms kit using RobotC language and
evaluation in real time scenario.
Eigen Subspace based Direction of Arrival Estimation for Coherent SourcesINFOGAIN PUBLICATION
Direction of arrival (DOA) estimation technology plays an important role in enhancing the performance of the adaptive arrays for mobile communication. In this paper comparative performance analysis of eigen subspace based DOA estimation for coherent sources is presented. A number of DOA estimation algorithms based on eigen subspace method have been developed. Among these MUSIC algorithm is considered to have exceptionally good results. The focus of this paper is to unveil the performance characteristics of MUSIC algorithm and its improved version for coherent sources. The simulation results show that the improved MUSIC algorithm is the best. Also it can be observed that the resolution of DOA estimation improves as the number of snapshots and signal to noise ratio increases.
Solving Unit Commitment Problem Using Chemo-tactic PSO–DE Optimization Algori...IDES Editor
This paper presents Chemo-tactic PSO-DE
(CPSO-DE) optimization algorithm combined with
Lagrange Relaxation method (LR) for solving Unit
Commitment (UC) problem. The proposed approach
employs Chemo-tactic PSO-DE algorithm for optimal
settings of Lagrange multipliers. It provides high-quality
performance and reaches global solution and is a hybrid
heuristic algorithm based on Bacterial Foraging
Optimization (BFO), Particle Swarm Optimization (PSO)
and Differential Evolution (DE). The feasibility of the
proposed method is demonstrated for 10-unit, 20-unit,
and 40-unit systems respectively. The test results are
compared with those obtained by Lagrangian relaxation
(LR), genetic algorithm (GA), evolutionary programming
(EP), and genetic algorithm based on unit characteristic
classification (GAUC), enhanced adaptive Lagrangian
relaxation (ELR), integer-coded genetic algorithm
(ICGA) and hybrid particle swarm optimization (HPSO)
in terms of solution quality. Simulation results show that
the proposed method can provide a better solution.
Sampling based positioning of unmanned aerial vehicles as communication relay...Inkonova AB
In the last years, the use of Unmanned Aerial Vehicles (UAVs, also known as “drones”) have found application in different environments that are dangerous or inaccessible by humans like inspection or mapping of underground mining stopes or shafts. During a drone mission it is often required to maintain connectivity with the ground station (referred hereinafter as GS). Even in autonomous flights, real-time communication provides several advantages like active operator supervision and eventual mission correction, in-flight mapping data transfer in case of drone crash inside an inaccessible area and others. In this context, we are interested in using a drone “leader” to explore unknown, dangerous and/or inaccessible underground areas, while keeping constant communication with the GS.
In this paper, we address the problem of using a swarm of autonomous drones, “repeaters”, as a relay chain to maintain communication between a GS and the drone leader responsible for exploration and data acquisition. We propose a sampling-based solution for dynamical positioning of the relay chain. Our method is fully decentralized, scalable and can deal with the case when the trajectory of the main drone is unknown. Simulation results are provided to show the performance of the proposed algorithm.
To simulate the behavior of the relay chain, we use a 2D simulation environment where the trajectory of the leader is predefined but not provided to the repeaters. The model used for the drone’s motion is based on a control signal that is provided as an acceleration and velocity that are bounded, and the drone is modeled as a point in space without orientation (also known as “headless” or “head-free”). In trivial situations, our algorithm can position the relay chain from the current and past mapping data from the leader. Further exploration and analysis of the utility functions to evaluate the sampled positions could drastically improve the performance. A higher level coordination for the whole drone repeaters’ chain could be achieved by using Behavior Trees, which would also increase the robustness and reliability of the whole system.
The document proposes a server-based Dynamic Optimal Random Access (DORA) algorithm for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications in vehicular ad hoc networks (VANETs). DORA aims to optimize uplink transmissions from vehicles to roadside access points (APs) by taking into account varying channel contention and data rates over time. For a single AP, DORA uses dynamic programming to compute the optimal access policy. For multiple APs, the Joint DORA algorithm jointly optimizes the policy across APs. Simulation results show DORA achieves up to 207% higher upload ratios than heuristic schemes, with
This document discusses using implicit training via the MUSIC algorithm to approximate the capacity of an MMSE equalizer in fading channels without full channel state information. It shows that for a slow fading channel, the MUSIC algorithm can approach the explicit MMSE training capacity of 5 bits/Hz within 1 dB. For a faster fading channel, implicit training via MUSIC can realize up to a 2 dB improvement in capacity compared to explicit training, achieving over 4 bits/Hz capacity. The document outlines the MMSE equalizer, MUSIC algorithm, and a proposed method to use MUSIC to implicitly train an MMSE equalizer without explicit training symbols.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Singular Value Decomposition: Principles and Applications in Multiple Input M...IJCNCJournal
The authors discuss the importance of using the singular value decomposition (SVD) in computing the capacity of multiple input multiple output (MIMO) and in estimation the channel gain from the transmitter to the receiver. Examples that show how the SVD simplifies computing the MIMO channel capacity are discussed. Numerical results that show what factors determine the performance of using SVD in channel
estimation are also discussed.
