Affiliations: [a] College of Field Engineering, The PLA University of Science and Technology, Nanjing, Jiangsu, China | [b] College of Command Information System, The PLA University of Science and Technology, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Donghui Zhan, College of Field Engineering, The PLA University of Science and Technology. Nanjing, Jiangsu 210007, China. Tel.: +86 15895970195; E-mail:zhandonghui1018@163.com
Abstract: The Particle Swarm Optimization (PSO) is a heuristic
optimization technique-based swarm intelligence that can be applied to
solving many real-world optimization problems. However, the standard PSO
algorithm can easily get trapped in the local optima and has slow
convergence speed, and these drawbacks have hindered its further development
in all fields. In this paper, a new optimization method based on neighbor
heuristic and Gaussian cloud learning is introduced in order to improve the
performance of traditional PSO (NHPSO). The NHPSO consists of two main steps.
First, by analyzing the relationship among particles in the evolutionary
process, a neighbor heuristic mechanism is performed to improve the search
efficiency and convergence speed. In addition, a Gaussian cloud learning
strategy is introduced to enhance population diversity and balance the
global and local search abilities. The performance of the NHPSO is tested
using 12 benchmark functions and 6 shifted functions. Results show that
NHPSO is superior to the recent variants of PSO in terms of convergence
speed, solution accuracy, algorithm efficiency and robustness.
