Abstract
Most multi-objective particle swarm optimization algorithms, which have demonstrated their good performance on various practical problems involving two or three objectives, face significant challenges in complex problems. For overcoming this challenges, a multi-objective particle swarm optimization algorithm based on enhanced selection(ESMOPSO) is proposed. In order to increase the ability of exploration and exploitation, enhanced selection strategy is designed to update personal optimal particles, and objective function weighting is used to update global optimal particle adaptively. In addition, R2 indicator is incorporated into the achievement scalarizing function to layer particles in archive, which promotes the archive update. Besides, Gaussian mutation strategy is designed to avoid particles falling into local optimum, and polynomial mutation is applied in archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental results demonstrate that ESMOPSO algorithm shows very competitive performance when dealing with complex MOPs.
Highlights
Multi-objective optimization problems(MOPs), which naturally arise in many disciplines, such as engineering application, scientific research and management problems, involve two or more conflicting objectives function to tackle simultaneously [1]
In view of the deficiency of particle swarm itself, this paper proposes a multi-objective particle swarm optimization algorithm based on the corresponding solution strategy
Inspired by decomposition approach to update the individual optimal particles, enhanced selection strategy is incorporated into ESMOPSO to accelerate the convergence speed and strengthen the ability of local exploration
Summary
Multi-objective optimization problems(MOPs), which naturally arise in many disciplines, such as engineering application, scientific research and management problems, involve two or more conflicting objectives function to tackle simultaneously [1]. The main purpose by metaheuristic algorithms for solving complex MOPs is to obtain the Pareto optimal solutions set that is regarded as good. In MOPSO/D [27], the genetic algorithm is substituted in MOEA/D framework with PSO to solve continuous MOPs, and the decomposition method is applied to update individual and global information. The existing research have achieved the good convergence and diversity It remains to be seen whether they are fully applicable when using particle swarm optimization algorithm to solve complex problems. In view of the deficiency of particle swarm itself, this paper proposes a multi-objective particle swarm optimization algorithm based on the corresponding solution strategy. (1) In order to improve the ability of local search, enhanced selection strategy is designed to update individual optimal particles, including Tchebycheff aggregation function value, favorable weight’s Tchebycheff aggregation function value and random selection.
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