Special Issue on Developments and Applications of Fireworks Algorithm for International Journal of Swarm Intelligence Research (IJSIR) 6(2)

GUEST EDITORIAL PREFACE

Special Issue on Developments and Applications of Fireworks Algorithm

Ying Tan (Department of Machine Intelligence, Peking University, Beijing, China),
Andreas Janecek (Faculty of Computer Science, University of Vienna, Vienna, Austria),
Jianhua Liu (School of Information Science and Engineering, Fujian University of Technology, Fujian, China)

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ARTICLE 1

Attract-Repulse Fireworks Algorithm and its CUDA Implementation Using Dynamic Parallelism

Ke Ding (Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China & Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, China), Ying Tan (Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China & Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, China)

Fireworks Algorithm (FWA) is a recently developed Swarm Intelligence Algorithm (SIA), which has been successfully used in diverse domains. When applied to complicated problems, many function evaluations are needed to obtain an acceptable solution. To address this critical issue, a GPU-based variant (GPU-FWA) was proposed to greatly accelerate the optimization procedure of FWA. Thanks to the active studies on FWA and GPU computing, many advances have been achieved since GPU-FWA. In this paper, a novel GPU-based FWA variant, Attract-Repulse FWA (AR-FWA), is proposed. AR-FWA introduces an efficient adaptive search mechanism (AFW Search) and a non-uniform mutation strategy for spark generation. Compared to the state-of-the-art FWA variants, AR-FWA can greatly improve the performance on complicated multimodal problems. Leveraging the edge-cutting dynamic parallelism mechanism provided by CUDA, AR-FWA can be implemented on the GPU easily and efficiently.

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ARTICLE 2

Parallelization of Enhanced Firework Algorithm using MapReduce

Simone A. Ludwig (Department of Computer Science, North Dakota State University, Fargo, ND, USA), Deepak Dawar (Department of Computer Science, North Dakota State University, Fargo, ND, USA)

Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work investigates the scalability of the parallelization of the Enhanced FireWorks Algorithm (EFWA), which is an improved version of FWA. The authors use the MapReduce platform for parallelizing EFWA, investigate its ability to scale, and report on the speedup obtained on different benchmark functions for increasing problem dimensions.

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ARTICLE 3

Analytics on Fireworks Algorithm Solving Problems with Shifts in the Decision Space and Objective Space

Shi Cheng (Division of Computer Science, The University of Nottingham Ningbo, Ningbo, China), Quande Qin (College of Management, Shenzhen University, Shenzhen, China), Junfeng Chen (Hohai University, Changzhou, China), Yuhui Shi (Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China), Qingyu Zhang (Shenzhen University, Shenzhen, China)

Fireworks algorithms for solving problems with the optima shift in decision space and/or objective space are analyzed in this paper. The standard benchmark problems have several weaknesses in the research of swarm intelligence algorithms for solving single objective problems. The optimum is in the center of search range, and is the same at each dimension of the search space. The optimum shift in decision space and/or objective space could increase the difficulty of problem solving. A mapping strategy, modular arithmetic mapping, is utilized in the original fireworks algorithm to handle solutions out of search range. The solutions are implicitly guided to the center of search range for problems with symmetrical search range via this strategy. The optimization performance of fireworks algorithm on shift functions may be affected by this strategy. Four kinds of mapping strategies, which include mapping by modular arithmetic, mapping to the boundary, mapping to stochastic region, and mapping to limited stochastic region, are compared on problems with different dimensions and different optimum shift range. From experimental results, the fireworks algorithms with mapping to the boundary, or mapping to limited stochastic region obtain good performance on problems with the optimum shift. This is probably because the search tendency is kept in these two strategies. The definition of population diversity measurement is also proposed in this paper, from observation on population diversity changes, the useful information of fireworks algorithm solving different kinds of problems could be obtained.

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ARTICLE 4

Binary Fireworks Algorithm Based Thermal Unit Commitment

Lokesh Kumar Panwar (MNIT, Jaipur, India), Srikanth Reddy K (MNIT, Jaipur, India), Rajesh Kumar (MNIT, Jaipur, India)

This paper presents the first application of fireworks algorithm to solve thermal unit commitment and scheduling problem. The scheduling problem accompanied by many constraints i.e., equality constraints like load balance and inequality constraints like system reserve and bounds like power generation, up/down time and ramp rate limits, finally shapes into a complex optimization problem. In this work, the scheduling and commitment problem is solved using binary fireworks algorithm (BFWA), which mimics explosion of fireworks in the sky to define search space and distance between associated sparks to evaluate global minimum. Further, the effectiveness of fireworks pertaining to problem dimension, wide range of generation units from 10 to 100 are considered and evaluated. In addition, simulations results are compared to the existing optimization techniques in literature used for unit commitment and scheduling problem and it is observed that, BFWA is superior to some of the profound existing algorithms in achieving near optimal scheduling.

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www.igi-global.com/article/binary-fireworks-algorithm-based-thermal-unit-commitment/133580
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ARTICLE 5

Application of Fireworks Algorithm in Gamma-Ray Spectrum Fitting for Radioisotope Identification

Miltiadis Alamaniotis (Nuclear Engineering Program, University of Utah, Salt Lake City, UT, USA & Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA), Chan K. Choi (School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA), Lefteri H. Tsoukalas (School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA)

Identification of radioisotopic signature patterns in gamma-ray spectra is of paramount importance in various applications of gamma spectroscopy. Therefore, there are several active research efforts to develop accurate and precise methods to perform automated spectroscopic analysis and subsequently recognize gamma-ray signatures. In this work, the authors present a new method for radioisotope identification in gamma-ray spectra obtained with a low resolution radiation detector. The method fits the obtained spectrum with a linear combination of known template signature patterns. Coefficients of the linear combination are evaluated by computing the solution of a single objective optimization problem, whose objective is the Theil-1 inequality coefficient. Optimization of the problem is performed by the Fireworks Algorithm, which identifies a set of coefficients that minimize the Theil-1 value. The computed coefficients are statistically tested for being significantly different than zero or not, and if at least one is found to be zero then the Fireworks Algorithm is used to reiterate fitting using the non-zero templates. Fitting iterations are continued up to the point that no linear coefficients are found to be zero. The output of the method is a list that contains the radioisotopes that have been identified in the measured spectrum. The method is tested on a set of both simulated and real experimental gamma-ray spectra comprised of a variety of isotopes, and compared to a multiple linear regression fitting, and genetic algorithm Theil-1 based fitting. Results demonstrate the potentiality of the Fireworks Algorithm based method, expressed as higher accuracy and similar precision over the other two tested methodologies for radioisotope signature pattern identification in the framework of gamma-ray spectrum fitting.

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www.igi-global.com/article/application-of-fireworks-algorithm-in-gamma-ray-spectrum-fitting-for-radioisotope-identification/133581
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