基于反向学习和种群引导的多目标蝗虫优化算法

A multi-objective grasshopper optimization algorithm based on opposition-based learning and population guidance

为了解决多目标优化的相关问题,提出了求解多目标的蝗虫优化算法,结合单个目标的蝗虫优化算法的搜寻机制、帕累托优势以及拥挤度策略,并在算法中应用种群引导和高斯变异算子,加入了反向学习机制。将所提出的算法与经典的MOPSO、MOCS、MOGOA和MOWOA算法进行了比较,比较结果表明,所提出的改进多目标蝗虫优化算法具有良好的鲁棒性,所求得的解分布更均匀,收敛更快速,是一种有着良好应用前景的多目标进化算法。

In order to solve the related problems of multi-objective optimization,this paper proposes an improved multi-objective grasshopper optimization algorithm,by combining the search mechanism of the single-target grasshopper optimization algorithm and the Pareto advantage and crowding strategy,applying the population guidance and Gaussian mutation operator in the algorithm,and adding the reverse learning mechanism.In the experimental verification,the proposed algorithm is compared with the classic MOPSO,MOCS,MOGOA,MOWOA algorithms.The experimental results show that the improved multi-objective grasshopper optimization algorithm has good robustness,more uniform distribution of the solution,and fast convergence.It is a multi-objective evolutionary algorithm with good application prospects.