A Fast and Effective Method for Pruning of Non-dominated Solutions in Many-Objective Problems
Diversity maintenance of solutions is an essential part in multi-objective optimization. Existing techniques are suboptimal either in the sense of obtained distribution or execution time. This paper proposes an effective and relatively fast method for pruning a set of non-dominated solutions. The proposed method is based on a crowding estimation technique using nearest neighbors of solutions in Euclidean sense, and a technique for finding these nearest neighbors quickly. The method is experimentally evaluated, and results indicate a good trade-off between the obtained distribution and execution time. Distribution is good also in many-objective problems, when number of objectives is more than two.
KeywordsMultiobjective Optimization Vector Quantization Pruning Method Strength Pareto Evolutionary Algorithm Crowd Distance
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