Reactive approach for automating exploration and exploitation in ant colony optimization
Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-base...
Saved in:
Main Author: | |
---|---|
Format: | Thesis |
Language: | eng eng |
Published: |
2016
|
Subjects: | |
Online Access: | https://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf https://etd.uum.edu.my/5776/2/s94099_01.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my-uum-etd.5776 |
---|---|
record_format |
uketd_dc |
institution |
Universiti Utara Malaysia |
collection |
UUM ETD |
language |
eng eng |
advisor |
Ku Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani |
topic |
QA75 Electronic computers Computer science |
spellingShingle |
QA75 Electronic computers Computer science Allwawi, Rafid Sagban Abbood Reactive approach for automating exploration and exploitation in ant colony optimization |
description |
Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations. |
format |
Thesis |
qualification_name |
Ph.D. |
qualification_level |
Doctorate |
author |
Allwawi, Rafid Sagban Abbood |
author_facet |
Allwawi, Rafid Sagban Abbood |
author_sort |
Allwawi, Rafid Sagban Abbood |
title |
Reactive approach for automating exploration and exploitation in ant colony optimization |
title_short |
Reactive approach for automating exploration and exploitation in ant colony optimization |
title_full |
Reactive approach for automating exploration and exploitation in ant colony optimization |
title_fullStr |
Reactive approach for automating exploration and exploitation in ant colony optimization |
title_full_unstemmed |
Reactive approach for automating exploration and exploitation in ant colony optimization |
title_sort |
reactive approach for automating exploration and exploitation in ant colony optimization |
granting_institution |
Universiti Utara Malaysia |
granting_department |
Awang Had Salleh Graduate School of Arts & Sciences |
publishDate |
2016 |
url |
https://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf https://etd.uum.edu.my/5776/2/s94099_01.pdf |
_version_ |
1747827979561467904 |
spelling |
my-uum-etd.57762016-07-13T15:06:54Z Reactive approach for automating exploration and exploitation in ant colony optimization 2016 Allwawi, Rafid Sagban Abbood Ku Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA75 Electronic computers. Computer science Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations. 2016 Thesis https://etd.uum.edu.my/5776/ https://etd.uum.edu.my/5776/1/depositpermission_s94099.pdf text eng staffonly https://etd.uum.edu.my/5776/2/s94099_01.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Aleti, A. (2012). An adaptive approach to controlling parameters of evolutionary algorithms. Unpublished doctoral dissertation. Swinburne University of Technology, Melbourne Australia. Adenso-Diiaz, B., & Laguna, M. (2006). Fine-tuning of algorithms using fractional experimental designs and local search. Operations Research, 54(1), 99–114. Afshar, M. (2005). A new transition rule for ant colony optimization algorithms: Application to pipe network optimization problems. Engineering Optimization, 37(5), 525–540. Alaya, I., Solnon, C., & Ghedira, K. (2004). Ant algorithm for the multi-dimensional knapsack problem. In B. Flipic & J. Silic (Eds.), Proceedings of BIOMA'2004: The International Conference on Bioinspired Optimization Methods and Their Applications (pp. 63–72). Ljubljana, Slovenia: Jozef Stefan Institute. Aljanaby, A., Ku-Mahamud, K. R., & Norwawi, N. M. (2010). Interacted multiple ant colonies optimization framework: An experimental study of the evaluation and the exploration techniques to control the search stagnation. International Journal of Advancements in Computing Technology, 2(1), 78–85. Alobaedy, M. M., & Ku-Mahamud, K. R. (2015). Strategic Oscillation for Exploitation and Exploration of ACS Algorithm for Job Scheduling in Static Grid Computing. In 2015 Second International Conference on Computing Technology and Information Management (ICCTIM) (pp. 87–92). Johor: IEEE. Altiparmak, F., & Karaoglan, I. (2007). A genetic ant colony optimization approach for concave cost transportation problems. In Proceedings of CEC 2007: The IEEE Congress on Evolutionary