Particle swarm optimization based on swarm energy conservation

To the problem of premature convergence frequently appeared in standard particle swarm optimization(PSO) algorithm,an improved algorithm,swarm energy conservation particle swarm optimization (SEC-PSO),is proposed. The population is partitioned into two sub-swarms according to the energy of the particles. The good particles update their velocity according to the strategy with the worst particle. The bad particles update their velocity according to the strategy with penalty mechanism,and bear the swarm energy loss which is generated by speed reduction of the good population. Thus,the problem of premature convergence of the PSO algorithm is prevented. Simulations results for several typical test functions show that SEC-PSO possesses more powerful global search capabilities,better convergence rate and better performance of optimization.