Algorithm 3Locally improve the quality of an individual Input: A,J,Pijkpath,ctijk,csjk
Output: The improvedAandJ
1: Find allPm forA, recordPαandPβ for allPm .Pass-by insertion
2: foreachPmdo
3: Delete it fromA
4: Insert it betweenPαandPβ, form the newA0
5: Calculate the fitnessJ0 ofA0
6: ifJ0 < J then
7: A←A0,J ←J0
8: end if
9: end for
10: foreachAkdo .2-nearest-neighbor swapping
11: forj←1, lk−1do
12: Swapakj andakj+1, get the newA0
13: Calculate the fitnessJ0 ofA0
14: ifJ0< J then
15: A←A0,J ←J0
16: end if
17: end for
18: end for
19: returnthe improvedAandJ
Algorithm 4Least-waiting-time decoding algorithm
Input: IntermediateAobtained after decoding single-robot tasks and determiningPαfor all two-robot tasks,ctijk,csjk,(Rk, Pα, Pβ)for all two-robot tasks
Output: The phenotypeA
1: Calculate the arriving timeταafor all two-robot tasks
2: Allocate anNT ×4matrixV to store(Rk, Pα, Pβ, ταa)
3: Allocate a row vectorLto store the first active positions in task sequences of all robots, each elementLk←1
4: repeat
5: Sort all rows ofV in ascending order byταa
6: Find the indexmof elementV(1,2)in the task sequence of robotV(1,1)
7: LV(1,1)←m+ 1,U ← ∅
8: fori←1, NRdo
9: ifRi 6=V(1,1)then
10: j←0
11: repeat
12: j ←j+ 1
13: Calculate the waiting timecwif insertingV(1,3)intoAias thej-th element
14: Add(i, j, cw)intoU as the last row
15: untilj=length(Ai) + 1oraij is a subtask of a two-robot task
16: end if
17: end for
18: Find the minimal value of the third column inU, which is in then-th row
19: InsertV(1,3)intoAU(n,1) as theU(n,2)-th element, the waiting time isU(n,3)
20: LU(n,1)←U(n,2) + 1
21: Delete the first row ofV, recalculateταafor two-robot tasks inV
22: untilV =∅
23: returnA
Algorithm 5Encoding algorithm using the combination-based coding strategy Input: P,Ns,ctijk,csjk
Output: The set of subtask groupsQ
1: fork←1, NRdo .CalculateC0
2: fori←1, NP do
3: for j←1, NP do
4: ifi6=jthen
5: c0ijk←ctijk+csik+csjk
6: else
7: c0ijk←+∞
8: end if
9: end for
10: end for
11: end for
12: Allocate a row vectorV to store all elementsc0ijk(c0ijk6= +∞) .DetermineH
13: SortV in ascending order,n←length(V)
14: H is the(0.05n)-th element inV
15: c0ijk ←+∞for all elementsc0ijk satisfyingc0ijk> H .Combine subtasks
16: l←0, Q← ∅
17: repeat
18: l←l+ 1
19: Find the minimal elementc0abk inC0
20: ifc0abk≤H then
21: Ql ← {Pa, Pb},c0ijk←+∞for all elements in columnsaandb
22: ifNs>2then
23: repeat
24: Find the minimal elementc0βmkin all rowsα(Pα ∈Ql)
25: ifc0βmk≤Hthen
26: AddPm toQl,c0ijk←+∞for all elements in columnm
27: end if
28: untilc0βmk> H orlength(Ql) =Ns
29: end if
30: end if
31: c0ijk←+∞for all elements in all rowsα(Pα∈Ql)
32: untilc0abk> H
33: P0 ←(P\Q)
34: ifP06=∅then
35: repeat
36: Find a random subtaskPnfromP0, delete it fromP0,Ql←Pn,l←l+ 1
37: untilP0 =∅
38: end if
39: returnQ
Algorithm 6Greedy decoding algorithm using the combination-based coding strategy Input: Z,Q,ctijk
Output: The phenotypeA
1: foreachZk do
2: Ak← ∅, n←0
3: fori←1, lzkdo
4: repeat
5: Find the minimalctnαk in all elementsctnjk(Pj ∈zki)
6: AddPαintoAkas the last element,n←α, deletePαfromzik
7: untilzik=∅
8: end for
9: end for
10: returnA
Publications with Peer Review Process
Liu, C. and Kroll, A. “A Centralized Multi-Robot Task Allocation for Industrial Plant Inspection by Using A* and Genetic Algorithms”. In “11th International Conference on Artificial Intel-ligence and Soft Computing (ICAISA 2012)”,LNCS, vol. 7268, pp. 466–474. Zakopane, Poland, April 29–May 3, 2012.
