Particle Swarm and EDAs
Alison Jenkins, Vinika Gupta, Alexis Myrick, Mary Lenoir
TL;DR
This paper develops and tests various configurations of Particle Swarm Optimization algorithms, focusing on different topologies and update methods, to efficiently solve the Schaffer F6 function.
Contribution
It introduces multiple variations of PSO with different topologies and update schemes, demonstrating their effectiveness on a benchmark function.
Findings
PSO variants solve the Schaffer F6 function in fewer than 4000 evaluations
Different topology and update combinations are explored for performance comparison
The study provides insights into PSO configuration effectiveness
Abstract
The Particle Swarm Optimization (PSO) algorithm is developed for solving the Schaffer F6 function in fewer than 4000 function evaluations on a total of 30 runs. Four variations of the Full Model of Particle Swarm Optimization (PSO) algorithms are presented which consist of combinations of Ring and Star topologies with Synchronous and Asynchronous updates. The Full Model with combinations of Ring and Star topologies in combination with Synchronous and Asynchronous Particle Updates is explored.
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Fuzzy Logic and Control Systems · Neural Networks and Applications