Computation (pp. 1685–1692). Stamford, Singapore: IEEE. Amir, C., Badr, A., & Farag, I. (2007). A fuzzy logic controller for ant algorithms. Computing and Information Systems, 11(2), 26–34. Anghinolfi, D., Boccalatte, A., Paolucci, M., & Vecchiola, C. (2008). Performance evaluation of an adaptive ant colony optimization applied to single machine scheduling. In L. Xiaodong, M. Kirley, M. Zhang, D. Green, V. Ciesielski, H. Abbass, … S. Yuhui (Eds.), Lecture Notes in Computer Science: Simulated Evolution and Learning (Vol. 5361, pp. 411–420). Heidelberg, Germany: Springer. Angus, D. J. (2008). Niching ant colony optimisation. Unpublished doctoral dissertation. Swinburne University of Technology Melbourne, Australia. Baghel, M., Agrawal, S., & Silakari, S. (2012). Survey of metaheuristic algorithms for combinatorial optimization. International Journal of Computer Applications, 58(19), 21–31. Balaprakash, P., Birattari, M., & Stützle, T. (2007). Improvement strategies for the F-Race algorithm: Sampling design and iterative refinement. In T. Bartz-Beielstein, M. J. B. Aguilera, C. Blum, B. Naujoks, A. Roli, G. Rudolph, & M. Sampels (Eds.), Lecture Notes in Computer Science: Hybrid Metaheuristics (Vol. 4771, pp. 108–122). Heidelberg, Germany: Springer. Barbero, F., Patricelli, D., Witek, M., Balletto, E., Casacci, L. P., Sala, M., & Bonelli, S. (2012). Myrmica ants and their butterfly parasites with special focus on the acoustic communication. Psyche: A Journal of Entomology. Barbero, F., Thomas, J., Bonelli, S., Balletto, E., & Schönrogge, K. (2009). Queen ants make distinctive sounds that are mimicked by a butterfly social parasite. Science, 323(5915), 782–785. Barr, R. S., Golden, B. L., Kelly, J. P., Resende, M. G. ., & Stewart, W. R. (1995). Designing and reporting on computational experiments with heuristic methods. Journal of Heuristics, 1, 9–32. Battiti, R., & Birattari, M. (2013). The LION way. Machine Learning plus Intelligent Optimization. Los Angeles: Lionsolver Inc. Battiti, R., Brunato, M., & Mascia, F. (2008). Reactive search and intelligent optimization. U.S.A.: Springer. Battiti, R., & Protasi, M. (2001). Reactive local search for the maximum clique problem. In R. Battiti & M. Protasi (Eds.), Algorithmica (Vol. 29, pp. 610–637). Heidelberg, Germany: Springer. Bertsimas, D., Brown, D. B. D., & Caramanis, C. (2011). Theory and Applications of Robust Optimization. SIAM Review, 53(3), 464–501. Beer, C., Hendtlass, T., & Montgomery, J. (2012). Improving exploration in ant colony optimization with antennation. In 2012 IEEE Congress on Evolutionary Computation. Brisbane, Australia. Retrieved from http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=6252923 Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2009). An improved ant colony optimization for vehicle routing problem. European Journal of Operational Research, 196(1), 171–176. Birattari, M., Stützle, T., Paquete, L., & Varrentrapp, K. (2002). A racing algorithm for configuring metaheuristics. In W. B. Langdon, E. Cantü-Paz, K. E. Mathias, R. Roy, D. Davis, R. Poli, … N. Jonoska (Eds.), Proceedings of GECCO’02: The Genetic and Evolutionary Computation Conference (pp. 11–18). San Francisco, CA, USA: Morgan Kaufmann. Blum, C. (2002). ACO applied to group shop scheduling: A case study on intensification and diversification. In M. Dorigo, G. Di-Caro, & M. Sampels (Eds.), Lecture Notes in Computer Science (LNCS): Ant algorithms (Vol. 2463, pp. 14–27). Heidelberg, Germany: Springer. Blum, C. (2005a). Ant colony optimization: Introduction and recent trends. Physics Of Life Reviews, 2(4), 353–373. Blum, C. (2005b). Beam-ACO—hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Research, 32(6), 1565–1591. Blum, C., & Blesa, M. J. (2005). New metaheuristic approaches for the edgeweighted k-cardinality tree problem. Computers & Operations Research, 32(6), 1355–1377. Blum, C., & Dorigo, M. (2004). The hyper-cube framework for ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics, 34(2), 1161–1172. Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 11, 4135–4151. Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. New York, USA: Oxford Univ. Press. Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237(2013), 82–117. Bui, T. N., & Zrncic, C. M. (2006). An ant-based algorithm for finding degreeconstrained minimum spanning tree. In M. Keijzer, J. A. Foster, D. V. Arnold, A. Hernández-Aguirre, V. Babovic, G. S. Hornby, … D. Thierens (Eds.), Proceedings of GECCO’06: The 8th Annual Conference on Genetic And Evolutionary Computation (Vol. 1, pp. 11–18). New York, USA: ACM Press. Bullnheimer, B., Hartl, R. F., & Straub, C. (1997). A new rank based version of the ant system - A computational study (working paper no.1 ed.). Vienna: Institute of Management Science, University of Vienna. Burkard, R. ., Cela, E., Karisch, S. E., & Rendl, F. (1997). QAPLIB - A quadratic assignment problem library. Journal of Global Optimization, (10), 391–403. Retrieved from http://www.opt.math.tu-graz.ac.at/qaplib/. Carterette, B. (2011). An analysis of NP-completeness in novelty and diversity ranking. Information Retrieval, 14(1), 89–106. doi:10.1007/s10791-010-9157-1. Chen, W., Bian, W., & Zeng, R. (2013). Research on convergence of ACO subset algorithms. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 32(2), 649–660. Chusanapiputt, S., Nualhong, D., Jantarang, S., & Phoomvuthisarn, S. (2006). Selective self-adaptive approach to ant system for solving unit commitment problem. In M. Keijzer, J. A. Foster, D. V. Arnold, A. Hernández-Aguirre, V. Babovic, G. S. Hornby, … D. Thierens (Eds.), Proceedings of GECCO ’06: The 8th Annual Conference on Genetic and Evolutionary Computation (Vol. 2, pp. 1729–1736). New York, USA: ACM Press. Colas, S., & Monmarch, N. (2008). Artificial ants for the optimization of virtual keyboard arrangement for disabled people. In N. Monmarché, E.-G. Talbi, P. Collet, M. Schoenauer, & E. Lutton (Eds.), Lecture Notes in Computer Science (LNCS): Artificial Evolution (Vol. 4926, pp. 87–99). Heidelberg, Germany: Springer. Collings, J., & Kim, E. (2014). A distributed and decentralized approach for ant colony optimization with fuzzy parameter adaptation in traveling salesman problem. In 2014 IEEE Symposium on Swarm Intelligence (SIS) (pp. 1– 9). Florida, U.S.A. doi:10.1109/SIS.2014.7011805 Colorni, A., Dorigo, M., Maniezzo, V., Elettronica, D., & Milano, P. (1991). Distributed optimization by ant colonies. In Proceedings of ECAL91: The European Conference on Artificial Life (pp. 134–142). Paris, France: Elsevier Publishing. Cordon, O., Herrera, F., & Stützle, T. (2002). A review on the ant colony optimization metaheuristic: Basis, models and new Trends. Mathware and Soft Computing, 9(2-3), 141–175. Cordon, O., Herrera, I. F. de V. F., & Moreno, L. (2000). A new ACO model integrating evolutionary computation concepts: The best-worst ant system. In M. Dorigo, M. Middendorf, & T. Stützle (Eds.), Preceedings of ANTS’2000: From Ant Colonies to Artificial Ants: A Series of International Workshops On Ant Algorithims (pp. 22–29). Brussels, Belgium: IRIDIA, Universite Libre de Bruxelles. Çunkaş, M., & Özsağlam, M. Y. (2009). A Comparative Study on Particle Swarm Optimization and Genetic Algorithms for Traveling Salesman Problems. Cybernetics And Systems, 40(6), 490–507. doi:10.1080/01969720903068435. Deneubourg, J.-L., Aron, S., Goss, S., & Pasteels, J. M. (1990). The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behavior, 3(2), 159–168. Deren, L., Kaichang, D., & Deyi, L. (2000). Knowledge representation and uncertainty reasoning in GIS based on cloud models. In The 9th International Symposium On Spatial Data Handling. Beijing, China. Retrieved from http://shoreline.eng.ohio-state.edu/dkc/SDH2000_ Li_Di.pdf Di-Caro, G., & Dorigo, M. (1998). AntNet: Distributed stigmergetic control for communications networks. JAIR: Journal of Artificial Intelligence Research, 9(1), 317–365. Dorigo, M. (1992). Optimization, learning and natural algorithms. Unpublished doctoral dissertation. Politecnico di Milano, Italy. Dorigo, M. (2007). Ant colony optimization. Scholarpedia, 2(3), 1–12. Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2-3), 243–278. Dorigo, M., & Di-Caro, G.. (1999). The ant colony optimization meta-heuristic. In C. David, M. Dorigo, & F. Glover (Eds.), New Ideas in Optimization. London: McGraw-Hill. Dorigo, M., Di-Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172. Dorigo, M., & Gambardella, L. M. (1997). Ant colony system : A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66. Dorigo, M., Gambardella, L. M., Middendorf, M., & Stützle, T. (2002). Special section on ant colony optimization. IEEE Transactions on Evolutionary Computation, 6(4), 317–319. Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Positive feedback as a search strategy (Tech. Rept. 91-016). Milano, Italy: Laboratorio di Calcolatori, Dipartimento di Elettronica, Politecnico di Milano. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transaction on Systems, 26(1), 29–41. Dorigo, M., & Socha, K. (2007). Ant colony optimization. In T. F. Gonzalez (Ed.), Handbook of approximation algorithms and metaheuristics (pp. 1–25). Boca Raton, Florida: Chapman & Hall/CRC. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA, USA: MIT Press. Dorigo, M., & Stützle, T. (2010). Ant colony optimization: Overview and recent advances. In M. Gendreau & J. Potvin (Eds.), Handbook of Metaheuristics (Vol. 146, pp. 227–263). New York, USA: Springer US. Eiben, A., Michalewicz, Z., Schoenauer, M., & Smith, J. (2007). Parameter control in evolutionary algorithms. In F. G. Lobo, C. F. Lima, & Z. Michalewicz (Eds.), Studies In Computational Intelligence (SCI): Parameter Setting in Evolutionary Algorithms (Vol. 54, pp. 19–46). Heidelberg, Germany: Springer. Eiben, A., & Jelasity, M. (2002). A critical note on experimental research methodology in EC. In Proceedings of CEC’02: The 2002 Congress on Evolutionary Computation (Vol. 2, pp. 582–587). Washington, DC, USA: IEEE Computer Society. Eiben, A., & Smit, S. K. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1), 19–31. doi:10.1016/j.swevo.2011.02.001. Eswaramurthy, V. P., & Tamilarasi, A. (2009). Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems. The International Journal of Advanced Manufacturing Technology, 40(9-10), 1004–1015. Failho, A. R. S. (2010). Adaptive Operator Selection for Optimization. Unpublished doctoral dissertation. Ecole Doctorale d’ Informatique, Universite Paris, Paris, France. Favaretto, D., Moretti, E., & Pellegrini, P. (2009). On the explorative behavior of MAX–MIN ant system. In T. Stützle, M. Birattari, & H. H. Hoos (Eds.), Lecture Notes in Computer Science (LNCS): Engineering Stochastic Local Search Algorithms (Vol. 5752,, pp. 115–119). Heidelberg, Germany: Springer. Fletcher, R. (1997). Practical methods of optimization. Chichester, England: Weley & Sons Ltd. Forster, M., Bickel, B., Hardung, B., & Gabriella, K. (2007). Self-adaptive ant colony optimisation applied to function allocation in vehicle networks. In D. Thierens, H.-G. Beyer, J. Bongard, J. Branke, J. A. Clark, D. Cliff, … I. Wegener (Eds.), Proceedings of GECCO 2007: The 9th Annual Conference on Genetic And Evolutionary Computation (pp. 1991–1998). London, UK: ACM Press. Gaertner, D., & Clark, K. (2005). On optimal parameters for ant colony optimization algorithms TSP classifications. In H. Arabnia & R. Joshua (Eds.), Proceedings of IC-AI 2005: The International Conference on Artificial Intelligence (Vol. 2, pp. 83–89). Las Vegas, Nevada, USA: CSREA Press. Gambardella, L. M., & Dorigo, M. (1995). Ant-Q: A reinforcement learning approach to the traveling salesman problem. In A. Prieditis & S. J. Russell (Eds.), The Morgan Kaufmann Series in Machine Learning: Proceedings of The 12th International Conference on Machine Learning (pp. 252–260). California: Morgan Kaufmann. Gambardella, L. M., Montemanni, R., & Weyland, D. (2012). Coupling ant colony systems with strong local searches. European Journal of Operational Research, 220(3), 831–843. doi:10.1016/j.ejor.2012.02.038 Gambardella, L. M., Taillard, E. D., & Dorigo, M. (1999). Ant Colonies for the Quadratic Assignment Problem. The Journal of The Operational Research Society, 50(2), 167. doi:10.2307/3010565 Garey, M. R., & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of NP-completeness. San Francisco, CA, USA: Freeman. Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm Intelligence, 1(1), 3–31. Garro, B. A., Sossa, H., Vázquez, R. A., Juan, A., Batiz, D. D., & Othon, M. De. (2007). Evolving ant colony system for optimizing path planning in mobile robots. In Proceedings of CERMA 2007: The 4th Congress of Electronics, Robotics and Automotive Mechanics (pp. 444–449). Morelos, México: IEEE Computer Society Press. Gendreau, M., & Potvin, J.