Liu, C. and Kroll, A. “On Designing Genetic Algorithms for Solving Small- and Medium-Scale Traveling Salesman Problems”. In “International Symposium on Swarm Intelligence and Differential Evolution (SIDE 2012)”,LNCS, vol. 7269, pp. 283–291. Zakopane, Poland, April 29–May 3, 2012.
Liu, C. and Kroll, A. “Memetic Algorithms for Optimal Task Allocation in Multi-Robot Systems for Inspection Problems with Cooperative Tasks”,Soft Computing, 2014. doi:10.1007/s00500-014-1274-0
Other Publications
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[ADA08] Al-Dulaimi, B.F. and Ali, H.A. “Enhanced traveling salesman problem solv-ing by genetic algorithm technique (TSPGA)”. World Academy of Science, Engineering and Technology, vol. 38, pp. 296–302, 2008.
[AEOP02] Ahuja, R.K., Özlem Ergun, Orlin, J.B. and Punnen, A.P. “A survey of very large-scale neighborhood search techniques”.Discrete Applied Mathematics, vol. 123(1-3), pp. 75–102, 2002.
[Ahm14] Ahmed, Z.H. “The ordered clustered travelling salesman problem: A hybrid genetic algorithm”. The Scientific World Journal, 2014.
[APP02] Arai, T., Pagello, E. and Parker, L. “Guest editorial advances in multirobot systems”. IEEE Transactions on Robotics and Automation, vol. 18(5), pp.
655–661, October 2002.
[BBH+12] Barz, T., Bonow, G., Hegenberg, J., Habib, K., Cramar, L., Welle, J., Schulz, D., Kroll, A. and Schmidt, L. “Unmanned inspection of large industrial envi-ronments”. In “Proceedings of the 7th Security Research Conference, Future Security”, vol. 318 ofCommunications in Computer and Information Science, pp. 216–219. Bonn, Germany, September 4–6, 2012.
[BHX13] Bao, Y., Hu, Z. and Xiong, T. “A PSO and pattern search based memetic algorithm for SVMs parameters optimization”. Neurocomputing, vol. 117, pp.
98–106, 2013.
[BK13] Bonow, G. and Kroll, A. “Gas leak localization in industrial environments using a TDLAS-based remote gas sensor and autonomous mobile robot with the Tri-Max method”. In “IEEE International Conference on Robotics and Automation (ICRA 2013)”, pp. 987–992. Karlsruhe, Germany, May 6–10, 2013.
[BKB09] Baetz, W., Kroll, A. and Bonow, G. “Mobile robots with active IR-optical sens-ing for remote gas detection and source localization”. In “Proceedsens-ings of the 2009 IEEE International Conference on Robotics and Automation”, ICRA’09, pp. 1004–1009. Kobe, Japan, May 12–17, 2009.
[BKS10] Baetz, W., Kroll, A. and Soldan, S. “On gas leak detection of pressurised components by using thermograms and pattern recognition algorithms”. In
“Proceedings of the 8th International Conference on NDE in Relation to Struc-tural Integrity for Nuclear and Pressurised Components”, pp. 503–512. Berlin, Germany, September 29–October 1, 2010.
[Blu00] Blume, C. “Optimized collision free robot move statement generation by the
evolutionary software gleam”. In “Real-World Applications of Evolutionary Computing”, vol. 1803 of Lecture Notes in Computer Science, pp. 330–341.
Scotland, UK, April 17, 2000.
[BMSS05] Burgard, W., Moors, M., Stachniss, C. and Schneider, F. “Coordinated multi-robot exploration”. IEEE Transactions on Robotics, vol. 21(3), pp. 376–386, 2005.
[BPL+13] Ballestin, F., Perez, A., Lino, P., Quintanilla, S. and Valls, V. “Static and dy-namic policies with RFID for the scheduling of retrieval and storage warehouse operations”. Computers & Industrial Engineering, vol. 66(4), pp. 696–709, De-cember 2013.
