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The Scientific World Journal
Volume 2014, Article ID 836272, 21 pages
http://dx.doi.org/10.1155/2014/836272
Research Article

Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher

1School of Computer Science and Engineering, Xi’an University of Technology, P.O. Box 666, No. 5 South Jinhua Road, Xi’an 710048, China
2School of Computer Science, Xi’an Polytechnic University, China
3Shaanxi Huanghe Group Co., Ltd., Xi’an, China

Received 16 April 2014; Accepted 18 June 2014; Published 23 July 2014

Academic Editor: T. O. Ting

Copyright © 2014 Kaifeng Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.