Improved Genetic Algorithms to Solving Constrained Optimization Problems
Can Zhu, Ximing Liang, Zhou Shu-renhu · 2009
The slow convergence speed and the lack of effective constraint handling strategies are the major concerns when applying genetic algorithms (Gas) to constrained optimization problem. An improved genetic algorithm was proposed by dividing population into three parts: optimal subpopulation, elitists subpopulation and spare subpopulation. We applied genetic algorithm on three subpopulations with different evolutionary strategies. Isolation of optimal subpopulation was to improve convergence speed. Population diversity was kept by spare subpopulation setting and aperiodically decreasing of the size of optimal subpopulation. Gene segregation was carried out by crossover operation between optimal subpopulation and spare subpopulation. Combination of penalty function method and the strategy of elitists preservation by setting elitists subpopulation was used to constraint handling. Some numerical tests have been made and the results show that the algorithm is effective.