Abstract
The optimization problems are taking place at all times in actual lives. They are divided into single objective problems and multiobjective problems. Single objective optimization has only one objective function, while multiobjective optimization has multiple objective functions that generate the Pareto set. Therefore, to solve multiobjective problems is a challenging task. A multiobjective particle swarm optimization, which combined cosine distance measurement mechanism and novel game strategy, has been proposed in this article. The cosine distance measurement mechanism was adopted to update Pareto optimal set in the external archive. At the same time, the candidate set was established so that Pareto optimal set deleted from the external archive could be effectively replaced, which helped to maintain the size of the external archive and improved the convergence and diversity of the swarm. In order to strengthen the selection pressure of leader, this article combined with the game update mechanism, and a global leader selection strategy that integrates the game strategy including the cosine distance mechanism was proposed. In addition, mutation was used to maintain the diversity of the swarm and prevent the swarm from prematurely converging to the true Pareto front. The performance of the proposed competitive multiobjective particle swarm optimizer was verified by benchmark comparisons with several state-of-the-art multiobjective optimizer, including seven multiobjective particle swarm optimization algorithms and seven multiobjective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of optimization quality.
Highlights
In the field of engineering, aviation scheduling, optimal control, and others, most of the optimization problems are multiobjective optimization problems (MOPs) [1]
It is crucial to the performance of MOPSOs, because particle swarm optimization (PSO)-based multiobjective optimizations are very likely to be trapped into the local optimum of MOPs due to their fast convergence
The details of our proposed GCDMOPSO are introduced. e algorithm generates a new population from all individuals initialized randomly. e particles of this population will generate many levels according to their dominance relationship. e first-level individuals generated by the nondominated relationship flow into the candidate set, and a new external file is further created. en, based on the grid technology and the cosine distance strategy, the individuals introduced in the candidate set are screened to dynamically maintain the external archive
Summary
In the field of engineering, aviation scheduling, optimal control, and others, most of the optimization problems are multiobjective optimization problems (MOPs) [1]. PSO derived from the simulation of complex adaptive systems which was an evolutionary computation method based on swarm intelligence was proposed by Kennedy and Eberhart in 1995 It was developed inspired by the social behavior of a swarm of animals like birds. It is crucial to the performance of MOPSOs, because PSO-based multiobjective optimizations are very likely to be trapped into the local optimum (or one of many optima) of MOPs due to their fast convergence. A novel multiobjective particle swarm optimization based on cosine distance mechanism and game strategy was proposed, which was called GCDMOPSO. Based on the recently developed competitive group optimizer and combined game mechanism, this article proposed a novel global leader selection strategy based on the game mechanism.
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