Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion

This paper proposes an improved kinetic-molecular theory optimization algorithm (OKMTOA) by analyzing the characteristics of KMTOA cluster behavior and combining the opposition-based learning strategy with varying accelerated motion in physics. The algorithm first applies different opposition-based learning strategies to the population initialization and iterative process of the algorithm. The two-stage strategy is beneficial to improving the quality of the solution set and accelerating the convergence of the algorithm. Then, based on the concept of varying accelerated motion, the acceleration formula is improved to increase the ability to escape local optimum. The experimental results show that the algorithm has good performance in solution precision, convergence speed and can be well applied to the functions with different shift values.