A multi-population genetic algorithm based on chaotic migration strategy and its application to inventory programming

Consulting from the idea of isolated evolution and information exchanging in distributed parallel genetic algorithm, this paper comes up with a chaotic migration based multi-population genetic algorithm (CMBMGA) to solve optimization problem with considerations to restrain premature convergence. In this algorithm, asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence. Information exchanging among sub-populations is ensured to be efficient and sufficient due to that the sequence is ergodic and stochastic. Simulation study of the algorithm and its application to inventory programming show its global search ability, superiority to standard genetic algorithm and immunity against premature convergence.