Abstract
In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this algorithm, a disturbance strategy based on boundary fluctuations is first used for the updated new particles and nondominant particles. To prevent the population from falling into a local extremum, some particles are disturbed. Then, when the external archive capacity reaches the extreme value, we use a central control strategy to update the external archive, so that the archive solution gets a good distribution. When the dominance of the current particle and the individual best particle cannot be determined, to enhance the diversity of the population, the combination method of the current particle and the individual best particle can be used to update the individual best particle. The experimental results show that CCHMOPSO is better than four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms. It is a feasible method for solving multi-objective optimization problems.
Highlights
Multi-objective optimization problems (MOPs) are based on population-based meta-heuristic optimization designs
To address the above problems, researchers have proposed many improved multi-objective particle swarm optimization algorithms (MOPSOs), such as learning samples selected based on Pareto sorting scheme [13], global marginal sorting [14], and learning samples selected based on competition mechanism strategy [15]
Lin et al [28] proposed a MOPSO where the Particle swarm optimization (PSO)-based search method searched for particles in the population with crossover [24] and mutation operation [29] updated the particles in the archive
Summary
Multi-objective optimization problems (MOPs) are based on population-based meta-heuristic optimization designs. E multi-objective algorithm based on PSO has a fast convergence speed, but it is easy to fall into the local optimum of the MOP. To address the above problems, researchers have proposed many improved multi-objective particle swarm optimization algorithms (MOPSOs), such as learning samples selected based on Pareto sorting scheme [13], global marginal sorting [14], and learning samples selected based on competition mechanism strategy [15]. We propose a multi-objective particle swarm optimization based on central control and combination methods. Erefore, this study designs a new central control method, which deletes the nondominated solution according to the distribution of the solution and the Euclidean distance from the central particle It can improve the quality of the solution in the archive and accelerate the convergence speed of the algorithm. E specific work of the study is as follows. e second section reviews the MOP, briefly introduces the PSO, and the existing MOPSO. e third section gives the details of CCHMOPSO proposed in this study. e fourth section is the research comparison and relevant discussion. e fifth section summarizes this study
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