PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
IT IS ABOUT FUSION OF TWO NATURE INSPIRED OPTIMIZATION ALGORITHM(S).THE FIRST ONE IS GRAVITATIONAL SEARCH ALGORITHM(GSA) BASED ON NEWTONS UNIVERSAL LAW OF GRAVITATION AND OTHER ONE i.e; BIOGEOGRAPHY BASED OPTIMIZATION(BBO) BASED ON BIOGEOGRAPGY (THE STUDY OF SPECIES IN A PARTICULAR HABITAT).
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
A New Multi-Objective Mixed-Discrete Particle Swarm Optimization AlgorithmWeiyang Tong
A new multi-objective optimization algorithm to handle problems that are hightly constrained, highly nonlinear, and with mixed types of design variables
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
IT IS ABOUT FUSION OF TWO NATURE INSPIRED OPTIMIZATION ALGORITHM(S).THE FIRST ONE IS GRAVITATIONAL SEARCH ALGORITHM(GSA) BASED ON NEWTONS UNIVERSAL LAW OF GRAVITATION AND OTHER ONE i.e; BIOGEOGRAPHY BASED OPTIMIZATION(BBO) BASED ON BIOGEOGRAPGY (THE STUDY OF SPECIES IN A PARTICULAR HABITAT).
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
A New Multi-Objective Mixed-Discrete Particle Swarm Optimization AlgorithmWeiyang Tong
A new multi-objective optimization algorithm to handle problems that are hightly constrained, highly nonlinear, and with mixed types of design variables
A simple, widely used control method. This presentation will provide an introduction to PID controllers, including demonstrations, and practise tuning a controller for a simple system.
From the Un-Distinguished Lecture Series (http://ws.cs.ubc.ca/~udls/). The talk was given Mar. 30, 2007.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightWaqas Tariq
Software is the most expensive element of virtually all computer based systems. For complex custom systems, a large effort estimation error can make the difference between profit and loss. Cost (Effort) Overruns can be disastrous for the developer. The basic input for the effort estimation is size of project. A number of models have been proposed to construct a relation between software size and Effort; however we still have problems for effort estimation because of uncertainty existing in the input information. Accurate software effort estimation is a challenge in Industry. In this paper we are proposing three software effort estimation models by using soft computing techniques: Particle Swarm Optimization with inertia weight for tuning effort parameters. The performance of the developed models was tested by NASA software project dataset. The developed models were able to provide good estimation capabilities.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Planning Of Procurement o different goods and services
Newtonian Law Inspired Optimization Techniques Based on Gravitational Search Algorithm
1. AThesis Seminar on
Newtonian Law Inspired Optimization
Techniques Based on Gravitational Search
Algorithm
Presented by-
Rajdeep Chatterjee
M.Tech, 2009-11
School of Computer Engineering
KIIT University
Under the guidance of-
Prof. (Dr.) Madhabananda Das
Dean, School of Computer Engineering
KIIT University
3. Gravitational Search Algorithm
This algorithm is based on the Newtonian gravity: „„Every particle in
the universe attracts every other particle with a force that is directly
proportional to the product of their masses and inversely proportional to
the square of the distance between them”.
The position of the mass corresponds to a solution of the problem,
and its masses are determined using a fitness function.
9. GSA and PID Controller
Simulation Result
GSA PSO BFO
KP 0.85 0.56 0.80
KI - - -
KD 0.57 0.62 0.53
Cost 15.3026 19.1416 15.7906
Table 3
10. GSA and PID Controller
Fig. 6 Closed Loop Response
11. Mutation
Optimization algorithm often trapped into local
optima.
We cannot obtain the global optima and rather
ended up with local optimal value.
Mutation operator is used to lift the population
from local optima to not yet explored search
space.
13. Differential Evolution
It is a heuristic approach for minimizing possibly
nonlinear and non differentiable continuous space.
It was introduced by Rainer Storn and Kenneth Price in
the year 1995.
16. Proposed Algorithms
GSA–m :: Gravitational Search Algorithm with mutation
GDE :: Gravitational Differential Evolution
DE is used as mutation operator to improve the
convergence.
