CSCF: a chaotic sine cosine firefly algorithm for practical application problems
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ORIGINAL ARTICLE
CSCF: a chaotic sine cosine firefly algorithm for practical application problems Bryar A. Hassan1,2 Received: 24 April 2020 / Accepted: 26 October 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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, 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. Keywords CSCF Engineering design problems Variants Chaotic maps Optimization function
1 Introduction In recent decades, numerous algorithms have been proposed to overcome various optimization problems in the field of engineering [1]. These optimization problems determine the value of a few parameters under specific circumstances for optimizing the objective function. In general, the objective function is a specific characteristic that provides a minimal or maximal solution based on the problem. Therefore, to attain best optimistic solutions, the optimizations are broadly utilized in various applications such as industrial design, manufacture design, design analysis and engineering design [2]. There occur several optimization problems while obtaining optimal solution, & Bryar A. Hassan [email protected] 1
Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq
2
Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq
and these optimization problems are categorized into several types namely dynamic or static, continuous or discrete, single-objective or multi-objective as well as constrained or unconstrained. Hence, to enhance the accuracy and the efficiency of such optimization problems, several research scholars depend upon meta-heuristic algorithms for easy implementation, gradient information and to avoid or bypass local optimization problem [3]. At the same time, the meta-h
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