关键词: 粒子群优化算法, 差分进化算法, 群体智能, 二范数, 基准函数

Abstract: To reduce the possibility of falling into the local optima during the search process of the original Particle Swarm Optimization (PSO) and avoid destroying the population diversity, a hybrid two-norm particle swarm optimization algorithm with crossover term, namely HTPSO, was proposed. Firstly, the two-norm was employed to measure the Euclidean distance between current particle and its individual history best one. Then, the Euclidean distance was incorporated into the velocity updating formula in order to affect the influence of social term on particles' velocity, and inertia weight was randomly distributed in accordance with certain rules. Based on these operations, what's more, HTPSO was simplified and the crossover operator in the Differential Evolution (DE) algorithm was incorporated into the algorithm, which enables each particle to intersect with its individual history best one under a certain probability. In order to verify the excellent performance of HTPSO, four improved PSOs were introduced, including Particle Swarm Optimization algorithm for improved weight using Sine function (SinPSO), Self-adjusted Particle Swarm Optimization algorithm (SelPSO), Mean Particle Swarm Optimization algorithm based on Adaptive inertia Weight (MAWPSO) and Simple Particle Swarm Optimization algorithm (SPSO). The optima of eight commonly used benchmark functions in different dimensions were compared, the results of five algorithms were analyzed by T-test, success rate and average iteration times. Compared with the contrast algorithms, HTPSO has strong convergence, and the particles' movements are very flexible.

Key words: Particle Swarm Optimization algorithm (PSO), Differential Evolution (DE) algorithm, swarm intelligence, two-norm, benchmark function