In recent days, swarm intelligent (SI) optimizationalgorithms have been proved to be a powerful framework for finding tradeoff solutions of multi-objectiveoptimization problems (MOPs). Many researchers have proposed...
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In recent days, swarm intelligent (SI) optimizationalgorithms have been proved to be a powerful framework for finding tradeoff solutions of multi-objectiveoptimization problems (MOPs). Many researchers have proposed various SI optimizationalgorithms. multi-objectiveartificialflora (MOAF) optimizationalgorithm is a recently proposed algorithm for solving MOPs. However, problems of decreased population diversity and uniformity of solutions distribution in the late evolutionary period is existed in the algorithm. Hence, this paper proposes a knee point-driven MOAF (kpMOAF) optimizationalgorithm to address the vulnerability of MOAF optimizationalgorithm. Knee points of the non-dominant solutions are taken by the proposed algorithm as criterion to guide the population evolution. Researchers have proved that select knee points equals to select a large hypervolume. Therefore, using it as criterion is an effective way to enhance the population convergence rate and maintain the diversity of solutions. In addition, adaptive neighborhood control method is introduced in the evolution process to improve the algorithm development capability. Simulation results on 10 benchmark functions demonstrate the competitiveness of kpMOAF optimizationalgorithm.
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