Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

• Published in: IEEE transactions on evolutionary computation Vol. 21; no. 4; pp. 644 - 660
• Main Authors: Sun, Chaoli, Jin, Yaochu, Cheng, Ran, Ding, Jinliang, Zeng, Jianchao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Computational modeling; Computationally expensive problems; Convergence; Estimation; Fitness; fitness estimation strategy (FES); Heuristic methods; Machine learning; Optimization; Optimization algorithms; Particle swarm optimization; particle swarm optimization (PSO); radial-basis-function networks; Search problems; surrogate models
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2017.2675628

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.