Optimization based on Multi-Meme Memetic Algorithm

In this paper a new learning automata-based Multi-Meme memetic algorithm which is obtained from combination of learning automata (LA) and memetic algorithm (MA) is proposed for optimization problems. This algorithm is composed of two parts, genetic section and memetic section. Genetic section operates based on the irregular cellular learning automata (ICLA) which is a generalization of cellular learning automata (CLA) in which the restriction of rectangular grid structure in CLA is removed. Memetic section consists of a pool of memes in which each meme is correspond to a certain local search method and represented by a set of LAs by which the history of the corresponding local search method can be extracted. To show the superiority of our proposed algorithm over the some well-known algorithms, several computer experiments have been conducted. The obtained results show that the new algorithm performs better that other methods in terms of running time of algorithm and required number of colors.