A Clustering Ranking Based Multiobjective Evolutionary Algorithm
AbstractWe propose a new clustering ranking based multiobjective evolutionary algorithm. The algorithm uses decision maker’s preferences to reduce the search space and obtain a final set of preferred Pareto-optimal solutions. A new clustering ranking operator using Hierarchical Clustering on Principle Components (HCPC) and K-means methods is developed. We also develop a new crossover operator. The algorithm is implemented on several problems. The work is still in progress.