-Y. (2010). Handbook in metaheuristics. New York, USA: Springer. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99. Guntsch, M. (2004). Ant algorithms in stochastic and multi-criteria environments. Unpublished doctoral dissertation. Universität Fridericiana zu Karlsruhe, Karlsruhe, Germany. Guntsch, M., & Middendorf, M. (2002). A population based approah for ACO. In S. Cagnoni, J. Gottlieb, E. Hart, M. Middendorf, & G. R. Raidl (Eds.), Lecture Notes in Computer Science: Applications of Evolutionary Computing (Vol. 2279, pp. 72–81). Heidelberg, Germany: Springer. Hooker, J. N. (1995). Testing heuristics: We have it all wrong. Journal of heuristics, 1(1), 33–42. Hoos, H. H., & Stützle, T (2005). Stochastic local search: Foundations and applications. U.S.A.: Elsevier Inc. Hutter, F., & Leyton-brown, K. (2009). ParamILS : An Automatic Algorithm Configuration Framework. Journal of Artificial Intelligence Research, 36, 267–306. Johnson, D. S. (2001). A Theoreticians guide for experimental analysis of algorithms. In M. H. Goldwasser & C. C. McGeoch (Eds.), Data Structures, Near Neighbor Searches, and Methodology: Proceedings of The 5th and 6th DIMACS Implementation Challenges (Vol. 59, pp. 215–250). U.S.A.: American Mathematical Society. Johnson, D. S., & McGeoch, L. A. (2007). Experimental Analysis of Heuristics for the ATSP. In G. Gutin & A. P. Punnen (Eds.), The Traveling Salesman Problem And Its Variations (pp. 445–487). Springer. doi:10.1007/b101971. Kawamura, H., Yamamoto, M., & Suzuki, K. (2000). Multiple ant colonies algorithm based on colony level. IEICE TRANSACTIONS On Fundamentals of Electronics, Communications and Computer Sciences, E83(2), 371–379. Khichane, M., Albert, P., & Solnon, C. (2009). An ACO-based reactive framework for ant colony optimization : First experiments on constraint satisfaction problems. In T. Stützle (Ed.), Lecture Notes in Computer Science (LNCS): Learning and Intelligent Optimization (Vol. 5851, pp. 119–133). Heidelberg, Germany: Springer. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671–80. Kocer, H. E., & Akca, M. R. (2014). an Improved Artificial Bee Colony Algorithm With Local Search for Traveling Salesman Problem. Cybernetics and Systems, 45(8), 635–649. doi:10.1080/01969722.2014.970396. Korte, B., & Vygen, J. (2006). Combinatorial optimization: Theory and algorithms. Heidelberg, Germany: Springer. Kov, O., & Skrbek, M. (2008). Ant Colony Optimization with Castes. In V. Kůrková, R. Neruda, & J. Koutník (Eds.), Lecture Notes in Computer Science (LNCS): Artificial Neural Networks (ICANN 2008): Proceedings of the 18th International Conference (Vol. 5163, pp. 435–442). Heidelberg, Germany: Springer. Ku-Mahamud, K. R., & Alobaedy, M. M. (2013). New heuristic function in ant colony system algorithm for optimization. In A. Kanarachos (Ed.), Proceedings of MAMECTIS ’13: The 15th International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems (pp. 13–18). Lemesos, Cyprus: WSEAS. Lafayette, W. (2001). Experimental evaluation of heuristic optimization algorithms: A tutorial, 304, 261–304. Lawler, E. L. (1963). The quadratic assignment problem. Management Science, 9(4), 586–599 Lawler, E. L., Lenstra, J. K., Kan, A. H. G., & Shmoys, D. B. (1985). The travelling salesman problem. New Jersey, U.S.A.: John Wiley & Sons. Lin, Y., & Middendorf, M. (2013). Simple probabilistic population based optimization for combinatorial optimization. In SIS: IEEE Symposium on Swarm Intelligence (pp. 213–220). Singapore: IEEE. doi:10.1109/SIS.2013.6615181 Liu, X., & Yang, C. (2011). Optimization of Vehicle Routing Problem Based on Max-Min Ant System with Parameter Adaptation. In 2011 Seventh International Conference on Computational Intelligence And Security. China, Hainan: IEEE. Liu, Y., Ang, G., Chen, H., Zhao, Z., Zhu, X., & Liu, Z. (2011). An adaptive fuzzy ant colony optimization for feature selection. Journal Of Computational Information Systems, 4(7), 1206–1213. Lopez-Ibanez, M. (2010). Ant Colony Optimization. In M. Pelikan & J. Branke (Eds.), Proceedings of GECCO’10: The 12th Annual Conference Companion On Genetic and Evolutionary Computation (pp. 2353–2384). New York, NY, USA: ACM. Lopez-Ibanez, M., & Stützle, T. (2011). The automatic design of multi-objective ant colony optimization algorithms. IEEE Transactions on Evolutionary Computation, 16(6), 861 – 875. doi:10.1109/TEVC.2011.2182651. Lopez-Ibanez, M., & Stützle, T. (2014). Automatically improving the anytime behaviour of optimisation algorithms. European Journal of Operational Research, 235(3), 569–582. Maniezzo, V. (1999). Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS: Journal on Computing, 11(4), 1–22. Martens, D., Backer, M. De, Haesen, R., Vanthienen, J., & Snoeck, M. (2007). Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation, 11(5), 651–665. Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey : swarm intelligence for data mining. Machine Learning, 82(April 2010), 1–42. doi:10.1007/s10994-010-5216-5. Maur, M., Stützle, T., & López-Ibáñez, M. (2010). Pre-scheduled and adaptive parameter variation in MAX-MIN Ant System. In 2010 IEEE Congress on Evolutionary Computation (CEC) (pp. 1–8). Spain, Barcelona: IEEE. doi:10.1109/CEC.2010.5586332. Melo, L., Pereira, F., & Costa, E. (2010). MC-ANT: A multi-colony ant algorithm. In P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, & E. Lutton (Eds.), Lecture Notes in Computer Science: Artifical Evolution (Vol. 5975, pp. 25–36). Heidelberg, Germany: Springer. Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6(4), 333–346. Merkle, D., & Middendorf, M. (2001). Prospects for dynamic algorithm control: Lessons from the phase structure of ant scheduling algorithms. In GECCO 2001-Genetic and Evolutionary Computation Conference Workshop Program. San Francisco, California, USA. Merkle, D., & Middendorf, M. (2005). Swarm intelligence. In E. K. Burke & G. Kendall (Eds.), Search Methodologies (pp. 401–435). U.S.A.: Springer. Meyer, B. (2004). Convergence control in ACO. In M. Keijzer (Ed.), Proceedings of GECCO’04: Genetic and Evolutionary Computation Conference, seattle, washington, late-breaking paper available on CD. X-CD Technologies. Retrieved from http://www.cs.bham.ac.uk/~wbl/biblio/gecco 2004/LBP035.pdf Middendorf, M., Reischle, F., & Schmeck, H. (2000). Information exchange in multi colony ant algorithms. In J. Rolim (Ed.), Lecture Notes in Computer Science (LNCS): Parallel and Distributed Processing (Vol. 1800, pp. 645–652). Heidelberg, Germany: Springer. Mohan, B. C., & Baskaran, R. (2012). A survey : Ant Colony Optimization based recent research and implementation on several engineering domain. Expert Systems with Applications, 39(4), 4618–4627. doi:10.1016/j.eswa.2011.09.076. Monteiro, M., Fontes, D., & Fontes, F. (2012). Ant colony optimization: A literature survey (working paper, no. 474). Retrieved from FEP- Faculty of Engineering, University of Porto: http://www.fep.up.pt/investigacao/workingpapers/ wp474.pdf Moret, B. M. E. (2001). Algorithms and experiments: The new (and old) methodology, 7(5), 434–446. Neyoy, H., Castillo, O., & Soria, J. (2013). Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. In O. Castillo, P. Melin, & J. Kacprzyk (Eds.), Studies in Computational Intelligence (SCI): Recent Advances On Hybrid Intelligent Systems (Vol. 451, pp. 259–271). Heidelberg, Germany: Springer. Neyoy, H., Castillo, O., & Soria, J. (2015). Fuzzy logic for dynamic parameter tuning in ACO and its application in optimal fuzzy logic controller design. (O. Castillo & P. Melin, Eds.). Springer. doi:10.1007/978-3-319-10960-2_1 Olivas, F., Valdez, F., & Castillo, O. (2015). Dynamic parameter adaptation in Ant Colony Optimization using a fuzzy system for TSP problems. In 16th World Congress of The International Fuzzy Systems Association (IFSA) and The 9th Conference of The European Society for Fuzzy Logic and Technology (EUSFLAT) Dynamic (pp. 765–770). Gijón, Asturias, Spain: Published by Atlantis Press. Oliveira, S., Stützle, T., Roli, A., & Dorigo, M. (2011). A Detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP. In Proceedings of GECCO’11: Genetic and Evolutionary Computation Conference (pp. 13–14). doi:10.1145/2001858.2001866 Osman, I., & Laporte, G. (1996). Metaheuristics: A bibliography. In