期刊
APPLIED INTELLIGENCE
卷 51, 期 2, 页码 775-787出版社
SPRINGER
DOI: 10.1007/s10489-020-01836-8
关键词
Multi-objective optimization; Pareto dominance; State transition; Simulated annealing algorithm
资金
- National Natural Science Foundation of China [21606159]
- Key Research and Development Program of Shanxi Province [201803D121039]
In this article, a novel multi-objective optimization algorithm MOSTASA is proposed, which combines state-transition operators and the concept of Pareto dominance to generate and store Pareto optimal solutions, achieving a uniform distribution of solutions. Simulation experiments show that MOSTASA outperforms other algorithms in terms of efficiency and reliability.
In this article, a novel multi-objective optimization algorithm based on a state-transition simulated annealing algorithm (MOSTASA) is proposed, in which four state-transition operators for generating candidate solutions and the Pareto optimal solution is obtained by combining it with the concept of Pareto dominance and then storing it in a Pareto archive. To ensure the uniform distribution of the Pareto optimal solution, we define a crowded comparison operator to update the Pareto archive. Simulation experiments were conducted on several standard constrained and unconstrained multi-objective problems, in which convergence and spacing metrics were used to assess the performance of the MOSTASA. The test results manifest that the MOSTASA can converge to the true Pareto-optimal front, and the solution distribution is uniform. Compared to the performance of other multi-objective optimization algorithms, the proposed algorithm is more efficient and reliable.
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