Proceedings Article10.1109/ICMLC.2008.4620526
A complex-genetic algorithm for solving constrained optimization problems
Ming-Song Li,Pu-Hua Zeng,Ruo-Wu Zhong,Hui-Ping Wang,Fen-Fen Zhang +4 more
- 12 Jul 2008
- Vol. 2, pp 869-873
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TL;DR: A novel improved algorithm called complex-GA, which converts COPs into multi-Objective optimization problems (MOPs) and effectively combines multi-objective optimization concept with global and local search, was proposed to handle COPs.
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Abstract: Constrained optimization problems (COPs) are a kind of mathematic programming problem frequently encountered in the disciplines of science and engineering application. After analyzing weaknesses of existing constrained optimization evolutionary algorithms (COEAs), a novel improved algorithm called complex-GA, which converts COPs into multi-objective optimization problems (MOPs) and effectively combines multi-objective optimization concept with global and local search, was proposed to handle COPs. Complex-GA increases the speed of optima search noticeably by combining the advantages of the two methods and overcomes the disadvantages of them.
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References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
A new evolutionary algorithm for solving constrained optimization problems
Liu Hui
- 01 Jan 2006
TL;DR: The experimental results show that the algorithm proposed is very suitable for functions with high dimension and multimodality and is clear that the new algorithm is steadier than other COEAs from the literature in dealing with COPs.
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•Journal Article
Theoretic Analysis and Accelerating of a Class of Self-Adaptive Niching Genetic Algorithms
TL;DR: The analytical solution in equilibrium for two niche problem proves that NGA is capable of forming and maintaining stable subpopulations, which is verified by experiments and shows that, NGA+PLS is a much more competent optimization method than canonical genetic algorithms and other niche methods.
6
•Journal Article
A Fast Multi-Objective Genetic Algorithm Based on Clustering
TL;DR: It is proved that the individuals of an evolutionary population can be classified by the idea of quick sort and it is shown that the convergent speed of the algorithm discussed is more efficient than the other existing algorithms.
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