Abstract
The choice of the best optimization algorithm is a hard issue, and it sometime depends on specific problem. The Gravitational Search Algorithm (GSA) is a search algorithm based on the law of gravity, which states that each particle attracts every other particle with a force called gravitational force. Some revised versions of GSA have been proposed by using intelligent techniques. This work proposes some GSA versions based on fuzzy techniques powered by evolutionary methods, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), to improve GSA. The designed algorithms tune a suitable parameter of GSA through a fuzzy controller whose membership functions are optimized by GA, PSO and DE. The results show that Fuzzy Gravitational Search Algorithm (FGSA) optimized by DE is optimal for unimodal functions, whereas FGSA optimized through GA is good for multimodal functions.
Highlights
Solving optimization problems using exhaustive search techniques is not always the best way.in the problems with huge dimensional space search, the classical optimization algorithms do not provide a suitable solution
The results show that, both over 30 and 16 runs, Fuzzy Gravitational Search Algorithm (FGSA)-Differential Evolution (DE) is optimal for unimodal functions, whereas FGSA-Genetic Algorithms (GA) is good for multimodal functions
Many researches proposed revised versions of Gravitational Search Algorithm (GSA) to reduce the time for finding the global optimum
Summary
Solving optimization problems using exhaustive search techniques is not always the best way. In the problems with huge dimensional space search, the classical optimization algorithms do not provide a suitable solution. Many researchers take on the problem to optimize objective functions by designing algorithms inspired by the behaviors of natural phenomena. The issue of tuning some parameters of a search algorithm is one of the most important areas of research in evolutionary computation [1]. This research line was followed by Montiel et al [2] and Castillo et al [3], which treated the idea of adjusting an evolutionary algorithm. Among the intelligent evolutionary optimization methods, the Genetic Algorithms (GA) [4]
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