Research on Solving Combinatorial Optimization Problems Based on Hyper-heuristic Algorithms

Abstract

Due to the single mechanism of traditional heuristic algorithms and meta-heuristic algorithms, different algorithms for different problems or the same problem need to be customized. To solve these shortcomings, scholars have begun to study hyper-heuristic algorithms. This paper proposes a tabu search hyper-heuristic algorithm based on random selection to solve multiple combinatorial optimization problems. The algorithm model divides into high level and low level. The low level comprises meta-heuristic operators with multiple heterogeneous mechanisms and meta-heuristic operators with different parameter combinations of the same algorithm. According to the tabu search algorithm based on random selection, the high level automatically selects operators. Because the model organically integrates the tabu search algorithm and different meta-heuristic algorithms, it has a certain scalability. To verify the effect of the algorithm, two cases of combined optimization problems of CVRP and MRCPSP from the international benchmark case library for experiments. Experimental results show that the tabu search hyper-heuristic algorithm based on random selection has an excellent performance in multiple performance indicators such as target value and versatility. It can apply to different combinatorial optimization problems.