A New Improved Evolution Algorithm In Multi-stage Process Planning Optimization

The Traditional EAs often meet some challenges when put into practice. Preferred Optimization, Coding Model and Dynamic Environment ask a higher performance of EAs. In the Preferred Optimization, the weighted summation of fitness function which transforms multi-object into single-object and the reference point based decision are often used. In the weighted summation of fitness function, the weighted parameter is difficult to confirm and the reference point is not easy to find. Dynamic environment is always the largest challenge of EAs. Under dynamic conditions, the designed variables and object function often meet interferences. The main solution are Population Expansion(PE) and calculating the mean of sampling numbers, but the precision of the solution can’t be guaranteed..Aimed at the industry application in the Multi-stage process planning and considering the above deficiencies, this paper proposed a Multi-Decision Conbination Mulit-Object Evolution Algorithm(MDC-MOEA) and adopted filering technique. The main works of this paper includes the following points.Firstly, considering the practice application and based on the information of decision maker, this paper takes Mulit-Decision Combination technique which combines non-dominated decision, reference distance and crowding density decision with probability as the new decision policy. It proved to be a high performance in the simulation.Secondly, aimed at the noises in the EAs which make the EAs can’t find optimization, this paper has found a new filtering approach based on Fourier Space Transform and applied it in MDC-MOEA. Hence, the improved MDC-MOEA also performs well in dynamic environment.At last, By taking the Multi-stage Process Planning Problem in the manufacturing industry as optimization task, the MDC-MOEA with filtering approach has done preferred optimization in the dynamic environment successfully and simulation show its high efficiency.