混杂免疫多目标优化算法及对动态经济环境调度问题优化.

Dynamic economic emission dispatch（DEED） problem in power system is a kind of high-dimensional multiobjective optimization problem with large-scale constraints. The traditional evolutionary algorithm is easy to fall into local optimization, and the distribution and convergence of the obtained Pareto frontier are poor. In this paper, it fully explore the clonal selection principle of immune system, and propose a hybrid immune multi-objective optimization algorithm（HIMOA）. The proposed algorithm takes the traditional evolutionary algorithm as the basic framework.Since the deficiency of falling local optimization for the high-dimensional decision variables optimization, the external archive update mechanism is improved to preserve the excellent individuals from previous generations, and the cloning and Gaussian mutation strategy is adopted to strengthen the local exploitation ability, forcing effectively the HIMOA to jump out of the stagnant search state. In order to cope with the large-scale constraints, designing the fine-turning output power step by step to improve the feasibility of the evolutionary population. In the numerical simulation experiment, taking a 10-unit system as a test example, and compares HIMOA with the famous algorithms MODE,NSGAII, IMOEA/D-CH, ADEA, MOHDE＿SAT, MONNDE. The results show that HIMOA can provide a better Pareto solution to the 10-unit system of the DEED problem, and the convergence and distribution of the Pareto frontier obtained are better than other algorithms. The box diagram of each evaluation metric shows that HIMOA has better statistical characteristics than other algorithms. [ABSTRACT FROM AUTHOR]