Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: a survey - part III Online publication date: Wed, 28-Jul-2021
by Nadia Nedjah; Luiza De Macedo Mourelle; Reinaldo Gomes Morais
International Journal of Bio-Inspired Computation (IJBIC), Vol. 17, No. 4, 2021
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.
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