A Hybridization of Sine Cosine Algorithm with Steady State Genetic Algorithm for Engineering Design Problems

Abstract

Sine Cosine Algorithm (SCA), a newly proposed optimization approach, has gained the interest of researchers to solve the optimization problems in different fields due to its efficiency and simplicity. As well as a genetic algorithm (GA) has proved its robustness in solving a large variety of complex optimization problems. In this paper, a hybridization of SCA with steady state genetic algorithm (SSGA) is proposed to solve engineering design problems. This approach integrates the merits of exploration capability of SCA and exploitation capability of SSGA to avoid exposure to early convergence, speed up the search process and quick the convergence to best results in a reasonable time. The proposed approach incorporates concepts from SSGA and SCA and generates individuals in a new generation by crossover and mutation operations of SSGA and also by mechanisms of SCA. Efficiency of the proposed algorithm is evaluated using two complex engineering design problems to verify its validity and reliability. Results show that the proposed approach has superior performance compared to other optimizations techniques.