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An Evolutionary Multi-Objective Crowding Algorithm (Emoca): Benchmark Test Function Results, Ramesh Rajagopalan, Chilukuri K. Mohan, Kishan G. Mehrotra, Pramod K. Varshney

Electrical Engineering and Computer Science - All Scholarship

A new evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine benchmark multiobjective optimization problems, and shown to produce non-dominated solutions with significant diversity, outperforming state-of-the-art multi-objective evolutionary algorithms viz., Non-dominated Sorting Genetic Algorithm – II (NSGA-II), Strength Pareto Evolutionary algorithm II (SPEA-II) and Pareto Archived Evolution Strategy (PAES) on most of the test problems. The key new approach in EMOCA is to use a diversity-emphasizing probabilistic approach in determining whether an offspring individual is considered in the replacement selection phase, along with the use of a non-domination ranking scheme. This approach appears to provide a useful compromise between the …