Research on an Improved Evolution Algorithm and its Application in Function Optimization Problem

An improved evolution algorithm (IEA) is proposed in this paper. It has some new features: 1) using multi-parent search strategy and stochastic ranking strategy and a simple diversity rules to maintain the diversity of the population; 2) using a hybrid self-adaptive crossover-mutation operator, which can enhance the search ability and exploit the optimum offspring; The algorithm of this paper is tested on 13 benchmark optimization problems with linear or/and nonlinear constraints and compared with other evolutionary algorithms. The experimental results demonstrate that the performance of IEA outperforms other evolutionary algorithms in terms of the quality of the final solution and the stability; and its computational cost is lower than the cost required by the other techniques compared.