CSCF：一种求解实际问题的混沌正余弦萤火虫算法
CSCF: a chaotic sine cosine firefly Algorithm for practical application problems
摘要：
近年来，许多基于元启发式的方法被用来降低现有方法的计算复杂性，这些方法包括复杂的推导、非常大的存储空间需求、初始值敏感性等。然而，萤火虫算法、正弦余弦算法、粒子群优化算法等几种优化算法具有诸如计算复杂性、收敛速度等缺点。因此，为了克服这些缺点，摘要提出了一种新的多变量混沌正余弦萤火虫（CSCF）算法来求解优化问题。在这里，混沌形式的两个算法，即正弦余弦算法（SCA）和萤火虫（FF）算法集成，以提高收敛速度和效率，从而最大限度地减少一些复杂性问题。此外，建议的CSCF方法在各种混沌相位下运行，并选择包含最佳混沌映射的最佳混沌变量。然后利用大量的混沌基准函数对CSCF算法的系统性能进行了测试。最后，基于工程设计的问题的仿真结果表明，所提出的算法的效率，鲁棒性和有效性。
英文摘要：
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.
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