A hybrid memetic algorithm for global optimization

A hybrid memetic algorithm, called a memetic algorithm with double mutation operators (MADM), is proposed to deal with the problem of global optimization. In this paper, the algorithm combines two meta-learning systems to improve the ability of global and local exploration. The double mutation operators in our algorithms guide the local learning operator to search the global optimum; meanwhile the main aim is to use the favorable information of each individual to reinforce the exploitation with the help of two meta-learning systems. Crossover operator and elitism selection operator are incorporated into MADM to further enhance the ability of global exploration. In the first part of the experiments, six benchmark problems and six CEC2005@?s problems are used to test the performance of MADM. For the most problems, the experimental results demonstrate that MADM is more effective and efficient than other improved evolutionary algorithms for numerical optimization problems. In the second part of the experiments, MADM is applied to a practical problem, clustering complex and linearly non-separable datasets, with a satisfying result.