A New Combined Particle Swarm Optimization Algorithm Based Golden Section Strategy

Abstract

Based the defects of global optimal model falling into local optimum easily and local model with slow convergence speed during traditional PSO algorithm solving a complex high-dimensional and multi-peak function, a two sub-swarms particle optimization algorithm is proposed. All particles are divided into two equivalent parts. One part particles adopts global evolution model, while the other part uses local evolution model. If the global optimal fitness of the whole population stagnates for some iteration, a golden rule is introduced into local evolution model. This strategy can substitute the partial perfect particles of local evolution for the equivalent worse particles of global evolution model. So, some particles with advantage are joined into the whole population to make the algorithm keep active all the time. Compared with classic PSO and PSO-GL(A dynamic global and local combined particle swarm optimization algorithm, PSO-GL), the results show that the proposed PSO in this paper can get more effective performance over the other two algorithm in the simulation experiment for four benchmark testing function.

Geometrical Substantiation of Phi, the Golden Ratio and the Baroque of Nature, Architecture, Design and Engineering

“…Anthropomorphic Robot research is one of the interesting fields where the humanoid robot sizing can be established based on Golden Proportion to make the social robot more presentable and acceptable to the general public [16,34,40,41]. Golden Ratio also used in particle swarm optimization [42]. A recent study on ortho-cortex and para-cortex, two types of Cortex in Wool fibre, is conducted to have a better understanding on the relationship between the fibre structure and its properties where the fractal dimension of wool fibre shows the existence of Golden Mean in its structure [43].…”

An Improved Particle Swarm Optimization Algorithm

“…Parsopoulos and Vrahatis [4] proposed the vector evaluated particle swarm optimization algorithm, which improved the optimization performance significantly. Fan [5] applied the golden strategy to his new combined particle swarm optimization algorithm. The strategy can substitute the partial perfect particles of local evolution for the equivalent worse particles of global evolution model.…”