DE-1 :: DE/best/1/exp
DE-2 :: DE/best/2/exp
DE-3 :: DE/best/2/bin
17. Proposed GSA-m Algorithm
1. Generate initial population
2. For I: 0 to max-iteration or stop criteria is reached do
3. Evaluate the fitness for each agent
4. Update the G, best and worst of the population
5. Calculate M , F and a for each agent
6. Update velocity and position i.e updated-agent
7. If last r iterations give same result or (I mod k) == 0
8. Create Difference-Offspring from updated-agent
9. Evaluate fitness;
10. If an offspring is better than updated-agent
11. Then replace the updated-agent by offspring in the next generation;
12. End If;
13. End If;
14. End For
15. Return approximate global optima
18. Proposed GDE Algorithm
1. Generate initial population
2. For I: 0 to max-iteration or stop criteria is reached do
3. Evaluate the fitness for each agent
4. Update the G, best and worst of the population
5. Calculate M , F and a for each agent
6. Update velocity and position i.e updated-agent
7. Create Difference-Offspring from updated-agent
8. Evaluate fitness;
9. If an offspring is better than updated-agent
10. Then replace the updated-agent by offspring in the next
generation;
11. End If;
12. End For
13. Return approximate global optima
23. Pareto Optimality
A well formed Multi-objective problem, there should
not be a single solution that simultaneously minimizes
each objective to its fullest.
In each case we are looking for a solution for which
each objective has been optimized to the extent that
if we try to optimize it any further, then the other
objective(s) will suffer as a result.
Finding such a solution, and quantifying how much
better this solution is compared to other such
solutions (there will generally be many) is the goal
when setting up and solving a Multi-objective
optimization problem.
24. Types of Domination
Given two decision or solution vectors x and y,
we say that decision vector x weakly dominates (or
simply dominates) the decision vector y (denoted by x
y) if and only if fi(x) fi(y)∀ i = 1, ...,M (i.e., the
solution x is no worse than y in all objectives) and
fi(x) ≺ fi(y) for at least one i ∈ 1, 2, ...,M (i.e., the
solution x is strictly better than y in at least one
objective).
A solution x strongly dominates a solution y (denoted
by x ≺ y ), if solution x is strictly better than solution
y in all M objectives.
27. Multi-objective Gravitational Optimization
(MOGO)
Equation (8) is modified to (14)
…(8)
…(14)
Where m is the number of objectives; bestk and abestk are the
maximum and minimum fitness value among the solutions for kth
objective. Mass of an agent is the summation of the masses in all
dimensions of the objective space.
28. MOGO
1. Generate initial population and set the parameters
2. Evaluate fitness and add solutions to the archive
3. For I: 0 to max-iteration or stop criteria is reached do
4. Update the G, best and worst of the population
5. Calculate M , F and a for each agent
6. Update velocity and position
7. Evaluate fitness
8. Domination check for the new set of solutions with the
solutions in the archive
9. End For
10. Return set of non-dominated set of solutions
31. Observations
GSA is implemented to optimize gain parameters in PID
Controller.
GSA provides better gain values than other popular
algorithms – PSO and BFO.
Proposed algorithm GSA-m produces better results
than Classical GSA except F6.
Again, proposed algorithm GDE generates better results
than Classical GSA as well as GSA-m for all the test
functions.
32. Observations
Results obtained from GSA, new algorithms GSA-m and
GDE has been compared with existing popular
optimization techniques GA and PSO.
In F3, GA and in F5 PSO outperform all the three physics
inspired algorithms.
But GDE outclasses GA and PSO in all other test
functions. Also, results of GDE not far from these
popular algorithms.
Hence, our new Hybrid Algorithm GDE is very much
competitive with the GA and PSO.
33. Observations
Distribution of non-dominated points is not so uniform
in nature in all the cases.
As far as the spreads of the Pareto fronts for the
benchmark test functions are concerned, our results are
well suited except for Deb benchmark function.
Unlike in MOPSO approaches, we have no leader
selection strategy in our proposed MOGO. This in turn
has reduced the computational complexity to a great
extent as compared to MOPSO approaches.
Hence, proposed MOGO is a novel algorithm and it
serves the purpose quite well. It could lead us to a
complete new arena with very high possibilities.
34. Publications
R. Chatterjee and M. N. DAS, “Physics Inspired Optimization
Algorithms: Introducing New Hybrid Gravitational
Evolution & Gravitational Search Algorithm with
mutation”, International Symposium on Devices MEMS
Intelligence System Communication 2011, SMU, Sikkim, India, APR
2011.
https://www.researchgate.net/publication/259193474_Physics_Inspi
red_Optimization_Algorithms_Introducing_New_Hybrid_Gravitati
onal_Differential_Evolution_and_Gravitational_Search_Algorithm_
with_mutation?ev=prf_pub
35. References
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Hai Shen, Yunlong Zhu, Xiaoming Zhou, Haifenf Gho and Chuanguang
Chang. Bacterial Foraging Optimization Algorithm with Particle Swarm
Optimization Strategy for Global Numerical Optimization. In Proceeding
GEC '09 Proceedings of the rst ACM/SIGEVO Summit on Genetic and
Evolutionary Computation.
36. References
Hui Liu, Zixing Cai and Yong Wang. Hybridizing particle swarm optimization
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37. References
Liping Xie, Jianchao Zeng and Zhihua Cui. General framework of Artificial
Physics Optimization Algorithm. Nature & Biologically Inspired Computing
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