Solving MRTA problem based on multi-strategy genetic algorithm

This paper proposes a multi-strategy genetic algorithm (DIHA-GA) to address the issues of local optima and low efficiency in solving multi-robot task allocation (MRTA) using genetic algorithm (GA). Firstly, a dual chromosome coding strategy was adopted to simplify the coding process. Secondly, the population was divided into three parts to enhance the quality of chromosomes while maintaining randomness. Then, heuristic crossover operators were used to expand the search range of the solution and increase the algorithm′s ability to jump out of local optima. Finally, adaptive crossover probability and mutation probability were used to make the algorithm find the optimal solution faster. The results showed that in the cases of 20 and 40 tasks, compared to the hybrid particle swarm optimization (HPSO), the average distance of the proposed DIHA-GA is reduced by 14.46 m and 17.36 m, respectively, and the minimum distance is reduced by 14.89 m and 20.86 m, respectively. This indicates that the solution obtained by DIHA-GA is closer to the optimal solution. The average distance obtained by DIHA-GA in this article is reduced by 21.32 m and 18.73 m respectively compared to the improved ant colony optimization (IACO), and the minimum distance is reduced by 23.43 m and 22.32 m respectively. This is due to the premature convergence of IACO and its difficulty in jumping out of local optima. The effectiveness of DIHA-GA in solving MRTA problems has been verified through comparison.