Pareto-MEC for multi-objective optimization
Chongyi Sun, Xlaohong Qi, Ou Li · 2004
This paper proposes a new multi-objective optimization algorithm - Pareto Mind Evolutionary Computation (Pareto-MEC), which introduces the theory of Pareto into MEC for the multi-objective optimization. In the reference algorithms of Rand, VEGA, NSGA and SPEA, SPEA has the superior performance. Pareto-MEC is compared with these reference algorithms on a suit of four different test problems: convexity, non-convexity, discreteness and non-uniformity. On all test problems, Pareto-MEC outperforms Rand, VEGA and NSGA; it is as good as SPEA on the first three test problems; it beats SPEA on the last test problem. Different from the reference algorithms that use the pre-specified generation number as their terminations, Pareto-MEC has an objective termination criterion that can ensure the quality of solutions and the computational efficiency.