EditorialSwarm Intelligence in Engineering 2014

Baozhen Yao,1 Fang Zong,2 Bin Yu,3 and Rui Mu4

1School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China2College of Transportation, Jilin University, Changchun 130022, China3Transportation Management College, Dalian Maritime University, Dalian 116026, China4Delft University of Technology, NL-2628 BX Delft, Netherlands

Correspondence should be addressed to Bin Yu; ybzhyb@163.com

Received 19 November 2014; Accepted 19 November 2014

Copyright © 2015 Baozhen Yao et al. This 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.

Swarm intelligence (SI) is an artificial intelligence techniquebased on the study of cooperation behaviors of simpleindividuals (e.g., ant colonies, bird flocking, animal herding,and bees gathering honey) in various decentralized systems.The population, which consists of simple individuals, canusually solve complex tasks in nature by individuals inter-acting locally with one another and with their environments.Although a simple individual’s behavior is primarily char-acterized by autonomy, distributed functioning, and self-organizing capacities, local interactions among the individ-uals often lead to a global optimal solution.Therefore, swarmintelligence is a promising way to develop powerful solutionmethods for optimization problems in engineering.

However, for large and complex engineering problems, SIalgorithms often consume toomuch computation time due tothe stochastic features of their searching approaches. For thisreason, there is a potential requirement to develop efficientalgorithms that are able to find solutions under limitedresources, time, and money in real-world applications.

This special issue is aimed at highlighting the mostsignificant recent developments on the topics of SI and toapply SI algorithms in real-life scenarios. Papers selected forthis special issue present new findings and insights into thisfield. A broad range of topics are discussed, especially inthe following areas: benchmarking and evaluation of new SIalgorithms, convergence proof for SI algorithms, comparativetheoretical and empirical studies on SI algorithms, and SIalgorithms for real-world application.

Some works focus on the application of SI in the area oftraffic and transportation. For example, in the paper entitled“Road network vulnerability analysis based on improved ant

colony algorithm,” Y. Wang et al. developed a novel approachbased on an improved ant colony algorithm (IACA) toevaluate the vulnerability as well as identify the criticalinfrastructures of a real-world road network.They proved thehigh computational efficiency of the IACA-based approachwhen applied in large-scale networks and presented thedependence of road network vulnerability on the rationbetween traffic demand and road capacity.

In the paper titled “Hybridmodel for early onset predictionof driver fatigue with observable cues,” M. Zhang et al. pre-sented a hybrid model for the early onset prediction of driverfatigue. The hybrid model consists of three submodels whichseparately solve the three stages of the prediction problem.The support vector machine (SVM) is incorporated in thehybrid model for predicting the early onset driver fatiguestate, while the genetic algorithm is used for optimizingparameters in the SVM. The approach has a good perfor-mance in the numerical test for the early onset prediction ofdriver fatigue.

In the paper titled “Real-time arterial coordination controlbased on dynamic intersection turning fractions estimationusing genetic algorithm,” P. Jiao et al. established a real-time arterial coordination control model based on dynamicintersection turning fractions estimation. They developed animproved genetic algorithm to solve the dynamic intersectionturning fractions estimation model, the solution of whichwas the inputs of the real-time arterial coordination controlmodel. To solve the real-time arterial coordination controlmodel, the authors proposed another improved genetic algo-rithm. Simulation experiment based on actual data provedthe superiority of their approach.

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015, Article ID 858901, 4 pageshttp://dx.doi.org/10.1155/2015/858901

2 Mathematical Problems in Engineering

In the paper entitled “Study of on-ramp PI controller basedon dural group QPSO with different well centers algorithm,” T.Wu et al. proposed a new quantum behaved particle swarmoptimization (QPSO) algorithm, which is the dual-groupQPSO with different well centers (DWC-QOSO) algorithm.This novel algorithm could slow down the disappearance ofpopulation diversity and thus improve the global searchingability. The successful application of the DWC-QOSO algo-rithm in the on-ramp traffic control simulation indicated itspowerful potential in the study of on-ramp PI controller.