DESIGN OF DELAY COMPUTATION METHOD FOR CYCLOTOMIC FAST FOURIER TRANSFORMsipij
In this paper the Delay Computation method for Common Sub expression Elimination algorithm is being implemented on Cyclotomic Fast Fourier Transform. The Common Sub Expression Elimination algorithm is combined with the delay computing method and is known as Gate Level Delay Computation with Common Sub expression Elimination Algorithm. Common sub expression elimination is effective
optimization method used to reduce adders in cyclotomic Fourier transform. The delay computing method is based on delay matrix and suitable for implementation with computers. The Gate level delay computation method is used to find critical path delay and it is analyzed on various finite field elements. The presented algorithm is established through a case study in Cyclotomic Fast Fourier Transform over finite field. If Cyclotomic Fast Fourier Transform is implemented directly then the system will have high additive complexities. So by using GLDC-CSE algorithm on cyclotomic fast Fourier transform, the additive
complexities will be reduced and also the area and area delay product will be reduced.
The document discusses optimization of internet traffic routing. It presents three examples: (1) access control lists, which are not truly optimal due to duplication; (2) wireless network routing, which can be formulated as minimal spanning tree or relay problems; and (3) routing protocols, which are not inherently optimal as routers only know local topology, not global. True optimization requires considering traffic flows across an entire network or domain simultaneously.
This summarizes a fast re-route method to find an alternate path after a link failure, before the interior gateway protocol has reconverged. The method selects the next hop among a source node's neighbors based on which has the lowest number of visits (multiplicity) and shortest estimated distance to the destination. It is proven to always find an alternate path if one exists. The method improves over loop-free alternate approaches by not requiring tunnels. It can find paths for simple cases like a square topology where LFA fails.
The document proposes a successive overrelaxation (SOR)-based linear precoding scheme to reduce the complexity of matrix inversion required for regularized zero-forcing (RZF) precoding in massive MIMO systems. The SOR-based precoding approximates the matrix inversion using an iterative SOR method, which can reduce complexity by about one order of magnitude compared to RZF precoding. It is also shown to converge within a few iterations and achieve performance close to RZF precoding. An empirical formula is provided to choose the optimal relaxation parameter for the SOR method in practical massive MIMO configurations.
1) The document presents TIRI-DCT, a new video fingerprinting technique that aims to overcome limitations of existing methods.
2) TIRI-DCT extracts fingerprints from temporally informative representative images (TIRIs) of video segments, capturing both spatial and temporal information.
3) It is more efficient than previous 3D-DCT technique while maintaining good performance against distortions. TIRI-DCT reduces false matches through longer fingerprints.
This document summarizes an experiment to optimize the performance of an aluminum dross crusher using the Taguchi method of design of experiments. The researchers identified three parameters that affect the crusher's performance: blade profile, rotation speed, and duration. They conducted experiments using an L9 orthogonal array with these parameters set at different levels. Analysis of the results found that blade profile and duration were significant parameters, while rotation speed was not. Confirmation experiments at the optimized parameter settings increased the aluminum recovery rate from 60% to an average of 75%.
Effect of Combustion Air Pre-Heating In Carbon Monoxide Emission in Diesel Fi...IJERA Editor
This paper describes the effect of combustion air pre- heating in Diesel fired heat Treatment Furnace. The main
heat treatment processes are Normalizing, Tempering, Hardening, Annealing, Solution Annealing and Stress
Relieving. The emission of carbon monoxide is measured with combustion air pre-heating and without preheating.
The results are then compared and it is found that the emission of CO is reduced by 29.12%. With the
Combustion air pre-heating a considerable reduction in Specific Furnace Fuel Consumption (SFFC) is obtained.
The test was caaried out at Peekay Steels Casting (P) ltd, Nallalam, Calicut.
General Terms: Heat Treatment Furnace
Study Utility Vehicle Makassar City Transport a High- ErgonomicsIJERA Editor
The development of technology during this was to meet the man, but it should be men must be spoilt, But if it
turns out that all that did not make people feel safe, comfortable, healthy and easy, but the planning process,
decision-making and developments have experienced a deviation orientation. Public transport Transportation in
the Makassar city should be made with implementing aspects promotes ergonomic comfort, but it does not apply
in means of transportation to the public. Issues for public vehicles on access up and down not in accordance
with The aim of the research vehicle users. is to phrases dimensions body which have an effect on to utility
vehicle, to examine the public vehicles that high-promotes ergonomic comfort. The method assessment is the
measurement dimensions body to the passengers as well as the use questionnaires and analyzed in a holistic
approach ergonomics. Results of research high security tools to public vehicles that high-security vehicle users
generally by body dimensions as a powerful than Knee-and-a-half was knee, long your feet, and your elbow
kelantai. While utilities yangbernilai ergonomics was the first and second around 24.76 cm and 49.53 cm, wide
around 24.25 cm and was hangar 104, 78 cm.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
Cyprus International Documentary Film Festival ProgrammeAnima Slides
8th international Cyprus Documentary Film Festival - In August showing in Limassol, Cyprus Θέατρο Ένα Theatro Ena - more info: http://filmfestival.com.cy/
O documento descreve o Renascimento Cultural e Científico, quando a visão de mundo burguesa substituiu a visão medieval teocêntrica. A ciência floresceu com novos estudos da natureza e do corpo humano, enquanto a Igreja perdia influência sobre as manifestações culturais.
This document contains pictures of Myrna Macaalay Rabang and Imelda. The pictures are unlabeled but seem to feature the two people mentioned in the document along with something labeled "Me and Ate".
