Abstract: To overcome the weakness of over-concentration when the population of Particle Swarm Optimization (PSO) is initialized and the search precision of basic PSO is not high, an Improved PSO (IPSO) for constrained optimization problems was proposed. A technique of Good Point Set (GPS) was introduced to distribute the initialized particles evenly and the population with diversity would not fall into the local extremum. Co-evolutionary method was utilized to maintain communication between the two populations; thereby the search accuracy of PSO was increased. The simulation results indicate that, the proposed algorithm obtains the theoretical optimal solutions on the test of five benchmark functions used in the paper and the statistical variances of four of them are 0. The proposed algorithm improves the calculation accuracy and robustness and it can be widely used in the constrained optimization problems.

Key words: constrained optimization, good point set, particle swarm optimization, co-evolution

摘要： 针对种群初始化时粒子过于集中和基本粒子群算法搜索精度不高的缺陷，提出了一种求解约束优化问题的改进粒子群算法。该算法引入佳点集技术来优化种群的初始粒子，使种群粒子初始化时分布均匀，因而种群具有多样性，不会陷入局部极值；同时使用协同进化技术使双种群之间保持通信，从而提高算法的搜索精度。仿真实验结果表明：将该算法用于5个基准测试函数，该算法均获得了理论最优解，其中有4个函数的测试方差为0。该算法提高了计算精度且鲁棒性强，可以广泛应用于其他约束优化问题中。

关键词: 约束优化, 佳点集, 粒子群算法, 协同进化