Editorial Swarm Intelligence in Engineering

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2013, Article ID 835251, 3 pageshttp://dx.doi.org/10.1155/2013/835251

EditorialSwarm Intelligence in Engineering

Baozhen Yao,1 Rui Mu,2 and Bin Yu3

1 Dalian University of Technology, Dalian, Liaoning 116024, China2Delft University of Technology, Delft, The Netherlands3 Dalian Maritime University, Dalian, Liaoning 116026, China

Correspondence should be addressed to Bin Yu; [email protected]

Received 24 February 2013; Accepted 24 February 2013

Copyright © 2013 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 behavior of simple individuals (e.g.,ant colonies, bird flocking, animal herding, and honey bees),which has attracted much attention of researchers and hasalso been applied successfully to solve optimization problemsin engineering. However, for large and complex problems,SI algorithms consume often much computation time due tostochastic feature of the search approaches. Therefore, thereis a potential requirement to develop efficient algorithm tofind solutions under the limited resources, time, and moneyin real-world applications.

Within this context, this special issue servers as a forumto highlight the most significant recent developments onthe topics of SI and to apply SI algorithms in real-lifescenario. The works in this issue contain new insights andfindings in this field. A broad range of topics has beendiscussed, especially in the following areas, benchmarkingand evaluation of new SI algorithms, convergence proof for SIalgorithms, comparative theoretical and empirical studies onSI algorithms, and SI algorithms for real-world application.

Some works focus on the application of genetic algorithmin different area, for example, G. Ning et al.’s “Economicanalysis on value chain of taxi fleet with battery-swappingmode using multiobjective genetic algorithm” presents aneconomic analysis model on value chain of taxi fleet withbattery-swapping mode in a pilot city. A multiobjectivegenetic algorithm is used to solve the problem. The real datacollected from the pilot city proves that the multiobjectivegenetic algorithm is tested as an effectivemethod to solve thisproblem.

B. Zhenming et al. “Direct index method of beam dam-age location detection based on difference theory of strainmodal shapes and the genetic algorithms application” appliesdirect index method SMSD and the Genetic Algorithmsinto structural damage identification. Numerical simulationshows that the criteria of damage location detection canbe obtained by strain mode difference curve through cubicspline interpolation.

F. Zong et al.’s “Daily commute time prediction based ongenetic algorithm” presents a joint discrete-continuousmodelfor activity-travel time allocation by employing the orderedprobit model for departure time choice and the hazardmodel for travel time prediction. Genetic algorithm (GA) isemployed for optimizing the parameter in the hazard model.The results also show that the genetic algorithm contributesto the optimization and thus the high accuracy of the hazardmodel.

Qu et al.’s “The optimized transport scheme of emptyand heavy containers with novel genetic algorithm” proposeda model with objective maximizing the route benefits todesign the transport scheme of empty and heavy containersreasonably. A novel GA is developed to solve the model. Thecase study about China-Europe route proves that this modelcan improve the liner company’s benefits effectively.

W. Juan et al.’s “Genetic algorithm for multiuser discretenetwork design problem under demand uncertainty” presentsa bilevel model for discrete network design. An iterativeapproach including an improved genetic algorithm and aFrank-Wolfe algorithm is used to solve the bilevel model.The numerical results on the Nguyen Dupuis network show

2 Mathematical Problems in Engineering

that the model and the related algorithms were effective fordiscrete network design.

Z. Yu et al.’s “Dynamic route guidance using improvedgenetic algorithms” presents an improved genetic algorithm(IGA) for dynamic route guidance algorithm. The proposedIGA designs a vicinity crossover technique and a greedybackward mutation technique to increase the populationdiversity and strengthen local search ability. The simulationresults show the effectiveness of the proposed algorithm.

Y. Li and Z. Sun’s “Articulated human motion track-ing using sequential immune genetic algorithm” proposed anovel generative method for human motion tracking in theframework of evolutionary computation.The paper designedan IGA-based method to estimate human pose from staticimages. It also proposed a sequential IGA (S-IGA) algorithmby incorporating the temporal continuity information intothe traditional IGA. Experimental results show that ourIGA-based pose estimation method can achieve viewpointinvariant 3D pose reconstruction, and the S-IGA-basedtracking method can achieve accurate and stable tracking of3D human motion.

Some works present improved algorithm based on parti-cle swarm optimization. J. Yao and D. Han “Improved bare-bones particle swarm optimization with neighborhood searchand Its application on ship design” proposed a new BPSOvariant called BPSO with neighborhood search (NSBPSO)to achieve a tradeoff between exploitation during the searchprocess. In the paper, experiments are conducted on twelvebenchmark functions and a real-world problem of shipdesign. Simulation results show that NSBPSO outperformsthe standard PSO, BPSO, and six other improved PSOalgorithms.

J. Xi et al.’s “Ahybrid algorithm of traffic accident datamin-ing on cause analysis” puts forward an improved associationrule algorithm based on particle swarm optimization (PSO).The new method is used to analyze the correlation betweentraffic accident attributes and causes.T-testmodel andDelphimethod were deployed to test and verify the accuracy of theimproved algorithm, the result of which was ten times fasterspeed for random traffic accident data sampling analyseson average. And the final result proves that the improvedalgorithm was accurate and stable.

