Particle swarm optimization (PSO) is a dynamic nature-influenced optimization technique. PSO optimization technique can resolve the best solution in minimum iterations and operates more effectively and efficiently. But, the other optimization techniques like particle swarm optimization with passive congregation (PSOPC) technique and dissipative particle swarm optimization (DPSO) technique gives better solution in fewer iterations as compared to PSO. In this paper, the distance between swarms is optimized and compared to all the optimization techniques. Simulation results demonstrate that the PSOPC optimization technique delivers better results than the PSO and DPSO optimization techniques.

粒子群优化（PSO）是一种受自然影响的动态优化技术。PSO优化技术可以在最少的迭代中解决最佳解决方案，并且可以更高效地运行。但是，与PSO相比，其他优化技术（例如带有被动会聚的粒子群优化（PSOPC）技术和耗散粒子群优化（DPSO）技术）提供了更好的解决方案，迭代次数更少。本文对群体之间的距离进行了优化，并与所有优化技术进行了比较。仿真结果表明，PSOPC优化技术比PSO和DPSO优化技术提供了更好的结果。