Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: a survey - part II Online publication date: Tue, 12-Jan-2021
by Nadia Nedjah; Luiza De Macedo Mourelle; Reinaldo Gomes Morais
International Journal of Bio-Inspired Computation (IJBIC), Vol. 16, No. 4, 2020
Abstract: Meta-heuristics are algorithmic frameworks adaptable to complex optimisation problems. Swarm intelligence represents intelligent models inspired by existing social systems, based on interaction and organisation between agents to perform tasks. In the literature, there are many interesting swarm-based meta-heuristics. In the first part of this survey, we proposed a novel extensible taxonomy that is based on the inspiration behind the search strategy of the technique. As a whole, this work provides a survey of swarm-based meta-heuristic, which are commonly employed to solve complex continuous optimisations, aiming at building an inspiration-based taxonomy for such search strategies. In this second part of the survey, we propose a review of existing swarm-oriented search strategies, categorised according to fictional metaphors' inspiration as well as flocking and schooling inspirations, considered to be bio-inspired. For each of the included techniques, we explain the essence of the used search strategy.
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