CSCF: a chaotic sine cosine firefly algorithm for practical application problems

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, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. 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 and the firefly algorithms is 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 are 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.

Automated summary

This paper introduces a novel chaotic sine-cosine firefly (CSCF) algorithm and its variants to solve optimization problems. By integrating the chaotic form of the sine-cosine algorithm and firefly algorithm, the algorithm aims to improve convergence speed and efficiency while minimizing complexity issues. The performance of the CSCF algorithm is verified using various chaotic benchmark functions.