Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (8): 2148-2156.

### Hybrid two-norm particle swarm optimization algorithm with crossover term

1. School of Electrical Engineering & Automation, Jiangsu Normal University, Xuzhou Jiangsu 221116, China
• Received:2018-01-29 Revised:2018-03-18 Online:2018-08-10 Published:2018-08-11
• Supported by:
This work is partially supported by the National Natural Science Foundation of China (61403174), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_1576).

### 含交叉项的混合二范数粒子群优化算法

1. 江苏师范大学 电气工程及自动化学院, 江苏 徐州 221116
• 通讯作者: 张鑫
• 作者简介:张鑫(1994-),男,江苏金湖人,硕士研究生,主要研究方向:群智能优化算法;邹德旋(1982-),男,辽宁大连人,副教授,博士,主要研究方向:群智能优化算法、电力系统经济调度;沈鑫(1994-),男,江苏盐城人,硕士研究生,主要研究方向:群智能优化算法。
• 基金资助:
国家自然科学基金资助项目（61403174）；江苏省研究生科研创新计划项目（KYCX17_1576）。

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.

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