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What is Particle Swarm Optimization

Chapter 3
Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
A type of bio-inspired optimization algorithm inspired by how the fish school and birds fly.
Published in Chapter:
Metaheuristic Approaches for Extrusion Manufacturing Process: Utilization of Flower Pollination Algorithm and Particle Swarm Optimization
Pauline Ong (Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia), Desmond Daniel Vui Sheng Chin (Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia), Choon Sin Ho (Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia), and Chuan Huat Ng (Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/978-1-5225-2944-6.ch003
Abstract
Optimization, basically, is a method used to find solutions for a particular problem without neglecting the existing boundaries or limitations. Flower Pollination Algorithm (FPA) is one of the recently developed nature inspired algorithms, based on the intriguing process of flower pollination in the world of nature. The main aim of this study is to utilize FPA in optimizing cold forward extrusion process in order to obtain optimal parameters to produce workpiece with the minimum force load. It is very important to find the most optimal parameters for an extrusion process in order to prevent waste from happening due to trial and error method in determining the optimal parameters and thus, FPA is used to replace the traditional trial and error method to optimize the cold forward extrusion process. The optimization performance of the FPA is then compared with the particle swarm optimization (PSO), in which the FPA shows comparable performance in this regard.
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More Results
Chapter 10
Melanoma Identification Using MLP With Parameter Selected by Metaheuristic Algorithms
A popular natured inspired metaheuristic technique modeled upon the actions of animals.
Published in Chapter: Melanoma Identification Using MLP With Parameter Selected by Metaheuristic Algorithms; From: Intelligent Innovations in Multimedia Data Engineering and Management
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Chapter 48
Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer
A social-based algorithm that iteratively enhances solutions locally by updating features of position and velocity of a solution (bird) to finally reach a global optimum solution.
Published in Chapter: Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer; From: Handbook of Research on Machine Learning Innovations and Trends
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Chapter 24
Blood Pressure Estimation with Considering of Stroke Volume Effect
A natural-based technique which relays on a certain insight concerning on persons actions and cognitions.
Published in Chapter: Blood Pressure Estimation with Considering of Stroke Volume Effect; From: Encyclopedia of Healthcare Information Systems
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Chapter 232
2D-PAGE Analysis Using Evolutionary Computation
Evolutionary Computation technique that basis its functioning on natural swarm behaviour like the birds. This algorithm uses a swarm of particles to explore the search space
Published in Chapter: 2D-PAGE Analysis Using Evolutionary Computation; From: Encyclopedia of Artificial Intelligence
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Chapter 18
Optimization of Tuned Mass Dampers to Improve the Earthquake Resistance of High Buildings
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Chapter 12
Swarm Intelligence-Based Optimization for PHEV Charging Stations
Particle Swarm Optimization (PSO) algorithm was introduced by Kennedy and Eberhart in 1995, which is a heuristic global optimization method and a member of swarm intelligence family. PSO is a computational intelligence-based technique that is not largely affected by the size and nonlinearity of the problem, and can converge to the optimal solution in many problems where most analytical methods fail to converge.
Published in Chapter: Swarm Intelligence-Based Optimization for PHEV Charging Stations; From: Handbook of Research on Swarm Intelligence in Engineering
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Chapter 18
A Survey of Tasks Scheduling Algorithms in Distributed Computing Systems
Computational method that optimizes a problem by iteratively trying to improve a solution with regard to a given measure of quality.
Published in Chapter: A Survey of Tasks Scheduling Algorithms in Distributed Computing Systems; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 9
Quantum Inspired Swarm Optimization for Multi-Level Image Segmentation Using BDSONN Architecture
It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Published in Chapter: Quantum Inspired Swarm Optimization for Multi-Level Image Segmentation Using BDSONN Architecture; From: Handbook of Research on Swarm Intelligence in Engineering
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Chapter 10
Swarm Intelligence in Text Document Clustering
The Particle Swarm Optimization (PSO) is a population based stochastic optimization technique that can be used to find an optimal, or near optimal, solution to a numerical and qualitative problem. PSO was originally developed by Eberhart and Kennedy in 1995, inspired by the social behavior of flocking birds or a school of fish.
Published in Chapter: Swarm Intelligence in Text Document Clustering; From: Handbook of Research on Text and Web Mining Technologies
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Chapter 12
Evolutionary Computing: Principles and Applications to Portfolio Optimization
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Chapter 8
Heart Disease Diagnosis: A Machine Learning Approach
It is an optimization method that iteratively tries to improve solution based on certain measures.
Published in Chapter: Heart Disease Diagnosis: A Machine Learning Approach; From: Advanced Classification Techniques for Healthcare Analysis
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Chapter 229
Transportation Network Optimization
A search strategy that starts from an initial set of candidate solutions (the particles) and try to improve them by looking at their neighbors in the solution spaces. A solution, or particle is moved according to a local criteria (i.e. the particle moves to the local best), in combination with a criteria based on the situation of all other particles (i.e. the particle moves to the best known position of the other particles).
