Journal of Applied Mathematics

A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems

Ning Dong and Yuping Wang

Full-text: Open access

Abstract

The constrained optimization problem (COP) is converted into a biobjective optimization problem first, and then a new memetic differential evolution algorithm with dynamic preference is proposed for solving the converted problem. In the memetic algorithm, the global search, which uses differential evolution (DE) as the search scheme, is guided by a novel fitness function based on achievement scalarizing function (ASF). The novel fitness function constructed by a reference point and a weighting vector adjusts preference dynamically towards different objectives during evolution, in which the reference point and weighting vector are determined adapting to the current population. In the local search procedure, simplex crossover (SPX) is used as the search engine, which concentrates on the neighborhood embraced by both the best feasible and infeasible individuals and guides the search approaching the optimal solution from both sides of the boundary of the feasible region. As a result, the search can efficiently explore and exploit the search space. Numerical experiments on 22 well-known benchmark functions are executed, and comparisons with five state-of-the-art algorithms are made. The results illustrate that the proposed algorithm is competitive with and in some cases superior to the compared ones in terms of the quality, efficiency, and the robustness of the obtained results.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 606019, 15 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305815

Digital Object Identifier
doi:10.1155/2014/606019

Mathematical Reviews number (MathSciNet)
MR3219424

Citation

Dong, Ning; Wang, Yuping. A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems. J. Appl. Math. 2014 (2014), Article ID 606019, 15 pages. doi:10.1155/2014/606019. https://projecteuclid.org/euclid.jam/1425305815


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