Y. Lin’s “Particle swarm optimization algorithm for unre-lated parallel machine scheduling with release dates” proposeda heuristic and a very effective particle swarm optimiza-tion (PSO) algorithm to tackle the problem of minimizingmakespan for 𝑛 jobs on 𝑚 unrelated parallel machines withrelease dates. Computational results show that the proposedPSO is very accurate and that it outperforms the existingmetaheuristic.

A. Szabo and L. de Castro’s “A constructive data classifi-cation version of the particle swarm optimization algorithm”introduced new particle swarm optimization algorithm spe-cially designed to solve continuous parameter optimizationproblems. The proposals were applied to wide range ofdatabases from the literature, and the results show thatthey are competitive in relation to other approaches fromthe literature, with the advantage of having a dynamicallyconstructed architecture.

Also, ant colony algorithm is discussed in some works.Q. Xu et al.’s “Simulated annealing-based ant colony algorithmfor tugboat scheduling optimization” presents a hybrid simu-lated annealing-based ant colony algorithm to optimize thetugboat scheduling. In this paper, experiments are conductedto examine the effectiveness of the proposed algorithm for thetugboat scheduling problem.

G. Yan and D. Feng’s “Escape-route planning of under-ground coal mine based on improved ant algorithm” proposeda new escape-route planning method of underground minesbased on the improved ant algorithm. A tunnel networkzoning method and max–min ant systemmethod are used toimprove the performance of the ant algorithm. Experimentsshow that the proposed method can find good escape routescorrectly and efficiently and can be used in the escape-routeplanning of large and medium underground cone mines.

There are also some works discussing other algorithmsin this field. J. Wu’s “Solving unconstrained global opti-mization problems via hybrid swarm intelligence approaches”gives an overview of two efficient hybrid SGO approaches,namely, a real-coded genetic algorithm-based PSO (RGA-PSO)method and an artificial immune algorithm-based PSO(AIA-PSO)method.Numerical results indicate that theRGA-PSO and AIA-PSO approaches can be considered alternativeSGO approaches for solving standard-dimensional UGOproblems.

Z. Wei et al.’s “Bus dispatching interval optimization basedon adaptive bacteria foraging algorithm” applied the improvedbacterial algorithm to schedule the bus departing interval.Based on adaptive bacteria foraging algorithm (ABFA), amodel on one bus line in Hohhot city in China was estab-lished and simulated.The final results showed that ABFAwasmost feasible in optimizing variables.

S. TUO et al.’s “An improved harmony search basedon teaching-learning strategy for unconstrained optimizationproblems” presents an improved global harmony search algo-rithm named harmony search based on teaching-learning(HSTL) for high-dimensional complex optimization prob-lems. The experimental results of 31 complex benchmarkfunctions demonstrate that the HSTL method has strongconvergence, robustness, and better balance capacity of spaceexploration and local exploitation on high-dimensional com-plex optimization problems.

Y. Xu et al.’s “A simple and efficient artificial bee colonyalgorithm” proposes a new artificial bee colony (NABC) algo-rithm, which modifies the search pattern of both employedand onlooker bees. Experiments are conducted on a set oftwelve benchmark functions. Simulation results show thatthis approach is significantly better or at least comparable tothe original ABC and seven other stochastic algorithms.

S. Zhong et al.’s “Guidance compliance behavior on VMSbased on SOAR cognitive architecture” introduced SOARto design the agent with the detailed description of theworking memory, long-term memory, decision cycle, andlearning mechanism based on the multiagent platform.Experiments are simulated many times under given sim-ulation network and conditions. The results, including thecomparison between guidance and no guidance, the statetransition times, and average chunking times, are analyzed to

Mathematical Problems in Engineering 3

further study the laws of guidance compliance and learningmechanism.

Cuevas’s “A Swarm Optimization Algorithm for Multi-modal Functions and its Application in Multi-circle Detection”presents a new swarm multimodal optimization algorithmnamed as the Collective Animal Behavior (CAB). In theproposed algorithm, searcher agents emulate a group of ani-mals which interact to each other based on simple biologicallaws that are modeled as evolutionary operators. Numer-ical experiments are conducted to compare the proposedmethod with the state-of-the-art methods on benchmarkfunctions. The proposed algorithm has been also applied tothe engineering problem of multi-circle detection, achievingsatisfactory results.

G. Cabrera et al.’s “A hybrid approach using an artificialbee algorithm with mixed integer programming applied to alarge-scale capacitated facility location problem” presents ahybridization of two different approaches applied to the well-known capacitated facility location problem (CFLP).The arti-ficial bee algorithm (BA) is used to select a promising subsetof locations (warehouses) which are solely included in themixed integer programming (MIP) model. According to theresults, combining the BAwith amathematical programmingapproach appears to be an interesting research area in thecombinatorial optimization.

These articles demonstrate the advancement that swarmintelligence technologies have made for supporting problemsolving in engineering. Developing the efficient algorithm tofind solutions can provide solutions for large and complexproblems under the limited resources, time, and moneyin real-world applications. We would like to express ourgratitude to the many reviewers for their hard works. Wewould also like to thank the authors for their contributionsto the special issue. This special issue could not have beencompleted without their dedication and support.

Baozhen YaoRui MuBin Yu

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