Steady state particle swarm.

Carlos M Fernandes,Juan-Julián Merelo,Nuno Fachada,Agostinho C Rosa

doi:10.7717/peerj-cs.202

Carlos M Fernandes, Juan-Julián Merelo + Show 2 more

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https://doi.org/10.7717/peerj-cs.202

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Abstract

This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.

Highlights

Particle swarm optimization (PSO) is a social intelligence model for optimization and learning (Kennedy & Eberhart, 1995) that uses a set of position vectors to represent candidate solutions to a specific problem
steady state PSO (SS-PSO) algorithm Steady state PSO was inspired by a similarity between PSO and the Bak–Sneppen model: both are population models in which the individuals are structured by a network and evolve toward better fitness values
The most important conclusions here is that SS-PSO does not seem to be more sensitive to the parameters than S-PSO, displaying similar patterns when varying v, c1 and c2 and m, and that the performance enhancement brought by SS-PSO is observed on a reasonably wide range of parameter values

Summary

Introduction

Particle swarm optimization (PSO) is a social intelligence model for optimization and learning (Kennedy & Eberhart, 1995) that uses a set of position vectors (or particles) to represent candidate solutions to a specific problem. Every particle is evaluated by computing its fitness, after its speed and position are updated according to local and global information about the search. The particles move through the fitness landscape of the problem, following a simple set of equations that define the velocity (Eq (1)) and position (Eq (2)) of each particle in each time step and drive them heuristically toward optimal regions of a D-dimensional search space. The difference to the original PSO is the introduction of the inertia weight parameter v in order to help (together with c1 and c2) fine-tuning the balance between local and global search. All PSO implementations in this paper use inertia weight

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