In the paper entitled “Decision of multimodal trans-portation scheme based on swarm intelligence,” K. Lei etal. considered three elements of transportation costs, time,and risks when establishing a multimodal transportationscheme decisionmodel. To solve themodel, they proposed animproved SI algorithm combining particle swarm optimiza-tion (PSO) and ant colony (AC) algorithm. The numericaltest showed that their algorithm inherited the advantages ofboth PSO and AC and had good ability to solve the decisionproblem of multimodal transportation scheme.

Also, the SI is discussed in the area of computer science.In the paper entitled “Multipeak mean based optimizedhistogrammodification framework using swarm intelligence forimage contrast enhancement,” P. Babu et al. proposed a PSObased approach to enhance the contrast and preserve essen-tial details for any given image. They divided the histogramof an image into two subhistograms. The two subhistogramscan be modified by optimal enhancement parameters whichare found by the particle swarm optimization algorithm.

In the paper entitled “Binary structuring elements decom-position based on an improved recursive dilation-union modeland RSAPSOmethod,” Y. Zhang et al. developed an improveddilation-union model for decomposing the structuring ele-ments of any given shape, and they used the restartedsimulated annealing particle swarm optimization as the algo-rithm of their model. Experiments showed that the proposedmethod outperformed most existing methods.

In the paper entitled “Applications of PCA and SVM-PSObased real-time face recognition system,” M.-Y. Shieh et al.used the support vector machine (SVM) which could be anoptimal classifier of image recognition system to improve thevalidity of real-time face recognition systems. The particleswarm optimization is used by the authors to implement afeature selection, results of which are then input to the SVMfor calculating the fitness value of classification.

There are also some works focusing on the applicationof SI in the operation research and management science. Inthe paper entitled “Core business selection based on ant colonyclustering algorithm,” Y. Lan et al. proposed a method basedon the ant colony clustering algorithm to identify the corebusiness of an enterprise. By applying themethod, the authorssuccessfully found the core business of Tianjin Port from itsten main businesses.

In the paper entitled “A framing link based tabu searchalgorithm for large-scale multidepot vehicle routing problems,”X. Zhang et al. presented a framing link based Tabu searchalgorithm for solving a large-scale multidepot vehicle routingproblem (LSMDVRP). Framing links are extracted from con-tinuous great optimization of current solutions and updated

iteratively in the whole algorithm. The proposed algorithmwas tested with 18 LSMDVRP instances and the resultsindicated that it had high computation speed and reliability.

In the paper entitled “Forecasting dry bulk freight indexwith improved SVM,” Q. Han et al. attempted to forecast theshort-term trend of dry bulk freight index with an improvedSVM model which incorporated the wavelet transform. Asthe dry bulk freight index is usually influenced by randomevents in the freight market, the wavelet transform is thencombined to the SVM to denoise the index data series.The genetic algorithm is used to optimize parameters ofthe improved SVM. The prediction method displayed highaccuracy in the experiment based on real data.

In the paper entitled “Artificial intelligencemechanisms oninteractive modified simplex method with desirability functionfor optimising surface lapping process,” the authors appliedthe harmony search and firefly algorithms to optimize theinfluential parameters of surface lapping process of diskclamps.

In the paper entitled “An adapted firefly algorithm forproduct development project scheduling with fuzzy activityduration,” M. Huang et al. developed an adapted fuzzyfirefly algorithm to solve a mathematical model which wasbuilt for scheduling new product development project withuncertain activity duration. The effectiveness and efficiencyof the proposed algorithm were validated with benchmarkexperiments as well as a real-world example.

Besides thosementioned above, some authors present theapplication of SI in the electrical and electronic engineering.In the paper entitled “Short-term power generation energyforecasting model for small hydropower stations using GA-SVM,” G. Li et al. used a GA-based SVM model to predictshort-term power generation energy of small hydropowerand the numerical test proved the better performance ofthe GA-SVM forecasting model compared with the ARMAmodel.