Published in Chapter: Transportation Network Optimization; From: Encyclopedia of Business Analytics and Optimization
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Chapter 191
Particle Swarm Optimization and Image Analysis
Optimization technique inspired by the exploratory behavior of animal swarms/flocks/herds in search of food.
Published in Chapter: Particle Swarm Optimization and Image Analysis; From: Encyclopedia of Artificial Intelligence
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Chapter 15
Imprecise Solutions of Ordinary Differential Equations for Boundary Value Problems Using Metaheuristic Algorithms
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Chapter 1
Realizing the Need for Intelligent Optimization Tool
It is a population-based evolutionary computation technique that works according to the principles of bird flocking, fish schooling.
Published in Chapter: Realizing the Need for Intelligent Optimization Tool; From: Handbook of Research on Natural Computing for Optimization Problems
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Chapter 12
Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain
Particle swarm optimization (PSO) is a stochastic optimization approach, modeled on the social behaviour of bird flocks. PSO is a population-based search procedure where the individuals, referred to as particles, are grouped into a swarm. Applications of PSO include function approximation, clustering, optimization of mechanical structures, and solving systems of equations.
Published in Chapter: Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 12
Melanocytic Lesions Screening through Particle Swarm Optimization
A computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The population of particles are moving in the search space, according with an heuristic based on each particle’s position and velocity. Each particle’s movement is influenced by its local best known position and by the best position of the swarm, which are updated as better positions are found by other particles.
Published in Chapter: Melanocytic Lesions Screening through Particle Swarm Optimization; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 21
Optimal Placement and Sizing of Distributed Generation in Distribution System Using Modified Particle Swarm Optimization Algorithm: Swarm-Intelligence-Based Distributed Generation
PSO is an attractive stochastic optimization technique inspired by social behavior of living being such as birds flocking or fish schooling, introduced by Dr. Kennedy and Dr. Eberhart in 1995. The PSO algorithm is that population called swarm and consists of individuals called particles. The swarm is randomly generated in which particle changes their position (states) with time. In PSO each particle is moving in a multidimensional search space to adjust its position according to its own experience with the best solution it has achieved so for called pbest and the position tracked by its neighboring particle called lbest . When a particle takes all the swarm as its neighbors the best value (global best) will be called as gbest . The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO).
Published in Chapter: Optimal Placement and Sizing of Distributed Generation in Distribution System Using Modified Particle Swarm Optimization Algorithm: Swarm-Intelligence-Based Distributed Generation; From: Handbook of Research on Emergent Applications of Optimization Algorithms
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Chapter 17
Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection
PSO is a population based self adaptive stochastic optimization technique inspired by social behavior such as bird flocking or fish schooling.
Published in Chapter: Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 17
Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters
A computational method that optimizes problem within the defined searching space by using the moment and intelligence of swarms.
Published in Chapter: Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 20
An Evolutionary Approach for Load Balancing in Cloud Computing
Kennedy et al originally proposed the PSO algorithm for optimization. It is a population based search algorithm based on simulation behavior of birds. PSO soon become very popular for global optimizer. In PSO particles are flown through hyper dimension search space. Changes to the position of particle within the search space are based on the social psychological tendency of individuals to emulate the success of other individuals. The position of particles change according to their own experience.
Published in Chapter: An Evolutionary Approach for Load Balancing in Cloud Computing; From: Handbook of Research on Securing Cloud-Based Databases with Biometric Applications
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Chapter 34
An Improved Particle Swarm Optimization for Indoor Positioning
A swarm intelligence based algorithm to find a solution to an optimization problem in a search space, or model and predict social behavior in the presence of objectives.
Published in Chapter: An Improved Particle Swarm Optimization for Indoor Positioning; From: Handbook of Research on Mobile Multimedia, Second Edition
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Chapter 12
Competency Mapping in Academic Environment: A Swarm Intelligence Approach
is an optimization algorithm inspired by the movement aesthetics of a bird flock or a fish school trying to get closer to the food source and finally reach the target. Each individual in the flock represents a particle with a particular velocity, where each of them represents a feasible solution in the solution space.
Published in Chapter: Competency Mapping in Academic Environment: A Swarm Intelligence Approach; From: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
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Chapter 14
Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection
Particle swarm optimization (PSO) helps to optimize a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Published in Chapter: Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 18
Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles
Particle Swarm Optimization (PSO) algorithm was introduced by Kennedy and Eberhart in 1995, which is a heuristic global optimization method and a member of swarm intelligence family. PSO is a computational intelligence-based technique that is not largely affected by the size and nonlinearity of the problem, and can converge to the optimal solution in many problems where most analytical methods fail to converge.
Published in Chapter: Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles; From: Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
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Chapter 18
Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art
It is motivated from social behavior of bird or fish, where a group of birds randomly look for food in an area by following the nearest bird to the food.
Published in Chapter: Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art; From: Handbook of Research on Computational Intelligence Applications in Bioinformatics
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