I. Osman & G. Laporte (Eds.), Annals of Operational Research (Vol. 63, pp. 513–628). Baarn/Kluwer, Netherlands: Baltzer Science Publishers. Pellegrini, P. (2006). ACO : parameters, exploration and quality of solutions. Unpublished doctoral dissertation. Universit`a Ca’ Foscari Venezia, Venezia, Italy. Pellegrini, P., & Favaretto, D. (2012). Quantifying the exploration performed by metaheuristics. Journal of Experimental & Theoretical Artificial Intelligence, 24(2), 247–266. doi:10.1080/0952813X.2012.656327 Pellegrini, P., Favaretto, D., & Moretti, E. (2009). Exploration in stochastic algorithms : An application on MAX-MIN ant system. In N. Krasnogor, M. B. Melián-Batista, J. A. M. Pérez, J. M. Moreno-Vega, & D. A. Pelta (Eds.), Studies in computational intelligence (SCI): Nature Inspired Cooperative Strategies for Optimization (NICSO 2008) (Vol. 236, pp. 1–13). Heidelberg, Germany: Springer. Pellegrini, P., Stützle, T., & Birattari, M. (2010a). Off-line vs . on-line tuning : A study on MAX – MIN ant system for the TSP (TR/IRIDIA/2010-009). Bruxelles, Belgium: IRIDIA, Universite Libre de Bruxelles. Pellegrini, P., Stützle, T., & Birattari, M. (2010b). Tuning Max-Min ant system with off-line and on-line methods (TR/IRIDIA/2010-009). Bruxelles, Belgium: IRIDIA, Universite Libre de Bruxelles. Pellegrini, P., Stützle, T., & Birattari, M. (2012). A critical analysis of parameter adaptation in ant colony optimization. Swarm Intelligence, 6(1), 23–48. Perez-Caceres, L., Lopez-Ibanez, M., & Stützle, T. (2014). Ant colony optimization on a budget of 1000. In M. Dorigo, M. Birattari, S. Garnier, H. Hamann, M. M. de Oca, C. Solnon, & T. Stützle (Eds.), Lecture Notes in Computer Science (LNCS): Swarm Intelligence (Vol. 8667, pp. 50–61). Springer Berlin Heidelberg. doi:10.1007/978-3-319-09952-1_5 Pilat, M. L., & White, T. (2002). Using genetic algorithms to optimize ACS-TSP. In M. Dorigo, G. Di-Caro, & M. Sampels (Eds.), Lecture Notes in Computer Science: Ant Algorithms (Vol. 2463, pp. 282–287). Heidelberg, Germany: Springer. Rahmani, P., Dadbakhsh, M., & Gheisari, S. (2012). Improved MACO approach for grid scheduling. In Proceedings of ICIII 2012: The International Conference on Industrial and Intelligent Information (Vol. 31, pp. 135–142). Singapore: IACSIT Press. Rajaraman, A., & Ullman, J. D. (2012). Mining of massive datasets. U.S.A.: Cambridge University Press. Rappos, E., & Hadjiconstantinou, E. (2004). An ant colony heuristic for the design connected flow networks. In M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, & T. Stützle (Eds.), Lecture Notes in Computer Science (Vol. 3172, pp. 270–277). Heidelberg, Germany: Springer. Reinelt, G. (1991). TSPLIB-A traveling salesman problem library. ORSA Journal On Computing. Retrieved from http://comopt.ifi.uniheidelberg. de/software/TSPLIB95/ Rothlauf, F. (2011). Design of modern heuristics: Principles and application. Mainz, Germany: Springer. Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach. New Jersey, U.S.A.: Prentice Hall. Seo, M. (2009). Applications of the ant colony optimization algorithm in combinatorial optimization. Unpublished doctoral dissertation. Pennsylvania State University, Pennsylvania State, U.S. Shyong, J. S., Pengyeng, Y., & Bertrand, M. T. L. (2004). An ant colony optimization algorithm for the minimum weight vertex cover problem. In J. S. Shyong, Y. Pengyyeng, & M. T. L. Bertrand (Eds.), Annals of Operations Research (Vol. 131, pp. 283–304). U.S.A.: Kluwer Academic Publishers. Solimanpur, M., Vrat, P., & Shankar, R. (2005). An ant algorithm for the single row layout problem in flexible manufacturing systems. Computers & Operations Research, 32(3), 583–598. Solnon, C. (2010). Ant colony optimization and constraint programming. U.S.A.: Weley & Sons Ltd. doi:10.1002/9781118557563 Solnon, C., & Fenet, S. (2005). A study of ACO capabilities for solving the maximum clique problem. Journal of Heuristics, 12(3), 155–180. Stützle, T. (1999). Local search algorithms for combinatorial problems: Analysis, improvements, and new applications. Unpublished doctoral dissertation. Technische Universit, Darmstadt, German. Stützle, T. (2004). ACOTSP, Version 1.03. Retrieved from http://www.acometaheuristic. org/aco-code