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- W2152869430 abstract "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." @default.
- W2152869430 created "2016-06-24" @default.
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- W2152869430 date "2004-06-22" @default.
- W2152869430 modified "2023-09-27" @default.
- W2152869430 title "Pareto-MEC for multi-objective optimization" @default.
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- W2152869430 doi "https://doi.org/10.1109/icsmc.2003.1243836" @default.
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