Multi scenario chaotic transient search optimization algorithm for global optimization technique

Recently, chaotic maps (CMs) have been employed in many optimization algorithms as a motivator to find a better solution to non-convex engineering problems since they can avoid local optima and find the near-optimal solution rapidly. In this article, a metaheuristic, physics-based algorithm called chaotic transient search optimization (CTSO) algorithm is developed to solve 23 benchmark functions, including uni- and multi-modal optimization functions. Nine CMs integrated into the TSO to improve its search capabilities by applying various scenarios for improving the TSO random numbers. Further, the proposed CTSO was compared with the original TSO using the Wilcoxon p-value test, non-parametric sign test, t-test, convergence curves, and elapsed time. Furthermore, the proposed CTSO algorithm has been employed for solving real-life engineering design problems, including coil spring, welded beam, and pressure vessel design, where CTSO performed better than some recent optimization algorithms in finding the best design.