[Car03] Carter, A.E. Design and Application of Genetic Algorithms for the Multiple Traveling Salesperson Assignment Problem. Dissertation, Virginia Polytechnic Institute and State University, 2003.
[CC06] Chootinan, P. and Chen, A. “Constraint handling in genetic algorithms us-ing a gradient-based repair method”. Computers & Operations Research, vol. 33(8), pp. 2263–2281, 2006.
[CC14] Chand, P. and Carnegie, D.A. “Towards a robust feedback system for coordi-nating a hierarchical multi-robot system”.Robotics and Autonomous Systems, vol. 62(2), pp. 91–107, 2014.
[CDFVH06] Cotta, C., Dotu, I., Fernandez, A.J. and Van Hentenryck, P. “Scheduling social golfers with memetic evolutionary programming”. In “Proceedings of Inter-national Workshop on Hybrid Metaheuristics”, vol. 4030 of Lecture Notes in Computer Science, pp. 150–161. Gran Canaria, Spain, October 13–14, 2006.
[CPACMT13] Cruz, J., Paternina-Arboleda, C., Cantillo, V. and Montoya-Torres, J. “A two-pheromone trail ant colony system–tabu search approach for the heteroge-neous vehicle routing problem with time windows and multiple products”. Jour-nal of Heuristics, vol. 19(2), pp. 233–252, 2013.
[CSMO13] Capitan, J., Spaan, M.T.J., Merino, L. and Ollero, A. “Decentralized multi-robot cooperation with auctioned POMDPs”. International Journal of Robotics Research, vol. 32(6), pp. 650–671, 2013.
[CSVG13] Castro, M., Sörensen, K., Vansteenwegen, P. and Goos, P. “A memetic algo-rithm for the travelling salesperson problem with hotel selection”. Computers
& Operations Research, vol. 40(7), pp. 1716–1728, 2013.
[Dia04] Dias, M.B. TraderBots: A New Paradigm for Robust and Efficient Multirobot
Coordination in Dynamic Environments. Dissertation, Carnegie Mellon Uni-versity, 2004.
[DIR09] Distante, C., Indiveri, G. and Reina, G. “An application of mobile robotics for olfactory monitoring of hazardous industrial sites”. Industrial Robot: An International Journal, vol. 36(1), pp. 51–59, 2009.
[FIN04] Farinelli, A., Iocchi, L. and Nardi, D. “Multirobot systems: A classification focused on coordination”. IEEE Transactions on Systems, Man, and Cyber-netics, Part B: CyberCyber-netics, vol. 34(5), pp. 2015–2028, October 2004.
[GCBM07] GréWal, G., Coros, S., Banerji, D. and Morton, A. “Assigning data to dual memory banks in dsps with a genetic algorithm using a repair heuristic”. Ap-plied Intelligence, vol. 26(1), pp. 53–67, 2007.
[GK10] Gutin, G. and Karapetyan, D. “A memetic algorithm for the generalized travel-ing salesman problem”. vol. 9(1), pp. 47–60, 2010.
[GKN03] Goto, T., Kosaka, T. and Noborio, H. “On the heuristics of A* or A algorithm in its and robot path-planning”. In “IEEE/RSJ International Conference on Intel-ligent Robots and Systems (IROS 2003)”, vol. 2, pp. 1159–1166. Las Vegas, Nevada, USA, October 27–November 1, 2003.
[GLM13] Giordani, S., Lujak, M. and Martinelli, F. “A distributed multi-agent production planning and scheduling framework for mobile robots”.Computers & Industrial Engineering, vol. 64(1), pp. 19–30, January 2013.
[GM00] Gerkey, B.P. and Matari´c, M.J. “MURDOCH: Publish/subscribe task alloca-tion for heterogeneous agents”. In “Fourth Internaalloca-tional Conference on Au-tonomous Agents”, pp. 203–204. Barcelona, Spain, June 3–7, 2000.
[GM04] Gerkey, B.P. and Matari´c, M.J. “A formal analysis and taxonomy of task al-location in multi-robot systems”. International Journal of Robotics Research, vol. 23(9), pp. 939–954, September 2004.
[GML08] García-Martínez, C. and Lozano, M. “Local search based on genetic algo-rithms”. In “Advances in Metaheuristics for Hard Optimization”, Natural Com-puting Series, pp. 199–221. Berlin, Heidelberg: Springer, 2008.
