What is Particle Swarm Optimization

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A type of bio-inspired optimization algorithm inspired by how the fish school and birds fly. Learn more in: Metaheuristic Approaches for Extrusion Manufacturing Process: Utilization of Flower Pollination Algorithm and Particle Swarm Optimization

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A popular natured inspired metaheuristic technique modeled upon the actions of animals. Learn more in: Melanoma Identification Using MLP With Parameter Selected by Metaheuristic Algorithms

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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. Learn more in: Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer

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A natural-based technique which relays on a certain insight concerning on persons actions and cognitions. Learn more in: Blood Pressure Estimation with Considering of Stroke Volume Effect

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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 Learn more in: 2D-PAGE Analysis Using Evolutionary Computation

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Copying the behaviour of animal swarms to find optima. Learn more in: Optimization of Tuned Mass Dampers to Improve the Earthquake Resistance of High Buildings

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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. Learn more in: Swarm Intelligence-Based Optimization for PHEV Charging Stations

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Computational method that optimizes a problem by iteratively trying to improve a solution with regard to a given measure of quality. Learn more in: A Survey of Tasks Scheduling Algorithms in Distributed Computing Systems

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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. Learn more in: Quantum Inspired Swarm Optimization for Multi-Level Image Segmentation Using BDSONN Architecture

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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. Learn more in: Swarm Intelligence in Text Document Clustering

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It is an optimization paradigm inspired by the movement of swarm in nature. Learn more in: Evolutionary Computing: Principles and Applications to Portfolio Optimization

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It is an optimization method that iteratively tries to improve solution based on certain measures. Learn more in: Heart Disease Diagnosis: A Machine Learning Approach

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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). Learn more in: Transportation Network Optimization

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Optimization technique inspired by the exploratory behavior of animal swarms/flocks/herds in search of food. Learn more in: Particle Swarm Optimization and Image Analysis

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A population-based optimization algorithm inspired by the swarm movements of flocks (e.g., fish, birds). Learn more in: Imprecise Solutions of Ordinary Differential Equations for Boundary Value Problems Using Metaheuristic Algorithms

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It is a population-based evolutionary computation technique that works according to the principles of bird flocking, fish schooling. Learn more in: Realizing the Need for Intelligent Optimization Tool

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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. Learn more in: Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain

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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. Learn more in: Melanocytic Lesions Screening through Particle Swarm Optimization

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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). Learn more in: Optimal Placement and Sizing of Distributed Generation in Distribution System Using Modified Particle Swarm Optimization Algorithm: Swarm-Intelligence-Based Distributed Generation

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PSO is a population based self adaptive stochastic optimization technique inspired by social behavior such as bird flocking or fish schooling. Learn more in: Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection

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A computational method that optimizes problem within the defined searching space by using the moment and intelligence of swarms. Learn more in: Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters

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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. Learn more in: An Evolutionary Approach for Load Balancing in Cloud Computing

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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. Learn more in: An Improved Particle Swarm Optimization for Indoor Positioning

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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. Learn more in: Competency Mapping in Academic Environment: A Swarm Intelligence Approach

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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. Learn more in: Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

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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. Learn more in: Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

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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. Learn more in: Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art