Abstract
Particle swarm optimization (PSO) is a widely used heuristic algorithm. However, canonical PSO may lead to premature convergence. To solve this problem, researchers try to hybridize PSO with genetic algorithm (GA) which facilitates global effectiveness. One of the successful algorithms is genetic learning PSO (GL-PSO). However, we find that the selection in GL-PSO reduce the diversity of particles. It may lead premature convergence in some test functions. To solve this problem, we figure out a genetic learning particle swarm optimization with diverse selection (GL-PSODS). We test our proposed algorithm in test functions of CEC2014. Our experiments show that GL-PSODS has an improvement in some test functions compared to PSO and GL-PSO.
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