An improved firefly algorithm for numerical optimization problems and it’s application in constrained optimization

Metaheuristic algorithms are successful methods of optimization. The firefly algorithm is one of the known metaheuristic algorithms used in a variety of applications. Recently, a new and efficient version of this algorithm was introduced as NEFA, which indicated a good performance in solving optimiz...

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Bibliographic Details
Published in:Engineering with computers Vol. 38; no. 4; pp. 3793 - 3813
Main Authors: Rezaei, Kamran, Rezaei, Hassan
Format: Journal Article
Language:English
Published: London Springer London 01.08.2022
Springer Nature B.V
Subjects:
Algorithms
Attraction
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Clustering
Computer Science
Computer-Aided Engineering (CAD
Control
Heuristic methods
Math. Applications in Chemistry
Mathematical and Computational Engineering
Optimization
Original Article
Performance enhancement
Performance evaluation
Systems Theory
Mutation
Clustering
Firefly algorithm
Attraction model
ISSN:0177-0667, 1435-5663
Online Access:Get full text
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Summary:Metaheuristic algorithms are successful methods of optimization. The firefly algorithm is one of the known metaheuristic algorithms used in a variety of applications. Recently, a new and efficient version of this algorithm was introduced as NEFA, which indicated a good performance in solving optimization problems. However, the introduced attraction model in this algorithm may not provide good coverage of the search space and thus trap the algorithm in a local optimum. In this paper, a new and efficient improved firefly algorithm called INEFA is proposed to improve the performance of NEFA. In INEFA, a new model of attraction is introduced in which each firefly can be attracted to brighter fireflies located in different areas of the search space, using the clustering concept to classify fireflies. To evaluate the performance of INEFA, it was used to optimize several known benchmark functions. The results were compared with the results of the firefly algorithm and some of its known improvements. The comparison of results indicated the significant power of INEFA compared to the algorithms. It was used to evaluate its application in solving a constrained optimization problem. The comparison results showed that INEFA performs better than most of the compared algorithms.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-021-01412-9