Este documento presenta el sílabo para el curso de Expresión Gráfica II - 3D en la Facultad de Tecnología de la Universidad Nacional de Educación. El curso se enfoca en enseñar técnicas y métodos de representación gráfica tridimensional para comunicar efectivamente ideas arquitectónicas. El curso utiliza métodos como aprendizaje basado en problemas y aprendizaje experimental. Los estudiantes aprenderán a organizar y representar proyectos arquitectónicos utilizando software de diseño y otras herramientas
Integración de tic en la educación para la preparación de los estudiantes del...CindyTIC
El documento discute la importancia de integrar las TIC en la educación para preparar a los estudiantes del siglo XXI. Señala que los programas de estudio y currículos deben diseñarse para desarrollar competencias como pensamiento crítico, comunicación, trabajo cooperativo y conciencia global. También deben incluir temas transversales como economía, cultura cívica y salud. Las TIC permiten acceder a información y comunicarse de forma efectiva para investigar y organizar el conocimiento.
El Preludio No. 15 de Chopin presenta tres partes: la primera presenta una línea melancólica en la mano derecha con una nota repetida en la izquierda; la segunda parte contrasta con la primera al intercambiar las líneas entre las manos; la tercera parte repite la primera de forma abreviada antes de concluir con la nota repetida apagándose en la coda.
Para desarrollar una comunicación efectiva en momentos difíciles, el lider debe apoyarse en su equipo, para lo cual han desarrollado la capacidad de escucha y el respeto en la comunicación
Este documento proporciona información sobre placas tectónicas y terremotos en 4 secciones. La primera sección define conceptos clave como sismo, latitud, longitud y placas tectónicas. La segunda sección describe los efectos de los terremotos en las personas. La tercera sección explica qué hacer en caso de emergencia durante un terremoto. La cuarta sección concluye que los terremotos se miden en grados de magnitud y que la teoría de placas tectónicas explica cómo se movieron los continentes a lo largo del tiempo.
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
A empresa de tecnologia anunciou um novo smartphone com câmera avançada, bateria de longa duração e processador rápido. O dispositivo também possui tela grande e armazenamento expandível. O lançamento está programado para o final do ano com preço inicial sugerido de US$799.
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.
Vehicle route scheduling and transportation cost minimization in a latex indu...IJRES Journal
The vehicle route scheduling problem is concerned with the determination of routes and schedules for a fleet of vehicles to satisfy the demands of a set of customers. The goal of vehicle routing is to schedule multiple suppliers from various places. Vehicle routing has existed since the advent of the Industrial age, when large-scale production became possible. As the complexity and scale of the manufacturing world increased, the task of optimizing vehicle routing grew. The vehicle routing problem is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Often the context is that of delivering goods located at a central depot to customers who have placed orders for such goods or vice-versa. Implicit is the goal of minimizing the cost of distributing the goods. Many methods have been developed for searching for good solutions to the problem, however even for the smallest problems, finding global minimum for the cost function is computationally complex. The paper presents an optimization algorithm using Particle Swarm Optimization (PSO) for the vehicle routing that would enable the logistic manager of a latex industry to minimize the transportation cost and maximize the collection using minimum number of vehicles.
P REDICTION F OR S HORT -T ERM T RAFFIC F LOW B ASED O N O PTIMIZED W...ijcsit
Short term traffic forecasting has been a very impo
rtant consideration in many areas of transportation
research for more than 3 decades. Short-term traffi
c forecasting based on data driven methods is one o
f the
most dynamic and developing research arenas with en
ormous published literature. In order to improve
forecasting model accuracy of wavelet neural networ
k, an adaptive particle swarm optimization algorith
m
based on cloud theory was proposed, not only to hel
p improve search performance, but also speed up
individual optimizing ability. And the inertia weig
ht adaptively changes depending on X-conditional cl
oud
generator which has the stable tendency and randomn
ess property .Then the adaptive particle swarm
optimization algorithm based on cloud theory was us
ed to optimize the weights and thresholds of wavele
t
BP neural network, Instead of traditional gradient
descent method . At last, wavelet BP neural network
was
trained to search for the optimal solution. Based o
n above theory, an improved wavelet neural network
model based on modified particle swarm optimization
algorithm was proposed and the availability of the
modified prediction method was proved by predicting
the time series of real traffic flow. At last, the
computer simulations have shown that the nonlinear
fitting and accuracy of the modified prediction
methods are better than other prediction methods.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes a research paper that reviews techniques for optimal design and placement of pilot symbols for channel estimation in OFDM systems operating under rapidly time-varying channels. It discusses how particle swarm optimization, the Cramér–Rao Bound, and Bayesian Cramér–Rao Bound techniques are commonly used to optimize pilot sequence design to improve channel estimation performance and reduce intercarrier interference. Grouping pilot tones into clusters rather than evenly spacing each pilot tone can provide better channel estimation against doubly selective channels. The optimal clustered pilot sequence is derived using maximum likelihood estimation and is independent of signal-to-noise ratio or Doppler rate.
This document discusses using a learning automata approach to predict target locations in wireless sensor networks to reduce energy consumption and improve tracking accuracy. It proposes a learning automata based method that uses a target's movement history to predict its next location. Related works on target tracking techniques like tree-based, cluster-based, and prediction-based methods are summarized. Learning automata concepts are introduced. Simulation results are said to show the proposed method improves energy efficiency, reduces missed targets, and decreases transmitted packets compared to other methods.