[GO12] Guerrero, J. and Oliver, G. “Multi-robot coalition formation in real-time scenar-ios”. Robotics and Autonomous Systems, vol. 60(10), pp. 1295–1307, 2012.
[GS90] Gorges-Schleuter, M. “Explicit parallelism of genetic algorithms through pop-ulation structures”. In “Parallel Problem Solving from Nature”, vol. 496 of
Lec-ture Notes in Computer Science, pp. 150–159. Dortmund, Germany, October 1–3, 1990.
[GS94] Gorges-Schleuter, M. Parallel Evolutionary Algorithms and the Concept of Population Structures, chapter 15 and 16, pp. 261–319. New York: Wiley, 1994.
[Har94] Hart, W.E. Adaptive Global Optimization with Local Search. Dissertation, University of California, 1994.
[HCPGM99] Harik, G., Cantu-Paz, E., Goldberg, D.E. and Miller, B.L. “The gambler’s ruin problem, genetic algorithms, and the sizing of populations”. Evolution-ary Computation, vol. 7(3), pp. 231–253, 1999.
[HJK79] Hammer, P., Johnson, E. and Korte, B. “Conclusive remarks”. In “Discrete Op-timization II”, vol. 5 ofAnnals of Discrete Mathematics, pp. 427–453. Elsevier, 1979.
[HNR68] Hart, P.E., Nilsson, N.J. and Raphael, B. “A formal basis for the heuristic de-termination of minimum cost paths”. IEEE Transactions on Systems Science and Cybernetics, vol. SSC-4(2), pp. 100–107, 1968.
[Hol92] Holland, J.H. Adaptation in Natural and Artificial Systems. Cambridge, MA, USA: MIT Press, 1992.
[HVV94] Hoogeveen, J., van de Velde, S. and Veltman, B. “Complexity of scheduling multiprocessor tasks with prespecified processor allocations”. Discrete Ap-plied Mathematics, vol. 55(3), pp. 259–272, 1994.
[Jak10] Jakob, W. “A general cost-benefit-based adaptation framework for multimeme algorithms”. Memetic Computing, vol. 2(3), pp. 201–218, 2010.
[JBB04] Jakob, W., Blume, C. and Bretthauer, G. “Towards a generally applicable self-adapting hybridization of evolutionary algorithms”. In “Genetic and Evolu-tionary Computation – GECCO 2004”, vol. 3102 ofLecture Notes in Computer Science, pp. 790–791. Seattle, WA, USA, June 26–30, 2004.
[JBKL11] Janchiv, A., Batsaikhan, D., Kim, G.H. and Lee, S.G. “Complete coverage path planning for multi-robots based on”. In “11th International Conference on Control, Automation and Systems (ICCAS)”, pp. 824–827. Gyeonggi-do, Korea, October 26–29, 2011.
[JQSE01] Jakob, W., Quinte, A., Scherer, K.P. and Eggert, H. “Optimisation of a mi-cro fluidic component using a parallel evolutionary algorithm and simulation
based on discrete element methods”. In “Computer Aided Optimum Design of Structures VII”, pp. 337–346. Bologna, Italy, May, 2001.
[JQSS08] Jakob, W., Quinte, A., Stucky, K.U. and Süß, W. “Fast multi-objective schedul-ing of jobs to constrained resources usschedul-ing a hybrid evolutionary algorithm”. In
“Parallel Problem Solving from Nature - PPSN X”, vol. 5199 ofLecture Notes in Computer Science, pp. 1031–1040. Dortmund, Germany, September 13–
17, 2008.
[JSQ+13] Jakob, W., Strack, S., Quinte, A., Bengel, G., Stucky, K.U. and Süß, W. “Fast rescheduling of multiple workflows to constrained heterogeneous resources using multi-criteria memetic computing”. Algorithms, vol. 6(2), pp. 245–277, 2013.
[KBP09] Kroll, A., Baetz, W. and Peretzki, D. “On autonomous detection of pressured air and gas leaks using passive IR-thermography for mobile robot application”.
In “Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA 2009)”, ICRA’09, pp. 998–1003. Kobe, Japan, May 12–17, 2009.
