Steady-State Sine Cosine Genetic Algorithm Based Chaotic Search for Nonlinear Programming and Engineering Applications
作者： A. A. Mousa , M. A. El-Shorbagy , M. A. Farag
DOI: 10.1109/ACCESS.2020.3039882
关键词: Benchmark (computing) 、 Maxima and minima 、 Sine 、 Engineering design process 、 Nonlinear programming 、 Genetic algorithm 、 Local search (optimization) 、 Trigonometric functions 、 Algorithm
摘要: 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 combination of approach (SCA), algorithm (SSGA), search (CS), named as chaos-enhanced SCA with SSGA. The proposed integrates SSGA’s exploitation ability SCA’s exploration local capability CS. performance the new works in two different stages. Firstly, SSGA start together to increase tendencies. Secondly, CS used improve approximate solution obtained from first stage reach global solution. Hence, will be more robust it avoids trapping into minima addition speed process rapid convergence towards efficiency verified by using solve 32 well-known benchmark problems design problems. Simulation results show that competitive better most cases comparison others.
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