This document proposes an improved hybrid behavior ant colony algorithm to solve vehicle routing problems. It defines four types of ant behaviors - random, greedy, pheromone-based, and a hybrid behavior considering factors like distance, saving value, and vehicle load. The algorithm allows ants to select behaviors and routes probabilistically based on these factors. Simulation experiments on a 31-city dataset show the hybrid behavior outperforms basic ant colony and other variants, finding better solutions on average. The results demonstrate this improved algorithm can effectively solve vehicle routing problems.
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost
pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of
optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in
transportation field due to the spatial nature of such problems. In this context, we couple a geographical
information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical
solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
INTEGRATION OF GIS AND OPTIMIZATION ROUTINES FOR THE VEHICLE ROUTING PROBLEMijccmsjournal
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in transportation field due to the spatial nature of such problems. In this context, we couple a geographical information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
Integration Of Gis And Optimization Routines For The Vehicle Routing Problemijccmsjournal
This document discusses integrating geographic information systems (GIS) and optimization routines to efficiently solve vehicle routing problems (VRP). Specifically, it proposes coupling a GIS with a particle swarm optimization metaheuristic. This allows generating a geographic solution by mapping optimized vehicle routes rather than just a numeric solution. The approach is demonstrated on a real-world VRP case study for a region in Tunisia. Customer locations, roads, and potential routes are modeled in GIS. Particle swarm optimization is then used to determine the minimum cost vehicle routes while respecting vehicle capacities. This integrated GIS-optimization approach allows visualizing optimized routing solutions on maps for practical transportation planning.
Ant Colony with Colored Pheromones Routing for Multi Objectives Quality of Se...IJORCS
In this article, we present a new Ant-routing algorithm with colored pheromones and clustering techniques for satisfying users’ Quality of Service (QoS) requirements in Wireless Sensor Networks (WSNs). An important problem is to detect the best route from a source node to the destination node. Moreover, it is considered that the feature of non-uniformly distributed traffic load and possibility existing of the traffic requiring various performances; therefore, it is assumed the different class of traffic required for QoS of communication. In this paper, novel protocol, the suitability of using meta-heuristic an ant colony optimization based on energy saving and multi objectives, the demand of QoS routing protocol for WSN will be very adaptive ,resident power and mainly decrease end-to-end delay. These metrics are used by colored pheromones adapted to the traffic classes. Moreover, we reinforce the proposed method for scalability issue by clustering techniques. We use a proactive route discover algorithms in clusters and reactive discovery mechanism between different clusters. Compared to existing QoS routing protocols, the novel algorithm has been designed for various service categories such as real time (RT) and best effort (BE) traffic, resulted lower packet deadline miss ratio and higher energy efficiency and better QoS and longer lifetime.
The International Journal of Engineering and Science (The IJES)theijes
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.
On Demand Bandwidth Reservation for Real- Time Traffic in Cellular IP Network...IDES Editor
As real-time traffic requires more attention, it
is given priority over non-real-time traffic in Cellular IP
networks. Bandwidth reservation is often applied to serve
such traffic in order to achieve better Quality of Service
(QoS). Evolutionary Algorithms are quite useful in
solving optimization problems of such nature. This paper
employs Genetic Algorithm (GA) for bandwidth
management in Cellular IP network. It compares the
performance of the model with another model used for
optimizing Connection Dropping Probability (CDP) using
Particle Swarm Optimization (PSO). Both models, GA
based and PSO based, try to minimize the Connection
Dropping Probability for real-time users in the network
by searching the free available bandwidth in the user’s
cell or in the neighbor cells and assigning it to the realtime
users. Alternatively, if the free bandwidth is not
available, the model borrows the bandwidth from nonreal
time-users and gives it to the real-time users.
Experimental results evaluate the performance of the GA
based model. The comparative study between both the
models indicates that GA based model has an edge over
the PSO based one.
This document proposes a hybrid optimization algorithm using ant colony optimization and particle swarm optimization to solve the multiobjective multicast routing problem in wireless sensor networks. The goal is to optimize two objectives simultaneously - end-to-end delay and total transmitted power. ACO and PSO are combined to find Pareto-optimal solutions efficiently. Simulation results show the algorithm can find near-optimal solutions for minimizing delay and power consumption when routing data from a source to multiple destinations in wireless sensor networks.
Optimization Of K-Means Clustering For DECT Using ACOIRJET Journal
This document summarizes a research paper that proposes using an ant colony optimization (ACO) algorithm to optimize k-means clustering for distributed computing in a mobile cloud environment. The goal is to minimize network usage by optimally deploying software components between mobile devices and cloud infrastructure. The paper first discusses challenges with availability, load balancing, and computation offloading in cloud and mobile cloud systems. It then presents a new ACO-based graph partitioning algorithm that focuses on finding the shortest path between users and high-end servers, rather than just optimizing software deployment. The algorithm uses pheromone trails to develop optimal paths over time, reducing energy consumption and delay. Simulation results on test graphs of varying sizes demonstrate that the proposed approach
This document summarizes a research paper that proposes using multi-objective particle swarm optimization (MOPSO) to maximize coverage and lifetime in wireless sensor networks. The paper formulates the sensor network optimization problem as maximizing two objectives: coverage of the monitored area and lifetime of the sensor network. It describes using MOPSO to determine optimal sensor node locations that provide a set of Pareto optimal trade-offs between these two objectives. Simulation results show MOPSO obtaining a well-populated Pareto front of solutions and example optimal layouts that maximize either coverage or lifetime.
