The particle swarm optimization (PSO) algorithm is a popular evolutionary computation approach that has received an ever-increasing interest in the past decade owing to its wide application potential. Despite the many variants of the PSO algorithm with improved search ability by means of both the convergence rate and the population diversity, the local optima problem remains a major obstacle that hinders the global optima from being found. In this paper, a novel randomized particle swarm optimizer (RPSO) is proposed where the Gaussian white noise with adjustable intensity is utilized to randomly perturb the acceleration coefficients in order for the problem space to be explored more thoroughly. With this new strategy, the RPSO algorithm not only maintains the population diversity but also enhances the possibility of escaping the local optima trap. Experimental results demonstrate that the proposed RPSO algorithm outperforms some existing popular variants of PSO algorithms on a series of widely used optimization benchmark functions.

粒子群优化（PSO）算法是一种流行的进化计算方法，由于其广阔的应用潜力，在过去十年中受到越来越多的关注。尽管PSO算法的许多变体通过收敛速度和总体多样性提高了搜索能力，但是局部最优问题仍然是阻碍全局最优发现的主要障碍。本文提出了一种新颖的随机粒子群优化器（RPSO），其中利用强度可调的高斯白噪声随机扰动加速度系数，以便更全面地探索问题空间。通过这种新策略，RPSO算法不仅可以保持种群多样性，而且还可以逃避局部最优陷阱。