Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c 1 and c 2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost.

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Bare-bones particle swarm optimization (BPSO) is attractive since it is parameter free and easy to implement. However, it suffers from premature convergence because of quickly losing diversity, and the dimensionality of the solved problems has great impact on the solution accuracy. To overcome these drawbacks, this paper proposes an oppositionbased learning (OBL) modified strategy. First, to decrease the complexity of algorithm, OBL is not used for population initialization. Second, OBL is employed on the personal best positions (i.e., Pbest) to reconstruct Pbest, which is helpful to enhance convergence speed. Finally, we choose the global worst particle (Gworst) from Pbest, which simulates the human behavior and is called rebel learning item, and is integrated into the evolution equation of BPSO to help jump out local optima by changing the flying direction. The proposed modified BPSO is called BPSO-OBL, it has been evaluated on a set of well-known nonlinear benchmark functions in different dimensional search space, and compared with several variants of BPSO, PSOs and other evolutionary algorithms. Experimental results and statistic analysis confirm promising performance of BPSO-OBL on solution accuracy and convergence speed in solving majority nonlinear functions.

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