Hybrid of Ant Colony Optimization and Gravitational Emulation Based Load Bala...IRJET Journal
This document proposes a hybrid Ant Colony Optimization (ACO) and Gravitational Emulation Local Search (GELS) algorithm for load balancing in cloud computing. ACO is combined with GELS to take advantage of both algorithms - ACO is good for global search using pheromone trails while GELS is powerful for local search based on gravitational attraction. The hybrid algorithm is tested using CloudSim and shows improvements over existing algorithms like GA-GELS in metrics like resource utilization, makespan, and load balancing level.
Vehicle routing problem is a NP-hard problem, with the expansion of problem solving more difficult.
This paper proposes a hybrid behavior based on ant colony algorithm to solve the problem, ant to different
objectives in the first place as the path selection according to the analysis of the impact on the algorithm, then
define the ant behavior and design four concrete ant behavior by selecting different ways of ant behavior to
form different improved algorithm. Finally, experimental results show that the improved algorithm can solve
vehicle routing problems quickly and effectively.
An artificial immune system algorithm for solving the uncapacitated single all...IJECEIAES
The present paper deals with a variant of hub location problems (HLP): the uncapac- itated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.
This document describes algorithms for detecting single radio pulses in real-time using graphics processing units (GPUs). It presents two new algorithms that use incomplete sets of boxcar filters to detect pulses at accelerated speeds with minimal signal loss. The algorithms were tested on simulated data and were found to process data 266-500 times faster than real-time on GPUs, detecting pulses with a mean 7% reduction in signal power.
Algorithms And Optimization Techniques For Solving TSPCarrie Romero
The document discusses three algorithms - simulated annealing, ant colony optimization, and genetic algorithm - for solving the traveling salesman problem (TSP). It analyzes each algorithm's approach, parameters used, and results of experiments on 15 and 50 randomly generated cities. Simulated annealing had average distances of 4.1341 and 20.1316 units for 15 and 50 cities respectively. Ant colony optimization yielded average distances of 3.9102 units for 15 cities, running faster than simulated annealing. Genetic algorithm was tested on 15 cities in Brazil.
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.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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.
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
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
1. Shenglong YU, Xiaofei YANG, Yuming BO, Zhimin CHEN, Jie ZHANG / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.136-141
A novel particle swarm Optimization algorithm based Fine
Adjustment for solution of VRP
1
Shenglong YU , 2 Xiaofei YANG , 2Yuming BO, 1 Zhimin CHEN, 1Jie
ZHANG
[1] School of Automation, Nanjing University of Science and Technology, Nanjing 2 10094,PR China,
[2] Shanghai Radio Equipment Research Institute, Shanghai 200090, China
Abstract
To solve the problem of easily trapped and electrical engineer Eberhart in 1995. It
into local optimization and instable is sourced from group behavior theory and
calculation results, a new fine-adjustment enlightened by the fact that bird group and fish
mechanism-based particle swarm optimized group will develop toward a correct and proper
algorithm applicable to solution seeking of direction as anticipated through the special
VRP model is presented in this paper. This information delivery among individuals. It is a
algorithm introduces the fine-adjustment self-adaption random optimization algorithm
mechanism so as to get adapt to the judgment based on population searching. Due to its
base of function directional derivative value. simplicit y, convenience in actualization and
By adjusting the optimal value and group high calculation speed, it has been widel y
value, the local searching ability of algorithm applied in route planning, multi-target
in the optimal area is improved. The tracking, data classification, flow planning and
experiment results indicate that the algorithm decision support etc.. However, PSO also has
presented here displays higher convergence some defects. If the initial state forces the
speed, precision and stability than PSO, and population to converge toward the local
is a effective solution to VRP. optimal area, then the population may fail t o
jump out of the local area and thus be trapped,
making application of PSO in VRP solution
Key words: particle swarm, fine adjustment, become difficult.
VRP, perturbation, convergence In this paper, a fine adjustment is
introduced to PSO, acquiring FT-PSO which
will conduce to enhancement of local
1. Introduction searching of population in the optimal area at
[1]
the end of searching. This method, taking the
Vehicle routing problem was firstl y optimal value of particles as the center, forms
put forward by Dantzing and Ramser in a fine-adjustment area based on the mode
[ 2] defined by the algorithm. In the fine
1959.VRP can be described as : for a series
adjustment area, a fine adjustment particle
of loading and unloading places, to plan
swarm will form to calculate the fitness
proper vehicle route will facilitate smooth pass
function of each fine adjustment particle. The
of vehicle and certain optimal goals can be
particle with the largest function value is
reached by satisfying specific constraints.
selected to replace the optimal value of
Generally the constraint include: goods
updated particles, thus the precision of the
demand, quantity, delivery time, deliver y
algorithm is enhanced.
vehicles, time constraint etc., while the
optimal goals may consist of shortest distance,
lowest cost, the least time and so on. VRP is a 2. Establishment of VRP model
[3] The problem of vehicle route is decried
very important content in logistics as follows: the distribution center is required to
distribution researches. A good VRP algorithm deliver goods to l clients (1, 2, , l ) , and the
and strategy can bring huge benefit to logistics
distribution. cargo volume of the i th client
Particle swarm optimization algorithm is gi (i 1, 2, l ) , and the load weight of each
[ 4]
(PSO) is an intelligent optimization vehicle is q , at the same time g i q . Here we
algorithm put forward by
need to find the shortest route which well
satisfies the transport requirements.
