The Effect of Information Utilization: Introducing a Novel Guiding Spark in the Fireworks Algorithm

The fireworks algorithm (FWA) is a competitive swarm intelligence algorithm which has been shown to be very useful in many applications. In this paper, a novel guiding spark (GS) is introduced to further improve its performance by enhancing the information utilization in the FWA. The idea is to use...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2017-02, Vol.21 (1), p.153-166
Main Authors: Li, Junzhi, Zheng, Shaoqiu, Tan, Ying
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Explosions
Fireworks
fireworks algorithm (FWA)
guiding spark (GS)
Heuristic algorithms
information utilization
Linear programming
Optimization
Sparks
Swarm intelligence
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Summary:The fireworks algorithm (FWA) is a competitive swarm intelligence algorithm which has been shown to be very useful in many applications. In this paper, a novel guiding spark (GS) is introduced to further improve its performance by enhancing the information utilization in the FWA. The idea is to use the objective function's information acquired by explosion sparks to construct a guiding vector (GV) with promising direction and adaptive length, and to generate an elite solution called a GS by adding the GV to the position of the firework. The FWA with GS is called the guided FWA (GFWA). Experimental results show that the GS contributes greatly to both exploration and exploitation of the GFWA. The GFWA outperforms previous versions of the FWA and other swarm and evolutionary algorithms on a large variety of test functions and it is also a useful method for large scale optimization. The principle of the GS is very simple but efficient, which can be easily transplanted to other population-based algorithms.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2016.2589821