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Swallow swarm optimization algorithm: a new method to optimization

Authors :
Mehdi Neshat
Ghodrat Sepidnam
Mehdi Sargolzaei
Neshat, Mehdi
Sepidnam, Ghodrat
Sargolzaei, Mehdi
Source :
Neural Computing and Applications. 23:429-454
Publication Year :
2012
Publisher :
Springer Science and Business Media LLC, 2012.

Abstract

This paper presents an exposition of a new method of swarm intelligence-based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO. Refereed/Peer-reviewed

Details

ISSN :
14333058 and 09410643
Volume :
23
Database :
OpenAIRE
Journal :
Neural Computing and Applications
Accession number :
edsair.doi.dedup.....5a46c2ed40ba1cbb9eece8fa419f4a46
Full Text :
https://doi.org/10.1007/s00521-012-0939-9