American social psychologist Kennedy is In the mathematical model established
136 | P a g e
2. Shenglong YU, Xiaofei YANG, Yuming BO, Zhimin CHEN, Jie ZHANG / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.136-141
here, cij denotes the transportation cost from
point i to point j , including the distance, time yik 0or1, i 0,1, , l;k 1, 2, , m
(9)
and expenses etc.. The distribution center is
number as 0, clients numbered i (i 1, 2, , l ) , 3. Particle swarm algorithm
[5]
vehicle numbered k , at most m vehicles can Particle swarm algorithm essentially
be used. After finishing the transportation, the is enlightened by the modeling and simulation
vehicle will return back to the distribution center. research results of many bird populations,
The variables are defining as follows: wherein the modeling and simulation algorithm
1vehiclekdriveto jfromi mainly utilizes the mode of biologist Hepper . It
xijk (1) is an optimal algorithm based on group
0otherwise intelligent theory that will generate group
1Cargo transportation task of itobefinishedbyk intelligent to guide the optimal searching
yik (2)
through cooperation and competition of particles,
0otherwise [6]
The vehicle route problem is dealing and has been widely applied now. PSO
with the integer planning with constraints, that is, algorithm can be expressed as: initializing a
the sum of the clients' task on each vehicle route particle swarm with quantity of m, wherein the
can not exceed the capacity of this vehicle. number of iteration n , and the position of the
The total minimal transportation cost after i th particle X i ( xi1 , xi 2 , xin ) , the
finishing the service is:
speed Vi (vi1 , vi 2 , vin ) . During each iteration,
l l m particles are keeping upgrade its speed and
m i n c j xi k
Z i j position through individual
i 0 j 0 k 1
extremum P ( P1 , P 2 , P )
i i i in and global
m l
R max( gi yik q, 0) (3) extremum G ( g1 , g 2 , g n ) , accordingly
k 1 i 1 [7 ]
reaching optimization solution seeking . The
where R takes a very large positive number as upgrade equation is
the punishment coefficient.
Supposing all the vehicles used satisfying the
load requirements, then we have the following
vk 1 c0vk c1 ( pbestk xk ) c2 ( gbestk xk ) (10)
expression: xk 1 xk vk 1 (11)
l where v k denotes the particle speed, x k is the
g y
i 1
i ik q,k 1, 2, , m (4) position of the current particles, pbestk is the
Giving that only one vehicle is assigned to serve position where particles find the optimal
each customer only once, that is, solution, gbestk is the position of the
optimal solution of the population, c 0 , c1 and
m
1i 1, 2, , l
yik mi 0 (5) c 2 denote the population recognition coefficient,
k 1 c 0 usually is a random number in interval (0,1),
In the model the vehicle reaching and leaving
each client are the same, which is expressed as c1 , c 2 is a random number [8] in (0,2).
follows:
l 4 Particle swarm algorithm improvement
x
i 0
ijk ykj , j 0,1, 2, l ;k (6) 4.1 Mechanism of fine adjustment
According to the flow of PSO, after
calculation of all parties, pbest and a gbest
l
x
will be acquired, wherein the quantity and value
ijk yki ,i 0,1, 2, l ;k (7)
j 0
of pbest depends on the total number of
Given the variable value constraint as 0 or 1, and population. In fine adjustment, gbest is
the expression is: adjusted as the object of fine adjustment with the
goal of searching the optimal solution in the
xijk 0or1, i, j 0,1, , l;k 1, 2, , m
(8) neighboring area of gbest .
137 | P a g e
3. Shenglong YU, Xiaofei YANG, Yuming BO, Zhimin CHEN, Jie ZHANG / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.136-141
Whereas the excellent performance of divides the area and range by the defined method
PSO in fine adjustment principle, we should and forms a fine-adjustment area based on the
make full use of the existing advantages in the mode defined by the algorithm. In the fine
adjustment. Fine adjustment is not necessarily adjustment area, a fine adjustment particle
implemented in each generation unless meeting swarm will form to calculate the fitness function
specific requirements. Directional derivative is of each fine adjustment particle. The particle
intended to be used here as the determinant with the optimal adjustment is selected to
benchmark. Determination methods are compare with Yg and replace the optimal value
introduced as follows:
of updated particles and becomes a new gbest
Given Y the function of X , if its fitness excels Yg .
X [ x1 , x2 , , xn ] , the directional derivative of
Y at g direction is defined as: 5. Algorithm improvement and VRP
Y solving
Dg Y (12)
|| X || 5.1 Model improvement
In a specific generation j , the target Particle swarm algorithm is a solution
[9]
function fitness Yg corresponding to the overall algorithm for continuous space, whereas VRP
involves integer planning, thus the model should
optimal value of the population shows the
be improved to construct particles in the
position of gbest in the design space of j algorithm so as to solve the constraint issue.
generation that can be expressed as: Letting X v of particle denotes the number of
gbest j x1j ex1 x2j ex2 (13)
vehicle serving each task point, X r the
Supposing the population going through p delivery order of this node. Particle fitness
iterations, gbest arrives at the new position, function is the minimal cost value satisfying the
then the displacement vector of these two constraints.
positions are: The key of this paper lies in finding a
proper expression with which the particles
g ( x1j p x1j )u ( x2j p x2 )v (14)
correspond to the model. For this, the particle
The difference of the fitness of these two
objective functions is swarm optimized VRP issue is constructed as a
space with (l m 1) dimensions
Y Ygj Ygj p (15)
corresponding to l tasks. Numbering of clients
Using Equation A, the directional derivative
of function Yg in j p th generation is: is represented by natural number, i denotes the
i th clients and 0 denotes the delivery center. For
Y the VRP with l clients and m vehicles,
Dg Y (16)
|| g || m 1 of 0 are inserted into the client series
After calculation, if Dg Y acquired is which will be then divided into m sections,
larger, it means the population is performing each of which denotes the route of vehicles.
