Cooperative multi-population Harris Hawks optimization for many-objective optimization

Abstract This paper presents an efficient cooperative multi-populations swarm intelligence algorithm based on the Harris Hawks optimization (HHO) algorithm, named CMPMO-HHO, to solve multi-/many-objective problems. Specifically, this firstly proposes a novel framework with dual elite selection CMPMO/des. With four excellent strategies, namely one-to-one correspondence between objectives and subpopulations, global archive for information exchange cooperation among logistic chaotic single-dimensional perturbation strategy, mechanism fast non-dominated sorting reference point-based approach, CMPMO/des achieves considerably high performance solutions convergence diversity. Thereafter, in each subpopulation, HHO is used as single objective optimizer its impressive performance. Notably, however, proposed can work any other without modification. We comprehensively evaluated of CMPMO-HHO 34 multi-objective 19 many-objective benchmark problems extensively compared it 13 state-of-the-art multi/many-objective algorithms, three variants genetic CMPMO-GA. The results show that by taking advantages framework, promising solving