[KBP13] Korošec, P., Bole, U. and Papa, G. “A multi-objective approach to the applica-tion of real-world producapplica-tion scheduling”. Expert Systems with Applications, vol. 40(15), pp. 5839–5853, 2013.
[KC07] Kolling, A. and Carpin, S. “Cooperative observation of multiple moving tar-gets: An algorithm and its formalization”. International Journal of Robotics Research, vol. 26(9), pp. 935–953, 2007.
[KCL12] Kheng, C.W., Chong, S.Y. and Lim, M.H. “Centroid-based memetic algorithm - adaptive Lamarckian and Baldwinian learning”. International Journal of Sys-tems Science, vol. 43(7, SI), pp. 1193–1216, 2012.
[Kir07] Kirk, J. “Traveling salesman problem - genetic algorithm”.
http://www.mathworks.com/matlabcentral/fileexchange/13680-traveling-salesman-problem-genetic-algorithm, 2007.
[KM14] Kaveh, A. and Mahdavi, V. “Colliding bodies optimization method for optimum design of truss structures with continuous variables”.Advances in Engineering Software, vol. 70, pp. 1–12, 2014.
[Kro08] Kroll, A. “A survey on mobile robots for industrial inspection”. In “Interna-tional Conference on Intelligent Autonomous Systems (IAS10)”, pp. 406–414.
Baden-Baden, Germany, July 23–25, 2008.
[Kro13] Kroll, A. Computational Intelligence – Eine Einführung in Probleme,
Metho-den und technische Anwendungen. München, Germany: OlMetho-denbourg Verlag, 2013.
[KS00] Krasnogor, N. and Smith, J. “A memetic algorithm with self-adaptive local search: TSP as a case study”. In “Proceedings of the Genetic and Evolu-tionary Computation Conference (GECCO 2000)”, pp. 987–994. Las Vegas, Nevada, USA, July 8–12, 2000.
[KSD13] Korsah, G.A., Stentz, A. and Dias, M.B. “A comprehensive taxonomy for multi-robot task allocation”. International Journal of Robotics Research, vol. 32(12, SI), pp. 1495–1512, October 2013.
[Lan98] Land, M.W.S. Evolutionary Algorithms with Local Search for Combinatorial Optimization. Dissertation, University of California, 1998.
[LJ10] Lust, T. and Jaszkiewicz, A. “Speed-up techniques for solving large-scale biobjective TSP”. Computers & Operations Research, vol. 37(3), pp. 521–
533, 2010.
[LK12a] Liu, C. and Kroll, A. “A centralized multi-robot task allocation for industrial plant inspection by using A* and genetic algorithms”. In “11th International Conference on Artificial Intelligence and Soft Computing (ICAISA 2012)”, vol.
7268 ofLecture Notes in Computer Science, pp. 466–474. Zakopane, Poland, April 29–May 3, 2012.
[LK12b] Liu, C. and Kroll, A. “On designing genetic algorithms for solving small- and medium-scale traveling salesman problems”. In “International Symposium on Swarm Intelligence and Differential Evolution (SIDE 2012)”, vol. 7269 of Lec-ture Notes in Computer Science, pp. 283–291. Zakopane, Poland, April 29–
May 3, 2012.
[LnKM+99] Larrañaga, P., Kuijpers, C.M.H., Murga, R.H., Inza, I. and Dizdarevic, S. “Ge-netic algorithms for the travelling salesman problem: A review of representa-tions and operators”. Artificial Intelligence Review, vol. 13(2), pp. 129–170, 1999.
[LRKW14] Lu, J., Riedl, G., Kiniger, B. and Werner, E.A. “Three-dimensional tool de-sign for steady-state electrochemical machining by continuous adjoint-based shape optimization”. Chemical Engineering Science, vol. 106, pp. 198–210, 2014.
[LTC12] Lin, Y.I., Tien, K.W. and Chu, C.H. “Multi-agent hierarchical negotiation based on augmented price schedules decomposition for distributed design”. Com-puters in Industry, vol. 63(6), pp. 597–609, 2012.
[Lum77] Lumb, R. “Inspection of pipelines using nondestructive techniques”. Physics in Technology, vol. 6, pp. 249–256, 1977.
[LVK05] Lytridis, C., Virk, G. and Kadar, E. “Search performance of a multi-robot team in odour source localisation”. In “Proceedings of the 8th International Confer-ence Climbing and Walking Robots (CLAWAR 2005)”, pp. 809–816. London, UK, September 13–15, 2005.
