Multiple-Population Jaya Algorithm for Continuous Function Optimization

Cao, Sanqiang; Lei, Deming

doi:10.1109/iciicii.2017.45

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been proved that, compared with a single population, algorithms with multiple populations can effectively improve solution diversity for both single-objective optimization problems [5,32,33] and MOOPs [13,31,42] by dividing the population into multiple co-evolutionary/cooperative subpopulations. Nevertheless, some multi-population algorithms, such as MPEA/SG [30] and CIEMO/D [40], change the number of subpopulations during evolution, which makes it a tough challenge to determine the appropriate number of subpopulations when solving MaOPs.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Tang

Cai

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

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

show abstract