In the paper entitled “Short-term wind speed forecastingusing support vector regression optimized by cuckoo optimiza-tion algorithm,” J. Wang et al. proposed a hybrid forecastingmodel combining recurrence plot (RP) with support vectorregression (SVR) to predict short-term wind speed series.In the hybrid model, the RP is used to analyze the windspeed series and select input variables for the SVR whichis employed for short-term prediction, while the parametersof the SVR are optimized by three SI algorithms, respec-tively, say the GA, the PSO, and the cuckoo optimizationalgorithm (COA). The experimental results indicated thatthe forecasting model based on COA-SVR outperformed theother twomodels, especially in the context of jump samplingsand multistep prediction.

In the paper entitled “Swarm intelligence-based smartenergy allocation strategy for charging stations of plug-inhybrid electric vehicles,” I. Rahman et al. used the gravita-tional search algorithm (GSA) and the PSO to optimize thecharging allocation strategy for extensive participation ofplug-in hybrid electric vehicles, respectively, and analyzedthe advantages as well as disadvantages of the two SI-basedmethods in the numerical experiment.

Mathematical Problems in Engineering 3

In the paper entitled “Swarm intelligence-based hybridmodels for short-term power load prediction,” J. Wang et al.combined the singular spectrum analysis (SSA) with the sea-sonal autoregressive integrated moving average (SARIMA)and the SVR, respectively, and developed two improvedforecasting models, that is, the SSA-SARIMA and the SSA-SVR, to implement short-term power load prediction. Inorder to optimize the parameters of the two hybrid models,the cuckoo search is employed. The experimental resultsshowed that the application of CS could further improve theprediction accuracies of both the two models.

In the paper entitled “A swarm random walk basedmethod for the standard cell placement problem,” the authorspresented a new SI-based method, the swarm random walkmethod, to optimize the standard cell placement on large-sizechip. According to the resulting placement in the benchmarkexperiment, the proposed method could be very competitivefor solving the standard cell placement problem.

In the paper entitled “Pattern synthesis of planar nonuni-form circular antenna arrays using a chaotic adaptive invasiveweed optimization algorithm,” to optimize the nonuniformcircular antenna arrays, H.Wu et al. revised the invasive weedoptimization (IWO) by combining the chaotic searchmethodinto the IWO with adaptive dispersion and obtained a novelIWO called chaotic adaptive invasive weed optimization(CAIWO) which has better convergence speed as well asglobal searching ability.

There are several papers that apply SI in the civil engineer-ing. In the paper entitled “Displacement prediction of tunnelsurrounding rock: a comparison of support vector machine andartificial neural network,” Q. Wu et al. used two methods,that is, the SVM and artificial neural network (ANN), toseparately predict tunnel surrounding rock displacement,and they found that the SVM-based method was morerobust and accurate than the ANN-based method for thedisplacement prediction of tunnel surrounding rock.

In the paper entitled “Damage identification of bridgebased on modal flexibility and neural network improved byparticle swarm optimization,” H. Liu et al. developed a two-stage method to identify damage of bridge. In the first stage,modal flexibility indices are employed to localize damage andthen an ANN whose parameters are optimized by PSO isused in the second stage to identify damage severity. Thenumerical simulation proved the feasibility and superiorityof the proposed method.

In the paper entitled “Optimal sensor placement forlatticed shell structure based on an improved particle swarmoptimization algorithm,” X. Zhang et al. proposed a newimproved particle swarm optimization (IPSO) algorithm tooptimize the sensor placement in large-scale structures forthe structural health monitoring. In the case study, theyproved the feasibility of the IPSO-basedmethod, as well as itssuperiority to other PSO algorithms in terms of convergencespeed and precision.

Communication is another realmwhere SI can be applied.In the paper entitled “Optimal sizing of a photovoltaic-hydrogen power system for HALE aircraft by means of particleswarm optimization,” V. Sanchez et al. attempted to designa power supply system for high altitude long endurance

(HALE) aircrafts which could provide a wide range oftelecommunication services. The main problem they metresided in the size optimization for the system, and they usedthe PSO to solve the optimal sizing problem. Case studyshowed that the power supply systems calculated by PSOcould work efficiently and steadily for long time flights.

