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

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 ' Post-graduate Program in Electronics Engineering, State University of Rio de Janeiro University, Rio de Janeiro, RJ, Brazil

Abstract: Global optimisation techniques aim to find the best solution of a given a problem. In general, these problems are complex, nonlinear and intractable. On the other hand, there is a plethora of optimisation techniques. Swarm intelligence is based on models inspired by swarming behaviours. In the first part of this survey, we propose an inspiration-wise taxonomy of such metaheuristics and reviewed those that are inspired by physical system's properties. In the second part, we surveyed techniques that are inspired by fictional metaphors, flocking and schooling behaviours. In this third part, we concentrate on bioinspired methods that are guided by swarming, herding and proliferating behaviours. Overall, we point out the common inconvenience of using many of these metaheuristics, which is related to the setting required parameters. There are some proposed techniques that reduce the required parameters to a minimum. Moreover, dynamic adjustment of these parameters is usually exploited.

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

DOI: 10.1504/IJBIC.2021.116578

International Journal of Bio-Inspired Computation, 2021 Vol.17 No.4, pp.199 - 214

Received: 31 May 2020
Accepted: 24 Aug 2020
Published online: 28 Jul 2021 *

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