An external archive guided Harris Hawks optimization using strengthened dominance relation for multi-objective optimization problems

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Abstract

In this paper, Multi-Objective Harris Hawks Optimization (MOHHO) is proposed based on strengthened dominance relation (SRD) to solve multi-objective optimization problems. Specifically, MOHHO uses an additional population archive to store the best non-dominated solutions generated so far by the exploration process at the aim to maintain the elitism concept. Moreover, the leader’s solutions are selected from the external archive to guide the main population of Hawks to interesting search regions. Furthermore, the strengthened dominance relation is adopted to provide a good compromise between coverage and convergence of the obtained Pareto set. The proposed algorithm is validated via five bi-objective and seven three-objective test functions, and it is compared with three well-known multi-objective meta-heuristics. The experimental results show that MOHHO algorithm outperforms its competitors by providing better convergence behaviour with more diversified solutions.

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