global searching and in a variable situation. Or
otherwise the distribution of population particles Each particle corresponds to a
are gathering in the area where the current l m 1 -dimensional vector."
population locates for regional searching, while
the movement of population is also slow 5.2 Algorithm procedure
relatively. In this case, the algorithm determines The VRP solution by using the
whether to initiate fine adjustment mechanism algorithm presented here consists of the
by using Dg Y . following procedures:
Step 1: given the proper proportion of random
particle number q , execute the determined
4.2 Operation of fine adjustment
The core concept of fine adjustment is to values p of fine adjustment operation and
f
take gbest as the center. More specifically, it Dcr of directional derivative as well as the
138 | P a g e
4. Shenglong YU, Xiaofei YANG, Yuming BO, Zhimin CHEN, Jie ZHANG / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.136-141
particle number n of population. 8
(number 1, 2, ,), and the cargo volume of
each task g (unit: ton), loading (or unloading)
Step 2: When c p 1 , if the directional
time Ti (unit: hour) and the time range
derivative Dg Y of f g is less than or equal to
[ ETi ,LTi ] of each task are given in table 1.
f
Dcr , then implement step (3) and (4) for fine These tasks are to be finished by three vehicles
adjustment, or otherwise if Dg Y is more than with capacity of 8 ton respectively from parking
0. The Distance between the parking and each
f
Dcr , implement step (5). task point and the intervals between these tasks
points are given in Table 2. Supposing the
Step 3: Perform fine adjustment on gbesti , use driving time of vehicle is negatively
equation (17) to generate fine-adjustment proportional to distance, and the average driving
particles that are randomly generated and evenly speed of each vehicle is 50kh/h, than the driving
distributed in the fine-adjustment area centered time from i to j is tij d ij / 50 . Taking the
by gbesti .
distance between each point as the expense
xtd gbestd l [rand () 0.5] (17) cij dij (i, j 0,1, ,8) , and the unit
Step 4: For the fine-adjustment instance punishment of exceeding time window
generated, its fitness function values Yt are is c1 c2 50 , wherein n 80 ; w 1 ;
calculated and arranged in order to select the
optimal find-adjustment instance and the c1 1.5 ;c2 1.5 ,given the maximal iteration
corresponding optimal function fitness. If there number 200, each of the three algorithm runs
is an optimal function fitness in the original 1000 times and the operation results are given in
population, then it can be replaced by the Table 3.
optimal fin-adjustment particles and fitness
function values. Table 1 Cargo volume, loading and unloading
Step 5: Calculate the fitness function value Yi in time and time window at each distribution
point
accordance with the known target function;
Task 1 2 3 4 5 6 7 8
make a comparison between the fitness Yi of numb
each particle at the current position and the er
optimal solution Pbesti , if P Pbesti , then
i
Freig 2 1, 4. 3 1. 4 2. 3
ht 5 5 5 5
new fitness function value can replace the
volum
previous optimal solution, and the new particles
e
replace the previous ones, e.g., P Pbesti ,
i ( gi )
xi xbesti , or otherwise implement step (7). Ti 1 2 1 3 2 2. 3 0.8
Step 6: Compare the optimal fitness Pbesti of 5
ETi , 1, 4, 1, 4, 3, 2, 5, 1.5,
each particle with the optimal fitness gbesti of 4 6 2 7 5. 5 8 4
LTi 5
all particles, if Pbesti gbesti , then replace the
optimal fitness of all particles with the one of
Table 2 Distance between the parking and
each particles, at the same time reserve the
each task point and the intervals between
current state of particles, e.g.,
these task points
gbesti Pbesti , xbesti xbesti . 0 1 2 3 4 5 6 7 8
Step 7: use equation (10) and (11) to update the 0 0 40 60 75 90 20 10 16 80
speed and position of particles. 0 0 0
Step 8: determine the end condition, and exit if 1 40 0 65 40 10 50 75 11 10
the condition is satisfied, or otherwise transfer to 0 0 0
Step 2 for operation until the iterations are 2 60 65 0 75 10 10 75 75 75
finished or the preset precision is satisfied. 0 0
3 75 40 75 0 10 50 90 90 15
6. Simulation experiment 0 0
4 90 10 10 10 0 10 75 75 10
Experiment 1: Experimental analysis of
0 0 0 0 0
the example in literature[10] is performed. In
5 20 50 10 50 10 0 70 90 75
this example, there are 8 transportation tasks
139 | P a g e
5. Shenglong YU, Xiaofei YANG, Yuming BO, Zhimin CHEN, Jie ZHANG / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.136-141
0 0 0 Figure 1: Optimal solution curve of two
6 10 75 75 90 75 70 0 70 10 algorithms
0 0
7 16 11 75 90 75 90 70 0 10 Experimental results show that the
0 0 0 successful rate of the algorithm presented here is
8 80 10 75 15 10 75 10 10 0 higher than that of other algorithms. While
0 0 0 0 0 enhancing the precisions, it does not show any
significant increase in average searching time.
