Evolutionary Algorithm Based on the Evolution of Pareto Archive and Individual Migration
An evolutionary algorithm based on the parallel evolution of multiple single objective populations and Pareto archive population is proposed, which is not only suitable for solving multi-objective optimization, but also effective for multimodal function. For each single objective population, single objective evolutionary algorithm is applied to optimize separately each of multi-objective functions, where individuals generated by tournament selection from the union of single objective and Pareto Archive population form the single objective population of next generation. Especially, individuals in Pareto archive population also join evolutionary operations. Simulations manifest that the proposed method can realize the search from multiple directions to obtain the non-dominated solutions scattered more uniformly over the Pareto frontier with better convergence metric compared to well-known NSGA-II algorithm. Individual migration from Pareto archive population by tournament selection is also proved to have the advantage in improving the converging speed and converging precision. Moreover, for multimodal single objective function, simulations also show that ideal optimizing solution can be obtained by properly separating single objective function into multi-objective function and applying the above method.
Rongbin Qi Tianyi Ma Fan Sun Feng Qian
Advanced Control and Optimization for Chemical Processes, Ministry of Education. East China Universi Shanghai Business School, Shanghai, China.
国际会议
2011 International Symposium on Advanced Control of Industrial Processes(2011工业过程先进控制技术国际研讨会)
杭州
英文
90-95
2011-05-01（万方平台首次上网日期，不代表论文的发表时间）