Analysis About The Application Of The Multiobjective Genetic Algorithm

Using Evolutionary Algorithms (EAs) to solve global optimization problems, i.e.Evolutionary Optimization (EO), is always the most important research and applica-tion domain of EAs. Although having been successfully applied in many practicalproblems, EAs not only lack steady theoretical foundations, but expose many shortco-mings in practical applications. 1. We introduce related biology knowledge and give a brief review of the deve-lopment history of EAs. Then we conclude the main characters of EAs and sum up thepresent situation of the theories and applications of EAs. 2. This paper provides a new multi-objective genetic algorithm that improves onthe random weight multi-objective genetic algorithm from three parts as follow:(1) Reserving elite. Choosing individuals whose crowding distances are themost ones from temporary Pareto solution set as the elite to reserve. Therefore, thePareto solution whose individual density is small around currently is better than theothers, that is profit for holding the variety of the genus group;(2) Applying themethod of tracing weight to record the weights according to the individuals chosen inthe choosing progress;(3) Generating members of fixed weights to choose theoperation and find out the Pareto solution whose crowding distance is the biggest.Then generating some weights around the weight according to such solution to choosethe operation and make the Pareto solution to distribute in the Pareto former edgemore uniformly. The result of the test shows that the new algorithm has the bettereffect.Nelite3. We give a brief review on the development history of hierarchical optimalsystems, then we conclude the main characters of hierarchical optimal systems, statethe survey of hierarchical optimal systems.4. Towards the shortage of the present methods that solve the situation wherethere are upper level 0-1 decision variables and lower level multi-objective functions ,we propose anew method which accord with the actual demands. In this paper, wediscussed the situation where there are upper level 0-1 decision variables and lowerlevel multi-objective functions, which leads to certain amount of optimal valuesbetween preferences of the upper and lower level decision makers. We furtherconstruct a multi-population co-evolution genetic algorithm. Multiple subpopulationevolve simultaneously. The preferences of the lower level objective functions of eachsubpopulation are different and we use the information exchanging principle to solvethe optimal values in different preferences in one process of the evolution in thewhole population. The examples in our paper show the efficiency of this algorithm.