Abstract
Exploitation and exploration are two main search strategies of every metaheuristic algorithm. However, the ratio between exploitation and exploration has a significant impact on the performance of these algorithms when dealing with optimization problems. In this study, we introduce an entire fuzzy system to tune efficiently and dynamically the firefly algorithm parameters in order to keep the exploration and exploitation in balance in each of the searching steps. This will prevent the firefly algorithm from being stuck in local optimal, a challenge issue in metaheuristic algorithms. To evaluate the quality of the solution returned by the fuzzy-based firefly algorithm, we conduct extensive experiments on a set of high and low dimensional benchmark functions as well as two constrained engineering problems. In this regard, we compare the improved firefly algorithm with the standard one and other famous metaheuristic algorithms. The experimental results demonstrate the superiority of the fuzzy-based firefly algorithm to standard firefly and also its comparability to other metaheuristic algorithms.
Highlights
Optimization is the task of finding the best values for the parameters of a given function by maximizing or minimizing the output while satisfying problem constraints
There are several ways, and in this study we propose to use a fuzzy system as the parameter controller to determine the ratio between exploitation and exploration more intelligently
The results demonstrate that the proposed fuzzy controller increased the performance of the Standard Firefly Algorithm (SFA) since FFA outperforms SFA in all the experiments
Summary
Optimization is the task of finding the best values for the parameters of a given function by maximizing or minimizing the output while satisfying problem constraints. Metaheuristic algorithms are considered among the most practical approaches for solving optimization problems. These algorithms algorithms have been developed based on biological, physical processes, chemical and swarm intelligence. Metaheuristic algorithms can handle a wide variety of real-world optimization applications that are basically difficult and in some cases impractical to be solved by classical methods (Yang, X.S., 2010). Even though these algorithms find the best solution in an excellent running time, they do not always guarantee the solution optimality (Yang, X.S., 2010)
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