Apical-dominant particle swarm optimization

Particle swarm optimization (PSO) is a new stochastic population-based search methodology by simulating the animal social behaviors such as birds flocking and fish schooling. Many improvements have been proposed within the framework of this biological assumption. However, in this paper, the search pattern of PSO is used to model the branch growth process of natural plants. It provides a different potential manner from artificial plant. To illustrate the effectiveness of this new model, apical dominance phenomenon is introduced to construct a novel variant by emphasizing the influence of the phototaxis. In this improvement, the population is divided into three different kinds of buds associated with their performances. Furthermore, a mutation strategy is applied to enhance the ability escaping from a local optimum. Simulation results demonstrate good performance of the new method when solving high-dimensional multi-modal problems.