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| A Linear Evolutionary Algorithm for Solving Constrained Optimization Problems |
| TANG Ke-Zong1,3, YANG Jing-Yu1, GAO Shang2,3, LI Wei1 |
1.School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094
2.School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003
3.Key Laboratory of CAD＆CG,Zhejiang University,Hangzhou 310027 |
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Abstract A linear evolutionary algorithm for solving constrained optimization problems (LEACOP) based on real-coded method is proposed, and its complexity and convergence are also analyzed. One of the main advantages of the proposed algorithm is that the search space of constrained dominance problems with high dimensions is compressed into two dimensions. A linear fitness function based on mathematic analysis is deduced in two dimension space to fast evaluate fitness value of each individual in population. A crossover operator based on density function and a new mutation operator are developed to extend the search space and extract better solution. In addition, an average linkage based on hierarchical clustering method is introduced into the LEACOP to maintain the number of individuals on Pareto set. A few benchmark multi-objective optimization problem which is divided into three groups is introduced to test this algorithm. The numerical experiments show that proposed algorithm is feasible and effective, and it provides good performance in terms of uniformity and diversity of solutions.
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Received: 22 December 2008
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2009, Vol. 22 