Taking time and precision into consideration, the
Table 3 Comparison of different results algorithm here is of more practical value.
gained by different algorithm
Algorithm Search Average
success rate search time Experiment 2: inspect the situation
/(%) /s with multiple dimensions. Model and parameter
Basic PSO 72.3 0.34 are referred to literature [8]. Clients are
2
Gen etic 54.7 0.28 distributed in the interval [0,100] and divided
algorithm into high, middle and low ends based on their
Predatory 93.8 0.55
needs, e.g., U (1, 9) low ,
search
algorithm U (5,15) middle ,
FA-PSO 96.1 0.39 U (10, 20) high . Given the anticipation of
n
E[ Di ]
the cargo on the vehicle as f ,
1250 gQ
i 1
PSO
where g denotes the quantities of vehicles.
1200
FA-PSO
8 10 15
1150 E ( Di ) 11 . Letting
3
1100 f ' max{0, f 1} denotes the anticipation of
route failures. Q 11n / (1 f ) , where n
'
cost
1050
represents the number of client nodes to be
1000 served. In the experiment,
f ' 0.7 and f ' 0.9 . Taking
950
n 20,30, 40,50, 60 respectively, the
900 improvement rate is compared with greedy
algorithm, The simulation result is shown in the
850 following table:
0 20 40 60 80 100 120 140 160 180 200
iteration number
Table 2: Comparison of VRP solution performance by different algorithm
parameters Improvement rate /% Time/s
'
(n, Q, f ) PSO ACO FA-PSO CPSO PSO ACO FA-PSO CPSO
(20,129,0.7) 4.21 4.46 4.81 2.04 1.83 1.82
(20,116,0.9) 4.47 4.72 5.20 1.63 1.51 1.54
(30,194,0.7) 4.13 4.37 4.76 6.52 6.04 6.08
(30,174,0.9) 4.61 4.83 5.28 5.96 5.42 5.41
(40,259,0.7) 5.07 5.38 5.85 16.27 15.11 14.88
(40,232,0.9) 4.68 4.98 5.36 12.76 11.80 11.52
(50,324,0.7) 5.50 5.80 6.43 68.25 62.39 62.33
(50,289,0.9) 5.18 5.52 6.01 62.20 55.94 56.05
(60,388,0.7) 4.76 5.02 5.29 114.64 103.87 104.92
(60,347,0.9) 4.68 4.94 5.14 105.53 95.27 95.98
Average 4.72 5.03 5.40
Seeing from the simulation result, to
140 | P a g e
6. Shenglong YU, Xiaofei YANG, Yuming BO, Zhimin CHEN, Jie ZHANG / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.136-141
solve VRP by FA-PSO achieves better effect 507-519.
than ACO and PSO with an average [4] Ying Li, Bendu Bai, Yanning Zhang.
improvement rate of 5.40% by contrast with the Improved particle swarm optimization
primitive solution (greedy algorithm solution). algorithm for fuzzy multi-class SVM.
Besides, regarding the operation time, the Journal of Systems Engineering and
improved algorithm requires the least operation Electronics.2010,21(3): 509-513.
time, showing that the convergence of the [5] Z. Fang, G. F. Tong, X. H. Xu.
algorithm here has been improved in solving
VRP. Particle swarm optimized particle
filter. Control and Decision, 2007.
7. Conclusion
273-277.
Through analysis of the shortcomings
of PSO, a fine adjustment-based algorithm [6] ZHANG W , LIU Y T. Adaptive
FA-PSO, which is suitable for VRP solving, is particle swarm optimization for
proposed here. This algorithm enhances the
effectiveness of initial samples and at the same reactive power and voltage control in
time solve PSO's problem of being easily power systems. Lecture Note in
trapped into local optimization, thereby
improving the global searching ability. The Computer Science , 2005 , 449~452.
experimental result of VRP model indicates that
[7] Bergh F van den, Engelbrecht A P. A
the algorithm presented here features better
speed and precision than the previous algorithm Study of Particle Swarm Optimization
and thus is of good application value in solving
Particle Trajectories. Information
VRP.
Sciences. 2006, 176(8): 937-971.
9. Acknowledgement [8] Halil Karahan. Determining
This paper was supported by the National rainfall-intensity-duration-frequency
Nature Science Foundation of China (No. relationship using Particle Swarm
61104196), the National defense pre-research Optimization. KSCE Journal of Civil
fund of China (No. 40405020201), the Engineering.2012,16(4):667-675.
Specialized Research Fund for the Doctoral [9] Xudong Guo,Cheng Wang, Rongguo
Program of Higher Education of China (No. Yan. An electromagnetic localization
200802881017), the Special Plan of method for medical micro-devices
Independent research of Nanjing University of based on adaptive particle swarm
Science and Technology of China (No. optimization with neighborhood
2010ZYTS051). search.
Measurement.2011,44(5):852-858.
References [10] Jun LI, Yaohuang GUO. Optimization
[1] Claudia Archetti;Dominique
Feillet;Michel Gendreau,etl. of logistics delivery vehicle
Complexity of the VRP and SDVRP.
scheduling theory and methods.2001
Transportation Research Part C:
Emerging Technologies. 2011,19(5):
741-750.
[2] Eksioglu B, Vural A V, Reisman A. The
Vehicle Routing Problem:A Taxonomic
Review. Computers & Industrial
Engineering, 2009, 57(4): 1472-1483.
[3] Zagrafos K G, Androutsopulos K N. A
Heuristic Algorithm for Solving
Hazardous Materials Distribution
Problems. European Journal of
Operational Research, 2004, 152(2):
141 | P a g e