Improving particle swarm optimization: Using neighbor heuristic and Gaussian cloud learning

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.