A Survey of Swarm Algorithms Applied to Discrete Optimization Problems

Swarm Intelligence and Bio-Inspired Computation

Volumn , Issue , 2013, Pages 169-191

  Krause, Jonas ^a   Cordeiro, Jelson ^a   Parpinelli, Rafael Stubs ^a,b   Lopes, Heitor Silvério A ^a  

^a UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

^b SANTA CATARINA STATE UNIVERSITY   (Brazil)

Author keywords

Binary problems; Bioinspired algorithms; Combinatorial problems; Discrete domain; Swarm Intelligence

Indexed keywords

EID: 84884123869     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-12-405163-8.00007-7     Document Type: Chapter

References (89)

1. Abbass, H., 2001a. A monogenous MBO approach to satisfiability. International Conference on Computational Intelligence for Modelling, Control and Automation. Australia and IEEE ACT Section, Las Vegas, Nevada, July 9-11.
2. Abbass, H.A., 2001b. MBO: marriage in honey bees optimization a haplometrosis polygynous swarming approach. In: IEEE Congress on Evolutionary Computation, vol. 1, pp. 207-214.
3. Bahamish H.A.A., Abdullah R., Salam R.A. Protein conformational search using bees algorithm. Second Asia International Conference on Modelling & Simulation 2008, IEEE Computer Society, Washington, DC, pp. 911-916.
4. Balazs, K., Horvath, Z., Koczy, L., 2012. Hybrid bacterial iterated greedy heuristics for the permutation flow shop problem. In: IEEE Congress on Evolutionary Computation, pp. 1-8.
5. Banati H., Bajaj M. Fire fly based feature selection approach. Int. J. Comput. Sci. 2011, 8(4):473-480.
6. Banerjee S., Dangayach G.S., Mukherjee S.K., Mohanti P.K. Modelling process and supply chain scheduling using hybrid meta-heuristics. Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Vol. 128 of Studies in Computational Intelligence 2008, Springer, Berlin, Germany, pp. 277-300.
7. Banharnsakun, A., Achalakul, T., Sirinaovakul, B., 2010. ABC-GSX: a hybrid method for solving the traveling salesman problem. In: Second World Congress on Nature and Biologically Inspired Computing, pp. 7-12.
8. Banharnsakun A., Sirinaovakul B., Achalakul T. Job shop scheduling with the best-so-far ABC. Eng. Appl. Artif. Intell. 2012, 25(3):583-593.
9. Baykasoǧlu A., Ozbakir L., Tapkan P. Artificial bee colony algorithm and its application to generalized assignment problem. Swarm Intelligence: Focus on Ant and Particle Swarm Optimization 2007, 532-564. I-tech Education and Publishing, Istanbul, Turkey. F.T.S. Chan, M.K. Tiwari (Eds.).
10. Benatchba K., Admane L., Koudil M. Using bees to solve a data-mining problem expressed as a max-sat one. Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Vol. 3562 of Lecture Notes in Computer Science 2005, Springer-Verlag, Heidelberg, pp. 75-86.
11. Burnwal S., Deb S. Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int. J. Adv. Manuf. Technol. 2012, 1-9.
12. Cai Y. Artificial fish school algorithm applied in a combinatorial optimization problem. Int. J. Intell. Syst. Appl. 2010, 1:37-43.
13. Chen H., Li S., Tang Z. Hybrid gravitational search algorithm with random-key encoding scheme combined with simulated annealing. Int. J. Comput. Sci. Net. Secur. 2011, 11(6):208-217.
14. Chong, C.S., Sivakumar, A.I., Low, M.Y.H., Gay, K.L., 2006. A bee colony optimization algorithm to job shop scheduling. In: 38th Winter Conference on Simulation, pp. 1954-1961.
15. Congying, L., Huanping, Z., Xinfeng, Y., 2011. Particle swarm optimization algorithm for quadratic assignment problem. In: International Conference on Computer Science and Network Technology, vol. 3, pp. 1728-1731.
16. Davidovic T., Selmic M., Teodorovic D., Ramljak D. Bee colony optimization for scheduling independent tasks to identical processors. J. Heuristics 2012, 18(4):549-569.
17. Deep, K., Bansal, J.C., 2008. A socio-cognitive particle swarm optimization for multi-dimensional knapsack problem. In: First International Conference on Emerging Trends in Engineering and Technology, pp. 355-360.
18. Deng-Xu, H., Hua-Zheng, Z., Gui-Qing, L., 2011. Glowworm swarm optimization algorithm for solving multi-constrained QoS multicast routing problem. In: Seventh International Conference on Computational Intelligence and Security, pp. 66-70.
19. Dorigo M., Stützle T. Ant Colony Optimization 2004, MIT Press, Cambridge, MA.
20. Falcon, R., Almeida, M., Nayak, A., 2011. Fault identification with binary adaptive fireflies in parallel and distributed systems. In: IEEE Congress on Evolutionary Computation, pp. 1359-1366.
21. Feng, Q., Xiao, Q., 2011. Bacterial evolutionary route planning for unmanned aerial vehicle. In: Second International Conference on Artificial Intelligence, Management Science and Electronic Commerce, pp. 3808-3811.
22. Feng X., Lau F.C.M., Gao D. A new bio-inspired approach to the traveling salesman problem. Complex Sciences. Vol. 5 of Lecture Notes of the Institute of Computer Sciences 2009, 1310-1321. Springer, Berlin, Germany. J. Zhou (Ed.).
23. Fister, I., Yang, X.-S., Fister, I., Brest, J., 2012. Memetic firefly algorithm for combinatorial optimization. In: Fifth International Conference on Bioinspired Optimization Methods and their Applications, pp. 75-86.
