Abstract
Improvement of Firefly Algorithm using Particle Swarm Optimization and Gravitational Search Algorithm
Highlights
Since most real-life problems can be modeled as optimization tasks, many methods and techniques that could tackle such problems were developed
In scenario 5, which is obtained from the integration of scenarios 2–4, both of the total time and the response accuracy has improved simultaneously compared to Standard Firefly Algorithm (SFA)
This paper proposed models for improvement of Firefly algorithm
Summary
Since most real-life problems can be modeled as optimization tasks, many methods and techniques that could tackle such problems were developed. The main purpose of this, is to reduce the running time of the algorithm, but this method can be avoided confusion in choosing the optimal answer due to the high volume of information too Using this GSA feature and combine it with the firefly algorithm, the improved FA (that Introduced in this paper), will use only useful information in problem search processing instead of all the information - effective or ineffective -. Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) are used to improve the performance of Standard FA These algorithms help to improve Movement Operator in problem space search. The Gravitational Search Algorithm (GSA) is a kind of population based stochastic search algorithm which was first proposed by Rashedi et al [17], [18]
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