Collaborative optimization based on particle swarm optimization and chaos searching

This paper Aims at the problem that the particle swarm optimization algorithm(PSO) is easy to pass into the local minimum and the weakness of hybrid particle swarm optimization algorithm often uses too much iterations and consumes a long time, and puts forward a parallel collaborative optimization algorithms that combine PSO searching and chaotic searching together through using the advantages that particle swarm optimization algorithm can converge rapidly and the tent sequences have much better ergodicity. Parallel collaborative optimization algorithm utilizes PSO algorithm and chaotic searching respectively for global searching and local searching and reduces the number of iterations and the time consumption for searching through information exchange and sharing under the premise of ensuring the ability of global searching raises. Testing shows: the parallel collaborative optimization algorithm not only has a better optimization ability than originally algorithm, but although can find the optimal solution with the less iterations and use less time to complete the process of searching the best solution.