A surrogate-assisted multi-objective particle swarm optimization of expensive constrained combinatorial optimization problems

Abstract

Surrogate-assisted evolutionary algorithms have been commonly used in extremely expensive optimization problems. However, many existing algorithms are only significantly used in continuous and unconstrained optimization problems despite the fact that plenty of real-world problems are constrained combinatorial optimization problems. Therefore, a random forest assisted adaptive multi-objective particle swarm optimization (RFMOPSO) algorithm is proposed in this paper to address this challenge. Firstly, the multi-objective particle swarm optimization (MOPSO) combines with random forest model to accelerate the overall search speed of the algorithm. Secondly, an adaptive stochastic ranking strategy is performed to balance better objectives and feasible solutions. Finally, a novel rule is developed to adaptively update the states of particles. In order to validate the proposed algorithm, it is tested by ten multi-objective knapsack benchmark problems whose discrete variables vary from 10 to 100. Experimental results demonstrate that the proposed algorithm is promising for optimizing the constrained combinatorial optimization problem.