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A multiple surrogates based PSO algorithm
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DOI：10.1007/s10462-017-9601-3.png)
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Abstract
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Particle swarm optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution, which poses an obstacle for applying PSO to computationally expensive problems. In this paper, a multiple surrogates based PSO (MSPSO) framework is proposed, which consists of an inner loop optimization and an outer one. In the outer loop optimization, a PSO algorithm is used in both the optimization mode and the sampling one. In the inner loop optimization, a multiple surrogate based parallel optimization strategy is designed. Furthermore, the search history and the possible solutions from the outer loop optimization are provided for the inner one, and the result of the inner loop optimization is employed to guide the search of the outer one. To verify the performance of the proposed approach, a number of numerical experiments are conducted by using ten benchmark test functions and three time series regression modeling problems. The results indicate that the proposed framework is capable of converging to a good solution for the low-dimensional, non-convex and multimodal problems.
Keywords:
Particle swarm optimization
Experience knowledge
Surrogate model
Bayesian optimization
Parallel
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