Improved fireworks algorithm with information exchange for function optimization

The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta-heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are p...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 163; pp. 82 - 90
Main Authors: Cheng, Rong, Bai, Yanping, Zhao, Yu, Tan, Xiuhui, Xu, Ting
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.01.2019
Elsevier Science Ltd
Subjects:
Algorithms
Automotive engineering
Comparative advantage
Competition
Competitive advantage
Control systems design
Controllers
Design engineering
Design optimization
Engineering
Expert systems
Fireworks
Fireworks algorithm
Function optimization
Global optimization
Heuristic
Heuristic methods
Information sharing
LQR controller
Optimization
Optimization algorithms
Suspension
Swarm intelligence
Fireworks algorithm
Swarm intelligence
Function optimization
LQR controller
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta-heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are presented after an analysis of the drawbacks of EFWA. These improvements are a new explosion scheme, GS-Gaussian explosion operator, and deep information exchange strategy. The proposed IFWA is tested on 23 benchmark function optimization problems and a real engineering problem, namely, optimal controller design for automotive active suspension. Optimization results prove that IFWA has competitive advantage compared with EFWA and other popular meta-heuristic algorithms and demonstrates the potential to solve real problems effectively.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.08.016