Pareto-MEC for multi-objective optimization

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. Pare...

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Bibliographic Details
Main Authors: Chengyi Sun, Xiaohong Qi, Ou Li
Format: Conference Proceeding
Language:English
Subjects:
Cities and towns
Computational efficiency
Educational institutions
Evolutionary computation
Genetic algorithms
Paper technology
Pareto optimization
Performance evaluation
Sun
Testing
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Summary: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.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2003.1243836