A review of chaos-based firefly algorithms: Perspectives and research challenges
نویسندگان
چکیده
The firefly algorithm is a member of the swarm intelligence family of algorithms, which have recently showed impressive performances in solving optimization problems. The fire-fly algorithm, in particular, is applied for solving continuous and discrete optimization problems. In order to tackle different optimization problems efficiently and fast, many variants of the firefly algorithm have recently been developed. Very promising firefly versions use also chaotic maps in order to improve the randomness when generating new solutions and thereby increasing the diversity of the population. The aim of this review is to present a concise but comprehensive overview of firefly algorithms that are enhanced with chaotic maps, to describe in detail the advantages and pitfalls of the many different chaotic maps, as well as to outline promising avenues and open problems for future research. In the past, facing the real-world optimization problems was in the domain of mathematicians and engineers. They developed many mathematical methods for solving the optimization problems. The first algorithms solved the problems exactly by enumerating all the possible solutions, but rapidly algorithms for approximate (heuristic) solving of these problem have been emerged because of a huge time complexities of the exact methods. One of the more promising algorithms today are swarm intelligence (SI) based algorithms inspired with a collective behavior of some simple unintelligent insects or animals, who work together in order to be capable of solving the complex problems. For instance, a foraging of insects connects social living bees, ants and termites. In nature, one individual cannot survive, but when living together in colonies, individuals are stronger and thus capable of performing very complex tasks (e.g., huge mounds by termites). These colonies of insects act as decentralized, and self-organized systems that prevents a single insect to act alone. A firefly algorithm (FA) is one of the younger member of SI-based algorithms that was introduced by Yang in [40] at 2008. Since its introduction, many researchers began working with FA. At the beginning, some modified variants were proposed that were applied for solving the continuous optimization [43], multimodal [41], constrained optimization [30], and later also for real-world problems [47,36,16,25]. Competition of FA with other well-known meta-heuristics led to the development of more robust and sophisticated FA variants. For example, Fister et al. in [12] proposed a memetic self-adaptive FA
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ورودعنوان ژورنال:
- Applied Mathematics and Computation
دوره 252 شماره
صفحات -
تاریخ انتشار 2015