Title: Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: a survey - part I

Authors: Nadia Nedjah; Luiza De Macedo Mourelle; Reinaldo Gomes Morais

Addresses: Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro University, Rio de Janeiro, RJ, Brazil ' Department of Systems Engineering and Computation, State University of Rio de Janeiro University, Rio de Janeiro, RJ, Brazil ' State University of Rio de Janeiro University, Rio de Janeiro, RJ, 20550-000, Brazil

Abstract: In many science fields, such as engineering, administration, transportation, economics and biology, among many others, optimisation problem is a handy required tool. Global optimisation techniques aim to find the best solution in a set of feasible solutions to a problem. Currently, there are numerous optimisation techniques. In general, the problems to be optimised are complex, nonlinear and in some cases may be intractable. Meta-heuristics are general algorithmic frameworks adaptable to various optimisation problems and are generally applied to highly complex problems. Swarm intelligence represents intelligent models inspired by real-world social systems, based on interaction and organisation between simple agents to perform simple tasks. Nowadays, there are many swarm-based meta-heuristics. In this survey, we take advantage of the inspiration behind the strategies to contribute an inspiration-oriented novel taxonomy for the current state-of-the-art of swarm-oriented optimisation methods. The overall survey, which will be divided into three separate parts, provides a review of swarm-based meta-heuristic, which is commonly employed to solve complex continuous optimisations, aiming at building a inspiration-based taxonomy for such search strategies. In this part of the survey, we review meta-heuristics that are guided by some interesting human relation's characteristics and those that are inspired by physical system's properties.

Keywords: swarm intelligence; swarm-based meta-heuristics; bio-inspired computation.

DOI: 10.1504/IJBIC.2020.108597

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.4, pp.207 - 223

Received: 13 Nov 2019
Accepted: 14 Feb 2020

Published online: 15 Jul 2020 *

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