Steady-State Sine Cosine Genetic Algorithm Based Chaotic Search for Nonlinear Programming and Engineering Applications

This paper proposes a newly meta-heuristic approach, steady-state sine cosine genetic algorithm-based chaotic search, for solving nonlinear programming and engineering applications. It is a combination of sine cosine approach (SCA), steady-state genetic algorithm (SSGA), and chaotic search (CS), and named as chaos-enhanced SCA with SSGA. The proposed approach integrates SSGA's exploitation ability and SCA's exploration ability and local search capability of CS. The performance of the new approach works in two different stages. Firstly, SCA and SSGA start together to increase exploration capability and exploitation tendencies. Secondly, CS used to improve the approximate solution obtained from the first stage and reach the global solution. Hence, the proposed new approach will be more robust as it avoids trapping into local minima in addition to the speed of the search process and rapid convergence towards the global solution. The efficiency of the proposed approach is verified by using it to solve 32 well-known benchmark problems and different engineering design problems. Simulation results show that the proposed approach is competitive and better in most cases as a comparison to others.