Title: Swarm intelligence-based optimisation algorithms: an overview and future research issues

Authors: Jinqiang Hu; Husheng Wu; Bin Zhong; Renbin Xiao

Addresses: School of Equipment Management and Support, Armed Police Force Engineering University, Xi'an 710086, China ' School of Equipment Management and Support, Armed Police Force Engineering University, Xi'an 710086, China ' School of Equipment Management and Support, Armed Police Force Engineering University, Xi'an 710086, China ' School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract: Swarm intelligence-based optimisation algorithms, inspired by the collective intelligent behaviours of biology groups, have been widely recognised as efficient optimisers for many complex problems, e.g., dynamic optimisation problems, large-scale optimisation problems and many-objective optimisation problems. Swarm intelligence-based algorithms are the generic concepts to represent a range of metaheuristics with population-based iterative process, guided random search and parallel processing. This paper conducts an in-depth analysis of universality and difference of existing swarm intelligence-based algorithms. It also provides a systematical survey of some well-known algorithms. In addition, the expected research issues such as theoretical analysis, hybridisation strategy and complex problems optimisation are discussed thoroughly to inspire future study and more extensive applications.

Keywords: swarm intelligence; optimisation algorithm; universality; theoretical analysis; hybridisation strategy; complex optimisation problems.

DOI: 10.1504/IJAAC.2020.110077

International Journal of Automation and Control, 2020 Vol.14 No.5/6, pp.656 - 693

Received: 18 Jan 2019
Accepted: 30 Jul 2019

Published online: 05 Oct 2020 *

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