Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: a survey - part I
by Nadia Nedjah; Luiza De Macedo Mourelle; Reinaldo Gomes Morais
International Journal of Bio-Inspired Computation (IJBIC), Vol. 15, No. 4, 2020

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.

Online publication date: Mon, 20-Jul-2020

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