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Swallow swarm optimization algorithm: a new method to optimization

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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.

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  1. Department of Computer Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran

    Mehdi Neshat, Ghodrat Sepidnam & Mehdi Sargolzaei

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  1. Mehdi Neshat
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  2. Ghodrat Sepidnam
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Neshat, M., Sepidnam, G. & Sargolzaei, M. Swallow swarm optimization algorithm: a new method to optimization. Neural Comput & Applic 23, 429–454 (2013). https://doi.org/10.1007/s00521-012-0939-9

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