Balancing composite motion optimization

Abstract Meta-heuristic algorithms play an important role in the optimization field thanks to their robustness and programming simplicity. Many meta-heuristic methods have been devised in recent years. Inspired by nature, they usually simulate natural or human-specific phenomena in a better way. A large amount of them are based on complicated behaviors requiring several implementation steps and algorithm-specific control parameters, which impedes users and limits solutions to different types of optimization problems. Hence, designing effective simple and parameter-free optimization methods attracts much attention. In this paper, we propose a novel population-based optimization algorithm based on balancing composite motions (BCMO). The core idea is balancing composite motion properties of individuals in solution space. Equalizing global and local searches via a probabilistic selection model creates a movement mechanism of each individual. Four test suites selected in the literature, which vary from numerical benchmarks to practical problems, to demonstrate the performance of BCMO include: (1) 23 classical benchmark functions, (2) CEC 2005 benchmark functions, (3) CEC 2014 benchmark functions, and (4) 3 real engineering design problems. The statistical results reveal the promising performance and application of BCMO in a variety of optimization and practical problems with constrained and unknown search spaces.