Human Behavior-Based Particle Swarm Optimization

Abstract

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.

Novel Artificial Human Optimization Field Algorithms – The Beginning

“…for each particle i do 13) for each dimension d do 14) vi,d = w*vi,d + C1*Random(0,1)*( xi,dpworsti,d) + C2*Random(0,1)*( xi,dgworstd) 15) xi,d = xi,d + vi,d 17) end for 18) end for 19) iterations = iterations + 1 20) while ( termination condition is false)…”

ENTREPRENEUR : Artificial Human Optimization

“…Hence, in the proposed CIPSO, the particle with global best explores more by only using cognitive component with increasing inertia and self-cognition, where as other particles use explore and exploit using self with entire dimension selection and random social cognition with randomly selected dimensions for updating velocities. The performance of the proposed CIPSO is evaluated using 10 benchmark test functions as suggested in CEC2005 [3]. The performance is also compared with different variants of PSO algorithms reported in the literature.…”

An SNN-Based Semantic Role Labeling Model with Its Network Parameters Optimized Using an Improved PSO Algorithm

“…factory aspects in practical applications, such as premature convergence and poor ability in global optimization [35,39].…”