A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems

Abstract

Abstract Many multi-parent crossovers have been proposed to solve specific combinatorial optimization problem and not applicable to solve other problems (i.e. cannot produce feasible solution). Only multi-parent partially mapped crossover (MPPMX) and adjacency-based crossover (ABC) have been proposed to work over different combinatorial problems. However, both MPPMX and ABC suffered from a very high computational time or poor performance. Therefore, this work proposes a novel multi-parent order crossover (MPOX) for solving several combinatorial optimization problems with reasonable amount of time. The MPOX extends the two-parent order crossover by modifying the recombination operator to recombine more than two parents and generates a new offspring. MPOX at first selects the crossover points and divides the parents into n substrings based on these points (where n is the number of parents). Then, MPOX copies a predefined number of elements from each parent into the offspring based on their order while checking the feasibility of the offspring. The performance of MPOX is tested on the traveling salesman problems and berth allocation problems, which are widely studied in the literature. Experimental results demonstrated that the MPOX significantly improves the OX in both problem domains and outperforms both ABC and MPPMX over the travelling salesman problem and the berth allocation problem with less computational time. These results indicate the effectiveness of MPOX over OX, ABC and MPPMX, and its capability for solving both problems.