Multi-objective enhanced imperialistic competitive method for multi-criteria engineering issues
by Mohamed Nejlaoui
International Journal of Computer Applications in Technology (IJCAT), Vol. 69, No. 4, 2022

Abstract: Optimising multi-criteria engineering issues using a multi-objective evolutionary algorithm has received a lot of attention in recent years. In this work, a multi-objective enhanced version of the Imperialist Competitive Algorithm (ICA), named MOEICA, was developed and investigated to handle multi-criteria constrained engineering problems. A new strategy for colonies' progress towards their imperialists, which is called enhanced assimilation, is implemented to ameliorate the efficiency of the algorithm to achieve the global optima. Moreover, in contrast to ICA, the proposed algorithm integrates the Pareto dominance strategy to store the Pareto optimal solutions of multiple conflicting functions. Two performance metrics are used to evaluate the performance of the proposed algorithm: (a) convergence to the true Pareto-optimal set and (b) diversities of optimal solutions. The obtained results show that for both benchmark functions and multi-objective engineering issues, the MOEICA outperforms other common techniques in terms of convergence characteristics and global search capability.

Online publication date: Tue, 07-Mar-2023

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