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

TLDR

Surrogate models effectively aid metaheuristics for expensive optimization, yet their success has only been confirmed on low‑dimensional problems. The paper proposes a surrogate‑assisted cooperative swarm optimization algorithm that combines surrogate‑assisted PSO and surrogate‑assisted SL‑PSO to jointly search for the global optimum. The two algorithms cooperate by sharing promising real‑fitness solutions, with SL‑PSO driving exploration and PSO performing local search. Experiments on twelve high‑dimensional benchmark problems (50‑D and 100‑D) show the algorithm achieves high‑quality solutions within a limited computational budget.

Abstract

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.