Autonomous Groups Particles Swarm Optimization (AGPSO)

A Particle Swarm Optimization Algorithm with Autonomous Groups of Particles
Updated 22 May 2018

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A modified particle swarm optimization (PSO) algorithm called autonomous groups particles swarm optimization (AGPSO) is proposed to further alleviate the two problems of trapping in local minima and slow convergence rate in solving high-dimensional problems. The main idea of AGPSO algorithm is inspired by individuals’ diversity in bird flocking or insect swarming. In natural colonies, individuals are not basically quite similar in terms of intelligence and ability, but they all do their duties as members of a colony. Each individual’s ability can be useful in a particular situation. In this paper, a mathematical model of diverse particles groups called autonomous groups is proposed. In other words different functions with diverse slopes, curvatures, and interception points are employed to tune the social and cognitive parameters of the PSO algorithm to give particles different behaviors as in natural colonies.
This is the source codes of the paper: S. Mirjalili, A. Lewis, A. Safa Sadiq, Autonomous Particles Groups for Particle Swarm Optimization, Arabian Journal for Science and Engineering, Volume 39, Issue 6, pp 4683-4697, 2014.

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Cite As

Seyedali Mirjalili (2024). Autonomous Groups Particles Swarm Optimization (AGPSO) (, MATLAB Central File Exchange. Retrieved .

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