Editorial Nature-Inspired Algorithms for Real-World ...Editorial Nature-Inspired Algorithms for...
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Editorial Nature-Inspired Algorithms for Real-World Optimization Problems Wei Fang, 1 Xiaodong Li, 2 Mengjie Zhang, 3 and Mengqi Hu 4 1 School of IoT Engineering, Jiangnan University, No. 1800, Lihu Avenue, Wuxi 214122, China 2 School of Computer Science and IT, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia 3 Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand 4 University of Illinois at Chicago, 842 W. Taylor Street, 2039 ERF, Chicago, IL 60661, USA Correspondence should be addressed to Wei Fang; [email protected] Received 26 August 2015; Accepted 26 August 2015 Copyright © 2015 Wei Fang et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Nature-inspired algorithms are a set of novel problem-solving methodologies and approaches and have been attracting con- siderable attention for their good performance. Representa- tive examples of nature-inspired algorithms include artificial neural networks (ANN), fuzzy systems (FS), evolutionary computing (EC), and swarm intelligence (SI), and they have been applied to solve many real-world problems. Despite the popularity of nature-inspired algorithms, many challenges remain which require further research efforts. e contributions presented in this special issue include some latest developments of nature-inspired algorithms, such as genetic algorithm, particle swarm optimization, ant colony optimization, migrating birds optimization, neural networks, gravitational search algorithm, and their applications. Several real-world optimization problems have been studied by several nature-inspired algorithms. K. G. Ing at al. present the application of gravitational search algorithm (GSA) in determining the optimal daily configuration of distribution network based on photovoltaic generation and system loading. e distribution network reconfiguration problem is formulated as a minimization problem to minimize the power loss of the distribution. Experimental results show that GSA with selection approach is a simple yet effective technique to minimize total daily power loss. e work of E. Lalla-Ruiz et al. studies the improved migrating birds optimization (MBO) approach for solving two seaside problems, which are the Dynamic Berth Alloca- tion Problem (DBAP) and Quay Crane Scheduling Problem (QCSP). MBO approach can solve these two problems with high-quality solutions with a small short computational cost, which makes this technique a competitive method for frequently seaside operations either performed individually or embedded into real decision-support systems. e paper by I. G. Hidalgo et al. integrates genetic algorithm (GA) with Strength Pareto Evolutionary Algorithm (SPEA) and ant colony optimization (ACO) to deal with the short-term scheduling problem. e problem is solved by the proposed two hybrid approaches in two phases. e experimental results on two hydroelectric plants show that both approaches produce good performance for the optimal dynamic dispatch in the short-term operation of hydroelectric plants. S. Demirel et al. focus on the optimal design of ultra- wideband (UWB) low-noise amplifier (LNA) based on the support vector regression machine (SVRM) microstrip line model. Particle swarm optimization (PSO) algorithm has been employed in the solving procedure for two parameters resulting in good performance in terms of accuracy and fast convergence. F. Kamaruzaman et al. propose the coincidence detection (CD) classifier with two learning methods based on the Spiking Neural Network (SNN). e proposed method can produce an output spike pattern from an input pair identical Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2015, Article ID 359203, 2 pages http://dx.doi.org/10.1155/2015/359203
Transcript of Editorial Nature-Inspired Algorithms for Real-World ...Editorial Nature-Inspired Algorithms for...
EditorialNature-Inspired Algorithms for Real-WorldOptimization Problems
Wei Fang,1 Xiaodong Li,2 Mengjie Zhang,3 and Mengqi Hu4
1School of IoT Engineering, Jiangnan University, No. 1800, Lihu Avenue, Wuxi 214122, China2School of Computer Science and IT, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia3Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington,P.O. Box 600, Wellington 6140, New Zealand4University of Illinois at Chicago, 842 W. Taylor Street, 2039 ERF, Chicago, IL 60661, USA
Correspondence should be addressed to Wei Fang; [email protected]
Received 26 August 2015; Accepted 26 August 2015
Copyright © 2015 Wei Fang et al.This is an open access article distributed under the Creative CommonsAttribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nature-inspired algorithms are a set of novel problem-solvingmethodologies and approaches and have been attracting con-siderable attention for their good performance. Representa-tive examples of nature-inspired algorithms include artificialneural networks (ANN), fuzzy systems (FS), evolutionarycomputing (EC), and swarm intelligence (SI), and they havebeen applied to solve many real-world problems. Despite thepopularity of nature-inspired algorithms, many challengesremain which require further research efforts.
The contributions presented in this special issue includesome latest developments of nature-inspired algorithms, suchas genetic algorithm, particle swarm optimization, ant colonyoptimization, migrating birds optimization, neural networks,gravitational search algorithm, and their applications. Severalreal-world optimization problems have been studied byseveral nature-inspired algorithms.
K. G. Ing at al. present the application of gravitationalsearch algorithm (GSA) in determining the optimal dailyconfiguration of distribution network based on photovoltaicgeneration and system loading. The distribution networkreconfiguration problem is formulated as a minimizationproblem to minimize the power loss of the distribution.Experimental results show that GSA with selection approachis a simple yet effective technique to minimize total dailypower loss.
The work of E. Lalla-Ruiz et al. studies the improvedmigrating birds optimization (MBO) approach for solving
two seaside problems, which are the Dynamic Berth Alloca-tion Problem (DBAP) and Quay Crane Scheduling Problem(QCSP). MBO approach can solve these two problems withhigh-quality solutions with a small short computationalcost, which makes this technique a competitive method forfrequently seaside operations either performed individuallyor embedded into real decision-support systems.
The paper by I. G. Hidalgo et al. integrates geneticalgorithm (GA)with StrengthPareto EvolutionaryAlgorithm(SPEA) and ant colony optimization (ACO) to deal withthe short-term scheduling problem. The problem is solvedby the proposed two hybrid approaches in two phases.The experimental results on two hydroelectric plants showthat both approaches produce good performance for theoptimal dynamic dispatch in the short-term operation ofhydroelectric plants.
S. Demirel et al. focus on the optimal design of ultra-wideband (UWB) low-noise amplifier (LNA) based on thesupport vector regression machine (SVRM) microstrip linemodel. Particle swarm optimization (PSO) algorithm hasbeen employed in the solving procedure for two parametersresulting in good performance in terms of accuracy and fastconvergence.
F. Kamaruzaman et al. propose the coincidence detection(CD) classifier with two learning methods based on theSpiking Neural Network (SNN). The proposed method canproduce an output spike pattern from an input pair identical
Hindawi Publishing CorporationJournal of Applied MathematicsVolume 2015, Article ID 359203, 2 pageshttp://dx.doi.org/10.1155/2015/359203
2 Journal of Applied Mathematics
to the discrete Spike Response Model (SRM) with signifi-cantly lower floating operations and amuch faster processingtime.
The papers included in this special issue are of highquality, hopefullymaking useful contributions to the researcharea of nature-inspired algorithms.
Acknowledgments
Dr. Wei Fang acknowledges the support from the NationalNature Science Foundation of China (Grants nos. 61105128,61170119, and 61373055), the Nature Science Foundationof Jiangsu Province, China (Grants nos. BK20131106,BK20130161, and BK20130160), the Postdoctoral ScienceFoundation of China (Grant no. 2014M560390), theFundamental Research Funds for the Central Universities,China (Grant no. JUSRP51410B), and Six Talent Peaks’Project of Jiangsu Province (Grant no. DZXX-025). As guesteditors, we would like to thank the contributing authors andthe reviewers for their hard work in preparing and reviewingthe submissions.
Wei FangXiaodong Li
Mengjie ZhangMengqi Hu
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