[MA04] Marques, L. and de Almeida, A. “Finding odours across large search spaces:
A particle swarm-based approach”. In “Proceedings of the 7th International Conference Climbing and Walking Robots (CLAWAR 2004)”, pp. 419–426.
Madrid, Spain, September 22-24, 2004.
[MHD12] Mouthuy, S., Hentenryck, P. and Deville, Y. “Constraint-based very large-scale neighborhood search”. Constraints, vol. 17(2), pp. 87–122, 2012.
[MK13a] Ordoñez Müller, A. and Kroll, A. “Effects of beam divergence in hand-held TDLAS sensors on long distance gas concentration measurements”. In “Inter-national Workshop on Advanced Infrared Technology and Applications (AITA 2013)”, vol. 12, pp. 9–13. Turin, Italy, September 10–13, 2013.
[MK13b] Ordoñez Müller, A. and Kroll, A. “On range extension of tunable diode laser absorption spectroscopy (TDLAS) based devices in remote gas sensing ap-plications”. In “International Symposium on Olfaction and Electronic Nose – ISOEN 2013”, Daegu, Korea, July 2–5, 2013.
[MK14] Ordoñez Müller, A. and Kroll, A. “On the use of cooperative autonomous mo-bile robots and optical remote sensing in inspection robotics”. In “Automation 2014”, pp. 847–864. Baden-Baden, Germany, July 1–2, 2014.
[MM10] Mosteo, A.R. and Montano, L. “A survey of multi-robot task allocation”. Techni-cal report AMI-009-10-TEC, Instituto de Investigación en Ingeniería de Aragón (I3A), 2010.
[MM11] Marjovi, A. and Marques, L. “Multi-robot olfactory search in structured en-vironments”. Robotics and Autonomous Systems, vol. 59(11), pp. 867–881, 2011.
[MM13] Marjovi, A. and Marques, L. “Optimal spatial formation of swarm robotic gas sensors in odor plume finding”.Autonomous Robots, vol. 35(2-3), pp. 93–109, 2013.
[MP13] Murray, C.C. and Park, W. “Incorporating human factor considerations in un-manned aerial vehicle routing”. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 43(4), pp. 860–874, July 2013.
[Mv04] Murovec, B. and Šuhel, P. “A repairing technique for the local search of the job-shop problem”. European Journal of Operational Research, vol. 153(1), pp. 220–238, 2004.
[NL12] Nazif, H. and Lee, L.S. “Optimised crossover genetic algorithm for capaci-tated vehicle routing problem”. Applied Mathematical Modelling, vol. 36(5), pp. 2110–2117, 2012.
[NOK07] Nguyen, Q., Ong, Y. and Krasnogor, N. “A study on the design issues of memetic algorithm”. In “IEEE Congress on Evolutionary Computation”, pp.
2390–2397. Singapore, September 25–28, 2007.
[Nor93] Noreils, F.R. “Toward a robot architecture integrating cooperation between mobile robots: Application to indoor environment”. The International Journal of Robotics Research, vol. 12, pp. 79–98, February 1993.
[NVTK03] Nikolos, I., Valavanis, K., Tsourveloudis, N. and Kostaras, A. “Evolutionary algorithm based offline/online path planner for UAV navigation”. IEEE Trans-actions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 33(6), pp. 898–912, December 2003.
[OB09] Özcan, E. and Ba¸saran, C. “A case study of memetic algorithms for constraint optimization”. Soft Computing, vol. 13(8-9), pp. 871–882, 2009.
[OOKT00] Onoyama, T., Oyanagi, K., Kubota, S. and Tsuruta, S. “GA applied method for interactively optimizing a large-scale distribution network”. In “Proceedings of TENCON 2000”, vol. 2, pp. 253–258. Kuala Lumpur, Malaysia, September 24–27, 2000.
[Par98] Parker, L.E. “ALLIANCE: An architecture for fault tolerant multi-robot cooper-ation”. IEEE Transactions on Robotics and Automation, vol. 14, pp. 220–240, 1998.
[Par08] Parker, L.E. Multiple Mobile Robot Systems, In B. Siciliano and O. Khatib (Editors), “Springer Handbook of Robotics”, pp. 921–941. Berlin: Springer, 2008.
