Abstract
In order to solve the shortcomings of particle swarm optimization (PSO) in solving multiobjective optimization problems, an improved multiobjective particle swarm optimization (IMOPSO) algorithm is proposed. In this study, the competitive strategy was introduced into the construction process of Pareto external archives to speed up the search process of nondominated solutions, thereby increasing the speed of the establishment of Pareto external archives. In addition, the descending order of crowding distance method is used to limit the size of external archives and dynamically adjust particle parameters; in order to solve the problem of insufficient population diversity in the later stage of algorithm iteration, time-varying Gaussian mutation strategy is used to mutate the particles in external archives to improve diversity. The simulation experiment results show that the improved algorithm has better convergence and stability than the other compared algorithms.
Highlights
In many engineering problems, the problems are composed of multiple goals that influence and conflict with each other
Sun et al [12] proposed a novel multiobjective particle swarm optimization algorithm based on Gaussian mutation and an improved learning strategy. is method uses Gaussian mutation strategy to improve the consistency of external archives and current population
Different from other study results, in this study, we proposed a multiobjective particle swarm optimization algorithm based on competition mechanism strategy and Gaussian mutation to balance the exploration and exploitation of the algorithm and enable the algorithm to search the optimal location and converge to the Pareto front more quickly
Summary
The problems are composed of multiple goals that influence and conflict with each other. PDJI-MOPSO maintains diversity of newly found nondominated solutions via proportional distribution and combines advantages of wide-ranged exploration and Complexity extensive exploitations of PSO in the external repository with the jump improved operation to enhance the solution searching abilities of particles. Sun et al [12] proposed a novel multiobjective particle swarm optimization algorithm based on Gaussian mutation and an improved learning strategy. Zhang [14] proposed an improved MOPSO algorithm with a mutation operator that can maintain the diversity of optimal solutions and has good convergence. Different from other study results, in this study, we proposed a multiobjective particle swarm optimization algorithm based on competition mechanism strategy and Gaussian mutation to balance the exploration and exploitation of the algorithm and enable the algorithm to search the optimal location and converge to the Pareto front more quickly.
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