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

TL;DR

This paper introduces a novel Chaotic Sine Cosine Firefly (CSCF) algorithm that combines chaotic variants of existing meta-heuristics to enhance convergence speed and efficiency in solving complex optimization problems.

Contribution

It develops a new hybrid algorithm integrating chaotic sine cosine and firefly algorithms, improving performance over traditional methods.

Findings

CSCF outperforms existing algorithms on benchmark functions.

The algorithm demonstrates robustness in engineering design problems.

Chaotic variants significantly enhance convergence speed.

Abstract

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.