[Pas13] Paszkowicz, W. “Genetic algorithms, a nature-inspired tool: A survey of appli-cations in materials science and related fields: Part II”. Materials and Manu-facturing Processes, vol. 28(7), pp. 708–725, 2013.
[Pin01] Pinedo, M.L. Scheduling: Theory, Algorithms, and Systems. Upper Saddle River, NJ, USA: Prentice Hall PTR, 2001.
[Pin05] Pinedo, M.L. Planning and Scheduling in Manufacturing and Services. New York, USA: Springer, 2005.
[PL12] Park, H. and Lee, J.W. “Task assignment and migration in wireless sen-sor networks via task decomposition”. Information Technology and Control, vol. 41(4), pp. 340–348, 2012.
[PRB+14] Paul, P.V., Ramalingam, A., Baskaran, R., Dhavachelvan, P., Vivekanandan, K. and Subramanian, R. “A new population seeding technique for permutation-coded genetic algorithm: Service transfer approach”. Journal of Computa-tional Science, vol. 5(2), pp. 277–297, 2014.
[PVK12] Papa, G., Vukašinovi´c, V. and Korošec, P. “Guided restarting local search for production planning”. Engineering Applications of Artificial Intelligence, vol. 25(2), pp. 242–253, 2012.
[PWS+13] Pan, Q., Wang, L., Sang, H., Li, J. and Liu, M. “A high performing memetic algorithm for the flowshop scheduling problem with blocking”. IEEE Transac-tions on Automation Science and Engineering, vol. 10(3), pp. 741–756, 2013.
[QZL+10] Qin, H., Zhou, J., Lu, Y., Wang, Y. and Zhang, Y. “Multi-objective differential evolution with adaptive cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling”. Energy Conversion and Management, vol. 51(4), pp. 788–794, April 2010.
[RPF+10] Ramchurn, S.D., Polukarov, M., Farinelli, A., Truong, C. and Jennings, N.R.
“Coalition formation with spatial and temporal constraints”. In “AAMAS’10”, pp. 1181–1188. Toronto, Canada, May 10–14, 2010.
[SBK12] Soldan, S., Bonow, G. and Kroll, A. “RobogasInspector - a mobile robotic sys-tem for remote leak sensing and localization in large industrial environments:
Overview and first results”. In “Proceedings of the 2012 IFAC Workshop on Automatic Control in Offshore Oil and Gas Production”, pp. 33–38. Norwegian University of Science and Technology, Trondheim, Norway, May 31–June 1, 2012.
[SDLM13] Soukour, A.A., Devendeville, L., Lucet, C. and Moukrim, A. “A memetic algo-rithm for staff scheduling problem in airport security service”. Expert Systems with Applications, vol. 40(18), pp. 7504–7512, 2013.
[SK13] Soldan, S. and Kroll, A. “Towards automated gas leak detection using ir gas imaging cameras”. In “International Workshop on Advanced Infrared Tech-nology and Applications (AITA 2013)”, pp. 195–199. Turin, Italy, September 10–13, 2013.
[Sol12] Soldan, S. “On extended depth of field to improve the quality of automated thermographic measurements in unknown environments”. Quantitative In-fraRed Thermography Journal, vol. 9(2), pp. 135–150, 2012.
[ST07] Smorodkina, E. and Tauritz, D. “Greedy population sizing for evolutionary al-gorithms”. In “IEEE Congress on Evolutionary Computation”, pp. 2181–2187.
Singapore, September 25–28, 2007.
[SWB+12] Soldan, S., Welle, J., Barz, T., Kroll, A. and Schulz, D. “Towards autonomous robotic systems for remote gas leak detection and localization in industrial environments”. In “Proceedings of the 8th International Conference on Field and Service Robotics Result”, vol. 92, pp. 233–247. Matsushima, Japan, July 16–19, 2012.
[SYD11] Sedighpour, M., Yousefikhoshbakht, M. and Darani, N.M. “An effective ge-netic algorithm for solving the multiple traveling salesman problem”. Journal of Optimization in Industrial Engineering, vol. 8, pp. 73–79, 2011.
[TZC13] Tian, Z., Zhang, L. and Chen, W. “Improved algorithm for navigation of rescue robots in underground mines”.Computers & Electrical Engineering, vol. 39(4), pp. 1088–1094, May 2013.
