Recently, numerous meta-heuristic based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity etc. However, several optimization algorithms namely firefly algorithm, sine cosine algorithm, particle swarm optimization algorithm have few drawbacks such as computational complexity, convergence speed etc. So to overcome such shortcomings, this paper aims in developing a novel Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine cosine algorithm (SCA) and the Firefly (FF) algorithms are integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping is selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.
翻译：最近,考虑了许多基于超重法的方法,以减少若干现有方法的计算复杂性,这些方法包括微妙的衍生法、非常大的内存空间要求、初始价值敏感度等。然而,若干优化算法,如火蝇算法、正弦共弦算法、粒子群优化算法等,几乎没有什么缺点,如计算复杂度、趋同速度等。因此,为了克服这些缺点,本文件旨在开发一个新的CSCF算法(CSCF)算法(CSCF),该算法有许多变量,以解决优化问题。在这里,两个算法的混乱形式,即正弦正弦正弦算法(SCA)和Firefly(FF)算法,已经整合,以提高趋同速度和效率,从而最大限度地减少若干复杂问题。此外,拟议的CSCFF方法是在不同的混乱阶段运行的,并选择了包含最佳混乱绘图的最佳混乱变方。然后,利用许多混乱的基准功能来审查CFCF算法的系统性功能。最后,基于工程设计的问题的模拟结果被证明为证明拟议的算法的效率、稳健和有效。
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