Double Traction Strategy Particle Swarm Optimization Algorithm.

To solve the problem that the particle swarm optimization algorithm is easy to fall into the local optimum, a Double Traction Strategy Particle Swarm Optimization algorithm(DTSPSO) is proposed. Firstly, the algorithm divides the population into multiple subgroups by random grouping method, and then randomly assigns an optimization strategy to each subgroup, namely combined traction strategy or optimal traction strategy. In the iterative process, the algorithm will replace the optimization strategy or regroup the packets by monitoring the optimal update of the population optimal solution. The performance of DTSPSO algorithm and other five classical algorithms on six international benchmark functions is compared. The experimental results show that the DTSPSO algorithm exhibits relatively good comprehensive search performance in the optimization of unimodal and multimodal functions.