A Hybrid Multiobjective Particle Swarm Optimization Algorithm Based on R2 Indicator

When dealing with complex multiobjective problems, particle swarm optimization algorithm is easy to fall into local optimum and lead to uneven distribution. Therefore, this paper presents a hybrid multiobjective particle swarm optimization algorithm based on R2 indicator (R2HMOPSO) for solving multi...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.14710-14721
Main Authors: Wei, Li-Xin, Li, Xin, Fan, Rui, Sun, Hao, Hu, Zi-Yu
Format: Article
Language:English
Subjects:
Algorithms
Approximation algorithms
Convergence
Crossovers
decomposition method
Multiobjective optimization problem
Multiple objective analysis
Mutation
Optimization algorithms
Pareto optimization
particle swarm algorithm
Particle swarm optimization
Polynomials
R2 indicator
Sociology
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Summary:When dealing with complex multiobjective problems, particle swarm optimization algorithm is easy to fall into local optimum and lead to uneven distribution. Therefore, this paper presents a hybrid multiobjective particle swarm optimization algorithm based on R2 indicator (R2HMOPSO) for solving multiobjective optimization problem. The proposed algorithm uses the sigmoid function mapping method to adjust the inertia weight and learning factors in order to tradeoffs the exploration and exploitation process effectively. In addition, simulation binary crossover operator is designed to reinitialize the particles to improve the search capability of the algorithm and to prevent particles from falling into local optimum and premature convergence. R2 indicator is incorporated into the R2HMOPSO algorithm so as to deal with the solutions of uneven distribution on the true Pareto front. Besides, polynomial mutation is used to maintain diversity in the external archive. The improved algorithm is evaluated on standard benchmarks. By comparing it with four state-of-the-art multiobjective optimization algorithms, the simulation results show that R2HMOPSO algorithm is competitive and effective in terms of convergence and distribution.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2812701