A Genetic Algorithm Based on Multi-bee population evolutionary for numerical optimization

In this paper, genetic algorithm based on multi-bee population evolutionary (BMGA) is proposed. In BMGA, there are many bee populations. One is from generation by the BMGA, the others are random populations, and consequentially it enhances the exploration of genetic algorithm. Optimum individual being a queen-bee in each population crossover with each selected individual (drone). As a result it reinforces the exploitation of genetic algorithm, avoids premature convergence, and extends search area. The experiments results show that BMGA is an efficient and effective improved genetic algorithm and in terms of the stability, convergence and coverage in searching a better value.