基于多策略融合的混合多目标蝗虫优化算法

为提高蝗虫优化算法（GOA）求解多目标问题的性能，提出一种基于多策略融合的混合多目标蝗虫优化算法（HMOGOA）。首先，利用Halton序列建立初始种群，保证种群在初始阶段具有均匀分布和较高多样性；然后，通过差分变异算子引导种群变异，促进种群向优势个体移动同时进行更大范围寻优；最后，利用自适应权重因子根据种群优化情况动态调整算法全局搜索和局部寻优能力，提高优化效率及解集质量。选取7个典型函数进行实验测试，并将HMOGOA与多目标蝗虫优化、多目标粒子群（MOPSO）、基于分解的多目标进化（MOEA/D）及非支配排序遗传算法（NSGA Ⅱ）对比分析。实验结果表明，该算法避免了其他四种算法的局部最优问题，明显提高了解集分布均匀性和分布广度，具有更好的收敛精度和稳定性。

Hybrid multi-objective grasshopper optimization algorithm based on fusion of multiple strategies

In order to improve the performance of Grasshopper Optimization Algorithm (GOA) in solving multi-objective problems, a Hybrid Multi-objective Grasshopper Optimization Algorithm (HMOGOA) based on fusion of multiple strategies was proposed. First, the Halton sequence was used to establish the initial population to ensure that the population had an uniform distribution and high diversity in the initial stage. Then, the differential mutation operator was applied to guide the population mutation, so as to promote the population to move to the elite individuals and extend the search range of optimization. Finally, the adaptive weight factor was used to adjust the global exploration ability and local optimization ability of the algorithm dynamically according to the status of population optimization, so as to improve the optimization efficiency and the solution set quality. With seven typical functions selected for experiments and tests, HMOGOA were compared with algorithms such as multi-objective grasshopper optimization, Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA Ⅱ). Experimental results indicate that compared with the above algorithms, HMOGOA avoids falling into local optimum, makes the distribution of the solution set significantly more uniform and broader, and has greater convergence accuracy and stability.