Multi-Pareto-Ranking Evolutionary Algorithm

摘要: This paper proposes a new multi-objective genetic algorithm, called GAME, to solve constrained optimization problems. GAME uses an elitist archive, but it ranks the population in several Pareto fronts. Then, three types of fitness assignment methods are defined: individuals depends on front they belong to. The crowding distance is also used preserve diversity. Selection based two steps: first selected, before choosing individual among solutions contains. probability choose given computed using parameters which tuned design experiments. influence number fronts studied experimentally. Finally GAME's performance assessed and compared with other algorithms according conditions CEC 2009 competition.