Efficient multi-objective optimization through parallel surrogate-assisted local search with tabu mechanism and asynchronous option

Abstract

Multi-objective optimization is challenging for computationally expensive objectives because most optimization methods rely on a large number of function evaluations to approach the Pareto-optimal front. To this end, this article develops a novel multi-objective optimization algorithm, the Multi-Objective Parallel Local Surrogate-assisted search (MOPLS) algorithm, which combines surrogate approximation and parallel computing. In each iteration, MOPLS incorporates a tabu mechanism to determine new points for expensive evaluations via a series of independent surrogate-assisted local searches. A master–worker architecture in MOPLS allows the algorithm to conduct either synchronous or asynchronous parallel processing. On a number of benchmark problems, MOPLS outperforms recent surrogate-assisted algorithms within a limited computational budget. Empirical results from parallel experiments indicate that MOPLS can significantly improve its efficiency through parallelism, and the asynchronous MOPLS shows advantages in handling objectives with non-constant evaluation times. Finally, the better performance of MOPLS over alternatives in calibrating a complex watershed simulation model demonstrates its competitiveness in solving real-world engineering applications.