An Improved Multiobjective Evolutionary Algorithm based on Decomposition with Fuzzy Dominance

被引：0

作者：

Nasir, Md ^{[1
]}

Mondal, A. K. ^{[1
]}

Sengupta, S. ^{[1
]}

Das, Swagatam ^{[1
]}

Abraham, Ajith ^{[2
,3
]}

机构：

[1] Jadavpur Univ, Dept Elect & Telecommun Engn, Kolkata 700032, India

[2] VSB Tech Univ Ostrava, Fac Comp Sci & Elect Engn, Ostrava, Czech Republic

[3] Machine Intelligence Res Labs MIR Labs, Seattle, WA USA

来源：

2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2011年

关键词：

Multiobjective; Dominance; Pareto; Decomposition; Fuzzy Dominance; Differntial Evolution; Individual; Parent; Offspring;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a new Multiobjective Evolutionary Algorithm (MOEA) based on decomposition, with fuzzy dominance (MOEA/DFD). The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. The diversity is maintained through the uniformly distributed weight vectors. In addition, Dynamic Resource Allocation (DRA) is used to distribute the computational effort based on the utilities of the individuals. To assess the performance of the proposed algorithm, experiments were conducted on two general benchmarks and ten unconstrained benchmark problems taken from the competition on real parameter MOEAs held under the 2009 IEEE Congress on Evolutionary Computation (CEC). As per the IGD metric, MOEA/DFD outperforms other major MOEAs in most cases.

引用

页码：765 / 772

页数：8

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