In the paper entitled “Location prediction-based data dis-semination using swarm intelligence in opportunistic cognitivenetworks,” J. Li et al. applied the ant colony optimization toaddress the data dissemination problem in the opportunisticcognitive networks. Real-world simulation indicated that therouting scheme for conveying message optimized by the antcolony algorithm had better performances.

There are also two papers discussing the SI in automotiveengineering. In the paper entitled “Research of ant colonyoptimized adaptive control strategy for hybrid electric vehicle,”L. Li et al. develop an ant colony optimization based methodto dynamically determine energy management control strat-egy of hybrid electric vehicles (HEVs) in different drivingcycles. Based on a certain type of driving cycle identifiedby a fuzzy driving cycle recognition algorithm, the antcolony optimization algorithm is used to optimize the controlparameters in the corresponding context. The optimizationmethod is validated in the simulation experiments.

In the paper entitled “Research of obstacle recognitiontechnology in cross-country environment for unmanned groundvehicle,” Z. Yibing et al. presented a multistep approachincorporating filtering algorithm and multifeature fusionalgorithm based on Bayes classification theory to solve theobstacle recognition problem of unmanned ground vehiclesin cross-country environment. The robustness and accuracyof the algorithm are well proved in the simulation test.

Some works use the SI in robotics. In the paper entitled“PSO-based robot path planning for multisurvivor rescue inlimited survival time,” N. Geng et al. studied the optimalrescue path planning of a robot when using it in urgent anddangerous circumstances. To solve the problem, the authorsdeveloped a novel improved PSO incorporating particleupdating, insertion and inversion operators, and a rapidlocal search method. The simulation results presented thefeasibility and efficiency of the algorithm in finding optimalpaths.

The robot path planning problem is also attempted byJ. J. Liang et al. In their paper entitled “Comparison ofthree different curves used in path planning problems basedon particle swarm optimizer,” they employed the dynamicmultiswarm particle swarm optimizer to search the optimalparameters of three commonly used curves which wereusually applied in robot path planning problems.

The following four papers mainly focus on enhancing therepresentative SI algorithms. In the paper entitled “Hybridbiogeography based optimization for constrained numericaland engineering optimization,” Z. Mi et al. proposed a newhybrid biogeography based optimization (HBBO) algorithmwhich contained chaotic search and a newmutation operatorcombining differential evolution mutation operator withsimulated binary crosser of genetic algorithm. To achieveglobal optimal, the differential evolution mutation operatoris still used in the HBBO to provide an update on half

4 Mathematical Problems in Engineering

population. The HBBO was tested with 12 benchmark andfour engineering optimization problems, and results showedthat the HBBO outperformed other evolutionary algorithms,especially for constrained optimization problems.

In the paper entitled “Amodified artificial bee colony algo-rithm based on search space division and disruptive selectionstrategy,” Z. He et al. improved the artificial bee colony (ABC)algorithm from three aspects. They proposed a new initial-ization method to generate high quality initial solutions, adisruptive selection strategy to enhance population diversity,and a novel definition of the scout bee phase.

In the paper entitled “Quantum behaved particle swarmoptimization algorithm based on artificial fish swarm,” theauthors introduced adaptive parameters and swarm and fol-low activities to the existing quantumbehaved particle swarmoptimization (QPSO) algorithm and obtained a new QPSOcalled quantum particle swarm optimization algorithm basedon artificial fish swarm, which was validated to have betteroptimization ability than QPSO.

In the paper entitled “The inertia weight updating strate-gies in particle swarm optimisation based on the beta distri-bution,” the authors compared the performances of differentPSOs under different selected random updating strategies ofinertia weight. By using 28 benchmark functions, they foundthat the multiswarm PSO combining two updating strategiesof inertia weight was the best.

Acknowledgments

These articles present rich and valuable advancements that SItechnologies have made for solving problems in engineering.We would like to thank all the authors for their excellentwork and contributions to this special issue. We would alsolike to express our gratitude to all the reviewers for theirfundamental work and patience in assisting us.

Baozhen YaoFang Zong

Bin YuRui Mu

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