A Cooperative Framework for Fireworks Algorithm

Zheng, Shaoqiu; Li, Junzhi; Janecek, Andreas; Tan, Ying

doi:10.1109/tcbb.2015.2497227

Cited by 90 publications

(34 citation statements)

References 49 publications

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“…p (x i ) = R (x i ) Figure 1 shows the general flowchart of the FWA, where the original author of the method [7] described each equation that was presented above and that we can find in more detail in the original paper [11] and other variants presented in [15][16][17].…”

Section: Selection Of Locationsmentioning

confidence: 99%

A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

Barraza

Rodríguez

Castillo

et al. 2018

Journal of Optimization

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The main aim of this paper is to present a new hybridization approach for combining two powerful metaheuristics, one inspired by physics and the other one based on bioinspired phenomena. The first metaheuristic is based on physics laws and imitates the explosion of the fireworks and is called Fireworks Algorithm; the second metaheuristic is based on the behavior of the grey wolf and belongs to swarm intelligence methods, and this method is called the Grey Wolf Optimizer algorithm. For this work we studied and analyzed the advantages of the two methods and we propose to enhance the weakness of both methods, respectively, with the goal of obtaining a new hybridization between the Fireworks Algorithm (FWA) and the Grey Wolf Optimizer (GWO), which is denoted as FWA-GWO, and that is presented in more detail in this work. In addition, we are presenting simulation results on a set of problems that were tested in this paper with three different metaheuristics (FWA, GWO, and FWA-GWO) and these problems form a set of 22 benchmark functions in total. Finally, a statistical study with the goal of comparing the three different algorithms through a hypothesis test ( -test) is presented for supporting the conclusions of this work.

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Section: Selection Of Locationsmentioning

confidence: 99%

A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

Barraza

Rodríguez

Castillo

et al. 2018

Journal of Optimization

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“…CoFFWA was proposed in 2017 by Zheng et al as a modification of FWA. 18 FWA is a kind of swarm intelligence algorithm inspired by fireworks; solutions are searched by launching fireworks in search space. 15 The concept of CoFFWA is illustrated in Figure 1.…”

Section: Overview Of Coffwamentioning

confidence: 99%

“…CoFFWA 18 With CoFFWA, multiple sparks are generated through explosion of fireworks in search space so as to look for new solutions. In so doing, fireworks standing for candidate solutions are provided with fitness values.…”

Section: Overview Of Coffwamentioning

confidence: 99%

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Development of Discrete CoFFWA for Distribution Network Reconfigurations

Ikegami

Mori

2018

IEEJ Trans. PE

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In this article, an efficient Discrete CoFFWA method is proposed to deal with distribution network reconfigurations. It is useful for minimizing the active power network losses by switching on/off sectionalizing switches and shunt capacitors. The mathematical formulation may be expressed as one of combinatorial optimization problems. This article presents a discrete version of cooperative framework for fireworks algorithm (CoFFWA) of advanced metaheuristics to solve distribution network reconfigurations efficiently. It is one of the efficient Fireworks Algorithms that imitate the behavior of firework explosion. To improve the solution accuracy, CoFFWA makes use of new strategies such as the independent selection method and the crowdnessavoiding cooperative strategy. The proposed method transforms CoFFWA into discrete one with multipoint mutation. The proposed method is successfully applied to the IEEE 69-node and 119-node distribution networks. K E Y W O R D SCoFFWA, distribution automation, distribution networks, fireworks algorithm, metaheuristics, network reconfigurations, optimization Electr Eng Jpn. 2018;205:55-62.

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“…Authors of the original fireworks algorithm continued to work on its improvement. The results are four upgraded versions of the FWA, enhanced FWA [39], corporative framework for FWA [40], FWA with enhanced interaction and the latest update is guided FWA (GFWA) [16]. In [38] they proposed another improvement of the enhanced fireworks algorithm (EFWA) based on an adaptive dynamic local search mechanism.…”

Section: Firework Algorithmmentioning

confidence: 99%

Adjusted Fireworks Algorithm Applied to Retinal Image Registration

Tuba¹,

Tuba²,

Dolicanin³

2017

SIC

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Abstract:Image registration is an important problem in image processing, especially in medical imaging. It is a hard optimization problem with objective function that is not appropriate for deterministic mathematical optimization. In this paper we propose a bi-resolution algorithm for rigid image registration based on recent fireworks algorithm. We tested our approach on retinal images from standard benchmark datasets and compared the results with other approaches from literature. Our proposed algorithm succeeded in registering images in all cases, and was more accurate than other compared methods.

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A Cooperative Framework for Fireworks Algorithm

Cited by 90 publications

References 49 publications

A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

Development of Discrete CoFFWA for Distribution Network Reconfigurations

Adjusted Fireworks Algorithm Applied to Retinal Image Registration

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