  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.
