Conventional Particle Swarm Optimization (PSO) algorithm has disadvantage of premature convergence and is easily trapped in local optima. An improved PSO algorithm with adaptive task allocation was proposed to avoid those disadvantages. Adaptive task allocation was applied to particles according to their distribution status and fitness. All the particles were divided into exploration particles and exploitation particles, and carried out different tasks with global model and dynamic local model respectively. This strategy can make better trade-off between exploration and exploitation and enhance the diversity of particle. Dynamic neighborhood strategy broadened the search space and effectively inhibited the premature stagnation. Gaussian disturbance learning was applied to the stagnant elite particles to help them jump out from local optima region. The superior performance of the proposed algorithm in global search ability and solution accuracy was validated by optimizing six complicated composition test functions.

Concerning that conventional Particle Swarm Optimization (PSO) is easy trapped in local optima and with low search efficiency in later stage, an improved PSO based on mean information and elitist mutation, named MEPSO, was proposed. Average information of swarm was introduced into MEPSO to improve the global search ability, and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. In the latter stage of the iteration, the Cauchy mutation operation was applied to the global best particle to improve the global search ability and to further reduce the risk of trapping into local optimum. Contrast experiments on six benchmark functions were given. Compared with Basic PSO (BPSO), PSO with TVAC (PSO-TVAC), PSO with Time-Varying Inertia Weight factor (PSO-TVIW) and Hybrid PSO with Wavelet Mutation (HPSOWM), MEPSO achieved better mean value and standard variance with shorter optimization time and better reliability. The results show that MEPSO can better balance the ability of local search and global search, and can converge faster with higher accuracy and efficiency.