Compressed-Objective Genetic Algorithm
A strategy for solving an optimisation problem with a large number of objectives by transforming the original objective vector into a two-objective vector during survival selection is presented. The transformed objectives, referred to as preference objectives, consist of a winning score and a vicinity index. The winning score, a maximisation criterion, describes the difference of the number of superior and inferior objectives between two solutions. The minimisation vicinity index describes the level of solution clustering around a search location, particularly the best value of each individual objective, is used to encourage the results to spread throughout the Pareto front. With this strategy, a new multi-objective algorithm, the compressed-objective genetic algorithm (COGA), is introduced. COGA is subsequently benchmarked against a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto genetic algorithm (SPEA-II) in six scalable DTLZ benchmark problems with three to six objectives. The results reveal that the proposed strategy plays a crucial role in the generation of a superior solution set compared to the other two techniques in terms of the solution set coverage and the closeness to the true Pareto front. Furthermore, the spacing of COGA solutions is very similar to that of SPEA-II solutions. Overall, the functionality of the multi-objective evolutionary algorithm (MOEA) with preference objectives is effectively demonstrated.
KeywordsGenetic Algorithm Pareto Front Multiobjective Optimization Extreme Solution Preference Objective
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