A coevolutionary quantum krill herd algorithm for solving multi-objective optimisation problems Online publication date: Thu, 07-Jan-2021
by Zhe Liu; Shurong Li
International Journal of Modelling, Identification and Control (IJMIC), Vol. 34, No. 4, 2020
Abstract: Multi-objective optimisation (MOO) has always been a challenging problem that received considerable attention in practical engineering applications due to the multicriteria objectives. This paper presents a coevolutionary quantum krill herd algorithm (CQKH) as a novel numerical method for solving MOO. The CQKH is a quantum-inspired evolutionary algorithm which improves the krill herd algorithm (KH) based on quantum representation and quantum rotation gate. As a result, CQKH has a stronger robustness and the capability of finding the optimal or near optimal solution faster by fewer individuals. In addition, the CQKH adopts a coevolutionary technique named multiple populations for multiple objectives (MPMO) to obtain the whole Pareto optimal front. The computation results of CQKH on numerical tests with various characteristics demonstrate its effectiveness and superiority compared to some state-of-the- art algorithms.
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