A complex-genetic algorithm for solving constrained optimization problems - Ming-Song Li; Pu-Hua Zeng; Ruo-Wu Zhong; Hui-Ping Wang; Fen-Fen Zhang (2008 International Conference on Machine Learning and Cybernetics, 2008)
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.