An improved particle swarm optimization algorithm with distributed time-delays of evolved acceleration coefficients and adaptive weights

Particle swarm optimization (PSO) is a classical computational method that optimizes a problem by iteratively trying to find the optimal solution. It still suffers somes defects such as poor local search ability, low search accuracy and premature convergence, especially in high-dimensional complex problems. In order to address these issues, this paper has proposed a novel PSO algorithm with distributed delays of adaptive weights and evolved acceleration coefficients (PSO-DWC). The main idea of the proposed improved PSO algorithm is three-fold: (1) a mechanism is introduced to evaluate the current evolutionary state by evolutionary factors of the swarm and to predict the next state by a probability transition matrix; (2) distributed time-varying time-delays are added into the velocity updated model; (3) adaptive inertia weight varies according to evolutionary factors, which describes the population distribution information; and newly-introduced evolved acceleration coefficients are determined by the predict next evolutionary state of the swarm. Owing to the promising issues mentioned above, the PSO-DWC algorithm has the advantages of keeping the diversity of particles, balancing the local and global search abilities and reaching to an acceptable solution. Experiments on twenty well-known benchmark functions have demonstrated that the proposed PSO-DWC algorithm has a superior performance over other five well-known PSO algorithms in high dimensional search space. Statistical significance tests verify the superiority of the new algorithm. Therefore it can be concluded that the novel PSO-DWC algorithm is able to solve the optimization problems with powerful global search and efficient convergence.