[WM95] Wolpert, D.H. and Macready, W.G. “No free lunch theorems for search”. Tech-nical report SFI-TR-95-02-010, Santa Fe Institute, NM, USA, 1995.
[WM97] Wolpert, D.H. and Macready, W.G. “No free lunch theorems for optimization”.
IEEE Transactions on Evolutionary Computation, vol. 1(1), pp. 67–82, 1997.
[WM00] Werger, B. and Matari´c, M.J. “Broadcast of local eligibility: Behavior-based control for strongly cooperative robot teams”. In “Proceedings of the Fourth In-ternational Conference on Autonomous Agents”, pp. 21–22. Barcelona, Spain, June 3–7, 2000.
[WSL09] Wang, B., Shu, H. and Luo, L. “A genetic algorithm with chromosome-repairing for min – # and min – εpolygonal approximation of digital curves”.
Journal of Visual Communication and Image Representation, vol. 20(1), pp.
45–56, 2009.
[XSB+12] Xhafa, F., Sun, J., Barolli, A., Takizawa, M. and Uchida, K. “Evaluation of ge-netic algorithms for single ground station scheduling problem”. In “IEEE 26th International Conference on Advanced Information Networking and Applica-tions (AINA)”, pp. 299–306. Fukuoka, Japan, March 26–29, 2012.
[ZCL07] Zhang, J., Chung, H.S.H. and Lo, W.L. “Clustering-based adaptive crossover
and mutation probabilities for genetic algorithms”. IEEE Transactions on Evo-lutionary Computation, vol. 11(3), pp. 326–335, June 2007.
[ZCX13] Zhang, C., Chen, J. and Xin, B. “Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning”.Applied Soft Computing, vol. 13(5), pp. 2947–2959, May 2013.
[ZGZ14] Zhang, J., Gong, D. and Zhang, Y. “A niching PSO-based multi-robot coop-eration method for localizing odor sources”. Neurocomputing, vol. 123, pp.
308–317, 2014.
[ZL10] Zhou, W. and Li, Y. “An improved genetic algorithm for multiple traveling sales-man problem”. In “2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)”, vol. 1, pp. 493–495. Wuhan, China, March 6–7, 2010.
[Zlo06] Zlot, R.M. An Auction-Based Approach to Complex Task Allocation for Mul-tirobot Teams. Dissertation, Robotics Institute, Carnegie Mellon University, December, 2006.
[ZLZ09] Zhang, G.L., Liu, X.X. and Zhang, T. “The impact of population size on the performance of GA”. In “2009 International Conference on Machine Learning and Cybernetics”, vol. 4, pp. 1866–1870. Hebei, China, July 12–15, 2009.
[ZP13] Zhang, Y. and Parker, L. “IQ-ASyMTRe: Forming executable coalitions for tightly coupled multirobot tasks”. IEEE Transactions on Robotics, vol. 29(2), pp. 400–416, 2013.
[ZR12] Zhou, K.X. and Roumeliotis, S. “A sparsity-aware QR decomposition algo-rithm for efficient cooperative localization”. In “IEEE International Conference on Robotics and Automation (ICRA)”, pp. 799–806. St. Paul, Minnesota, USA, May 14–18, 2012.
[ZS06] Zlot, R. and Stentz, A. “Market-based multirobot coordination for complex tasks”. International Journal of Robotics Research, Special Issue on the 4th International Conference on Field and Service Robotics, vol. 25(1), pp. 73–
101, January 2006.
[ZZ06] Zheng, T. and Zhao, X. “Research on optimized multiple robots path planning and task allocation approach”. In “IEEE International Conference on Robotics and Biomimetics”, pp. 1408–1413. Kunming, China, December 17–20, 2006.
Herausgegeben von / Edited by
Univ.-Prof. Dr.-Ing. Andreas Kroll, Universität Kassel
Band 1: Klassifikationsgestützte on-line Adaption eines robusten beobachter-basierten Fehlerdiagnoseansatzes für nichtlineare Systeme, Kassel 2011 Patrick Gerland
Band 2: Zur Identifikation mechatronischer Stellglieder mit Reibung bei Kraftfahrzeugen, Kassel 2012
Zhenxing Ren
Band 3: Sensordatenfusionsansätze in der Thermografie zur Verbesserung der Messergebnisse, Kassel 2014
Samuel Soldan