Convex combination search algorithm: A novel metaheuristic optimization algorithm for solving global optimization and engineering design problems

Abstract

In this paper, a novel metaheuristic optimization algorithm (MHOA) called convex combination search (CCS) is proposed as a solution to global optimization problems and engineering design problems. CCS is based on a combination of rules that depend upon the concept of the linear convex combination. These rules are mathematically modeled to guarantee the variety of solutions at the initialization stage and achieve equilibrium between exploitation, exploration capabilities at the generation stage, the algorithm’s convergence, and robustness. A detailed mathematical model of the algorithm is offered. As an advantage for the CCS algorithm, it requires just two parameters which are the population size and the number of generations for determining the global optimal solution of any optimization problem. The effectiveness of the suggested algorithm is investigated on 17 unconstrained multimodal test functions, and 7 constrained benchmark problems having different characteristics. In addition, five engineering design challenges are resolved to confirm the robustness and dependability of CCS in resolving engineering applications. The efficiency and competitiveness of the proposed algorithm were illustrated in comparison with other methods. A statistical analysis of the results has been carried out to illustrate the competitiveness and power effectiveness of the proposed algorithm. Finally, the sensitivity of the CCS parameters is presented to show the sensitivities of these parameters to the performance of the proposed algorithm.