基于个体强度的自适应差分多目标免疫算法

考虑到支配解可能携带有利于算法搜索到最优解的信息, 在克隆阶段选择一部分非支配解和支配解克隆以提高种群多样性和避免算法早熟收敛。在进化阶段, 先采用自适应差分进化算子交叉变异, 然后用多项式变异算子进行扰动以有效地平衡算法的全局搜索和局部搜索。基于个体强度建立外部文档储存一定数量的较好解, 并让这些较好解在每次迭代中参与进化且被更新。对10个标准测试函数进行仿真实验, 并与其他5种算法进行比较, 结果表明所提算法在收敛性和解的分布性方面均表现出明显优势。

Individual strength-based multi-objective immune algorithm with adaptive differential evolution

Considering that some information contained in the dominant solution may be helpful to search for the optimal solution, some nondominated solutions and dominated solutions are selected in the clone phase to enhance the diversity of population and avoid the premature. In the evolutionary phase, crossover and mutation are excuted by an adaptive differential evolution operator and population is perturbed by the polynomial mutation operator to balance effectively global and local search of the algorithm. An archive is built based on individual strength to store a number of good solutions which are evolved and updated at each iteration. The proposed algorithm is compared with five existing evolutionary algorithms on ten standard benchmark functions. Experimental results show that the proposed algorithm has superiority in convergence and distribution.