Abstract
To address the challenge of local optimization and step size factor parameter setting in the full attraction model of the firefly algorithm (FA), this paper introduces level-based attraction and variable step size to FA. In the level-based attraction model, fireflies are firstly grouped in different levels according to the brightness, and each firefly randomly selects two fireflies from a higher level to learn from. By using the variable step size strategy, the searching step size decreases with the number of iterations. Herein, the level-based attracting model can increase the diversity of individual learning and improve the ability to jump out of local optimization. Meanwhile, dynamic adjustment of step size can balance the detection and development ability of the algorithm and improve the optimization accuracy of the algorithm. To evaluate the effectiveness of the proposed algorithm, comparisons are drawn against optimized FA, particle swarm optimization algorithm, artificial bee colony algorithm and differential evolution algorithm on a variety of test function sets.
Highlights
In solving complex optimization problems, metaheuristic methods show high efficiency
Zhao et al.: firefly algorithm (FA) Based on Level-Based Attracting and Variable Step Size iterative process, each firefly only randomly selects a brighter individual to learn from, avoiding the oscillation caused by multiple movements of the firefly
This paper addressed the shortcomings of the full attraction model and step size factor in FA, and proposed a solution with level-based attracting model and variable step size
Summary
In solving complex optimization problems, metaheuristic methods show high efficiency. J. Zhao et al.: FA Based on Level-Based Attracting and Variable Step Size iterative process, each firefly only randomly selects a brighter individual to learn from, avoiding the oscillation caused by multiple movements of the firefly. Research shows that chaotic mapping can increase the detection ability in the search process of the algorithm and prevent the algorithm from falling into local optimization prematurely [32]–[34] To this end, different chaotic maps are used to improve the step size factor to improve the diversity of the firefly population. A small number of fireflies are scattered in the search space, which shows that the level-based attracting model can improve the ability of individual escaping from local optimization. The detailed flow of the proposed technique is summarized in Algorithm 1
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
