Genetic and Particle Swarm Algorithm-Based Optimization Solution for High-Dimension Complex Functions

A hybrid of genetic and particle swarm algorithms is proposed to solve the high-dimension complex functions optimization. The algorithm is formulated in a form of hierarchical structure. The global search is performed at the master level by genetic algorithm, while the local search is carried out at the slave level by particle swarm optimization. Through the harmonizing mechanism between master and slave level, and special translation function designed for the slave level, the algorithm can execute global exact search without relying on complex coding and complex evolving operators. The simulation and results from comparison with other algorithms demonstrate the effectiveness of the proposed algorithm for high-dimension complex functions optimization.