Stützle, T., & Hoos, H. (1999). MAX-MIN ant system and local search for combinatorial optimization problems. In S. Vob, S. Martello, I. H. Osman, & C. Roucairol (Eds.), Meta-Heuristics (pp. 313–329). U.S.A.: Springer. Stützle, T., & Hoos, H. H. (1998). Improvements on ant-system: Introducing MAXMIN ant system. In T. Stützle & H. H. Hoos (Eds.), Artificial Neural Nets and Genetic Algorithms (pp. 245–249). Vienna: Springe. Stützle, T., & Hoos, H. H. (2000). MAX-MIN ant system. FGCS: Future Generation Computer Systems, 16(8), 889–914. Stützle, T., & López-Ibáñez, M. (2013). Automatic (offline) algorithm configuration. In GECCO’13: The Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion (pp. 893–918). New York, USA: ACM. Stützle, T., López-Ibáñez, M., Pellegrini, P., Maur, M., Oca, M. M. De, Birattari, M., & Dorigo, M. (2012). Parameter adaptation in ant colony optimization. In Y. Hamadi, E. Monfroy, & F. Saubion (Eds.), Autonomous Search (pp. 191–215). Heidelberg, Germany: Springer. Taillard, E. (2010). FANT, Retrieved from http://mistic.heigvd.ch/taillard/codes.dir/fant_ qap.c. Talbi, E.-G. (2009). Metaheuristics from design to implementation. U.S.A.: Wiley. Talbi, E.-G., Roux, O., Fonlupt, C., & Robillard, D. (2001). Parallel ant colonies for the quadratic assignment problem. Future Generation Computer Systems, 17(4), 441–449. Theraulaz, G., & Bonabeau, E. (1999). A brief history of stigmergy. Artificial life, 5(2), 97–116. Thomas, J. A., Schonrogge, K., Bonelli, S., Barbero, F., & Balletto, E. (2010). Corruption of ant acoustical signals by mimetic social parasites. Communicative and Integrative Biology, 3(2), 169–171. Venables, H., & Moscardini, A. (2006). An adaptive search heuristic for the capacitated fixed charge location problem. In M. Dorigo, L. M. Gambardella, M. Birattari, A. Martinoli, R. Poli, & T. Stützle (Eds.), Lecture Notes in Computer Science: Ant Colony Optimization and Swarm Intelligence (Vol. 4150, pp. 348–355). Heidelberg, Germany: Springer. Wang, Y. (2013). Adaptive Ant Colony Algorithm for the VRP Solution of Logistics Distribution. Research Journal of Applied Sciences, Engineering and Technology, 6(5), 807–811. Weixin, L., & Huanping, L. (2007). An adaptive parameter control strategy for ant colony optimization. In CIS ’07: The International Conference on Computational Intelligence and Security (pp. 142–146). Harbin: IEEE. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83. Yancang, L., & Wanqing, L. (2007). Adaptive ant colony optimization algorithm based on information entropy. Fundamenta Informaticae, 77(3), 229–242. Ziqiang, L., & Yi, Y. (2014). A hybrid artificial fish-school optimization algorithm for solving the quadratic assignment problem. In 2014 10th International Conference on Natural Computation (ICNC) (pp. 1099–1104). China, Xiamen: IEEE. doi:10.1109/ICNC.2014.6975994. Zhaoquan, C., Han, H., Yong, Q., & Xianheng, M. (2009). Ant colony optimization based on adaptive volatility rate of pheromone trail. International Journal Of Communications, Network And System Sciences, 02, 792–796. Zhifeng, H., Han, H., Yong, Q., & Ruichu, C. (2007). An ACO algorithm with adaptive volatility rate of pheromone trail. In Y. Shi, G. Albada, J. Dongarra, & P. Sloot (Eds.), Lecture Notes in Computer Science: Proceedngs of ICCS2007- The 7th International Conference in Computational Science (Vol. 4490, pp. 1167–1170). Heidelberg, Germany: Springer. Zhifeng, H., Ruichu, C., & Han, H. (2006). An adaptive parameter control strategy for aco. In Proceedings of ICML 2006: The Fifth International Conference on Machine Learning and Cybernetics (pp. 203–206). China, Dalian: IEEE Press. Zhiyong, L., Yong, W., Jianping, Y., Youjia, Z., & Xu, L. (2008). A novel cloudbased fuzzy self-adaptive ant colony system. In ICNC 2008: The 4th International Conference on Natural Computation (pp. 460–465). Jinan, China: IEEE Computer Society. Zhongzhen, Y., Bin, Y., & Chuntian, C. (2007). A parallel ant colony algorithm for bus network optimization. Computer-Aided Civil and Infrastructure Engineering, 22(1), 44–55. Zufferey, N. (2012). Metaheuristics: Some Principles for an Efficient Design. Computer Technology and Application, 3(2012), 446–462. |