A particle swarm optimization (PSO) is a new random optimization technology with many diverse applications. In this paper, an improved algorithm—swarm energy constant particle swarm optimization (SEC-PSO) is proposed in order to improve the accuracy of the algorithm and speed up the convergence rate. In this new algorithm, the population is partitioned into several sub-swarms according to the energy of the particles. The developed approach gets the information of the particles through taking the worst value and the best value into consideration. Simulations for several typical test functions show that SEC-PSO possesses more powerful global search capabilities and better performance of optimization.