Self-adaptively Commensal Learning-based Jaya Algorithm with Multi-populations and its Application

Abstract

Jaya algorithm is an advanced optimization algorithm, which has been applied to many real-world optimization problems. Jaya algorithm has better performance in some optimization field. However, Jaya algorithm exploration capability is not better. In order to enhance exploration capability of the Jaya algorithm, a self-adaptively commensal learning-based Jaya algorithm with multi-populations (Jaya-SCLMP) is presented in this paper. In Jaya-SCLMP, a commensal learning strategy is used to increase the probability of finding the global optimum, in which the person history best and worst information is used to explore new solution area. Moreover, a multi-populations strategy based on Gaussian distribution scheme and learning dictionary is utilized to enhance the exploration capability, meanwhile every sub-population employed three Gaussian distributions at each generation, roulette wheel selection is employed to choose a scheme based on learning dictionary. The performance of Jaya-SCLMP is evaluated based on 28 CEC 2013 unconstrained benchmark problems. In addition, three reliability problems, i.e. complex (bridge) system, series system and series-parallel system are selected. Compared with several Jaya variants and several state-of-the-art other algorithms, the experimental results reveal that Jaya-SCLMP is effective.

Aerodynamic Analysis and Training Research of an S-Shaped Arc Ball Based on Hydrodynamics

“…The classification method is to divide motor skills into cyclical skills, aperiodic skills, or aperiodic combination skills (in this paper, it refers to the gait signal of football players and the detection node of S-shaped arc ball movement). The coupling analysis of gait dynamic characteristics and key point selection, as well as the vector processing analysis of multiple coupling combinations [18], can achieve the signal optimization and analysis processing of the whole football movement control. Figure 1 illustrates the basic principle of ant colony optimization.…”