Electrical Engineering and Computer Science
Electrical and Computer Engineering
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 two concerns of dominance and diversity in the evolving population.
R. Rajagopalan, C. K. Mohan, K. G. Mehrotra, and P. K. Varshney, "An evolutionary multi-objective crowding algorithm (EMOCA): Benchmark test function results," in 2nd Indian International Conference on Artificial Intelligence, IICAI 2005, December 20, 2005 - December 22, 2005, Pune, India, 2005, pp. 1488-1506.