24. Garnier S., Gautrais J., Theraulaz G. The biological principles of swarm intelligence. Swarm Intell. 2007, 1(1):3-31.
25. Gherboudj A., Layeb A., Chikhi S. Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm. Int. J. Bio-Inspired Comput. 2012, 4(4):229-236.
26. Glover F., Laguna M. Tabu Search 1997, Kluwer Academic, Norwell, MA.
27. Havens T.C., Spain C.J., Salmon N.G., Keller J.M. Roach infestation optimization. IEEE Swarm Intelligence Symposium 2008, IEEE Press, Piscataway, NJ, pp. 1-7.
28. He, D., Qu, L., Guo, X., 2009. Artificial fish-school algorithm for integer programming. In: International Conference on Information Engineering and Computer Science, pp. 1-4.
29. Hembecker F., Lopes H.S., Godoy W. Particle swarm optimization for the multidimensional knapsack problem. Proceedings of the Eighth International Conference on Adaptive and Natural Computing Algorithms, Part I. Vol. 4431 of Lecture Notes in Computer Science 2007, Springer-Verlag, Heidelberg, pp. 358-365.
30. Hendrawan Y., Murase H. Neural-discrete hungry roach infestation optimization to select informative textural features for determining water content of cultured sunagoke moss. Environ. Control Biol. 2011, 49(1):1-21.
31. Hernández H., Blum C. Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs. Swarm Intell. 2012, 6(2):117-150.
32. Inoue, T., Furuhashi, T., Maeda, H., Takaba, M., 2000. A study on bacterial evolutionary algorithm engine for interactive nurse scheduling support system. In: 26th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, pp. 651-656.
33. Karaboga, D., 2005. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report, Erciyes University, Engineering Faculty, Computer Engineering Department.
34. Karaboga D., Gorkemli B. A combinatorial artificial bee colony algorithm for traveling salesman problem. International Symposium on Intelligent Systems and Applications 2011, IEEE Press, Istanbul, Turkey, pp. 50-53.
35. Kennedy J., Eberhart R. Swarm Intelligence 2001, Morgan Kaufmann, San Francisco, CA.
36. Kennedy, J., Eberhart, R.C., 1997. A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Computational Cybernetics and Simulation, vol. 5, pp. 4104-4108.
37. Koudil M., Benatchba K., Tarabet A., Sahraoui E.B. Using artificial bees to solve partitioning and scheduling problems in codesign. Appl. Math. Comput. 2007, 186(2):1710-1722.
38. Krishnanand, K.N., Ghose, D., 2005. Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 84-91.
39. Kumar, A., Chakarverty, S., 2011. Design optimization for reliable embedded system using cuckoo search. In: Third International Conference on Electronics Computer Technology, vol. 1, pp. 264-268.
40. Labed S., Gherboudj A., Chikhi S. A modified hybrid particle swarm optimization algorithm for multidimensional knapsack problem. Int. J. Comput. Appl. 2011, 34(2):11-16.
41. Layeb A. A novel quantum inspired cuckoo search for knapsack problems. Int. J. Bio-Inspired Comput. 2011, 3:297-305.
42. Li, X., Wang, J., Zhou, J., Yin, M., 2010. An effective GSA based memetic algorithm for permutation flow shop scheduling. In: IEEE Congress on Evolutionary Computation, pp. 1-6.
43. Li, Z., Li, N., 2009. A novel multi-mutation binary particle swarm optimization for 0/1 knapsack problem. In: 21st Annual International Conference on Chinese Control and Decision Conference, pp. 3090-3095.
44. Lin S., Kernighan B.W. An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 1973, 21(2):498-516.
45. Lin T.-L., Horng S.-J., Kao T.-W., Chen Y.-H., Run R.-S., Chen R.-J., et al. An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Syst. Appl. 2010, 37(3):2629-2636.
46. Lopes, H.S., Coelho, L.S., 2005. Particle swarm optimization with fast local search for the blind traveling salesman problem. International Conference on Hybrid Intelligent Systems, pp. 245-250.
47. Lucic, P., Teodorovic, D., 2002. Transportation modeling: an artificial life approach. In: 14th IEEE International Conference on Tools with Artificial Intelligence, pp. 216-223.
48. Marinakis Y., Marinaki M., Matsatsinis N. A hybrid clustering algorithm based on honey bees mating optimization and greedy randomized adaptive search procedure. Learning and Intelligent Optimization 2008, 138-152. Springer-Verlag, Heidelberg. V. Maniezzo, R. Battiti, J.-P. Watson (Eds.).
49. Marinakis, Y., Marinaki, M., Matsatsinis, N.F., 2009. A hybrid discrete artificial bee colony-GRASP algorithm for clustering. In: International Conference on Computers & Industrial Engineering, pp. 548-553.
50. Miwa, M., Inoue, T., Matsuzaki, M., Furuhasi, T., Okuwa, S., 2002. Nurse scheduling system using bacterial evolutionary algorithm hardware. In: 28th Annual Conference of the IEEE Industrial Electronics Society, vol. 3, pp. 1801-1805.
51. Mladenovic N., Hansen P. Variable neighborhood search. Comput. Oper. Res. 1997, 24:1097-1100.
52. Nakamura R.Y.M., Pereira L.A.M., Costa K.A., Rodrigues D., Papa J.P., Yang X.-S. BBA: a binary bat algorithm for feature selection. Conference on Graphics, Patterns and Images 2012, IEEE Computer Society, Los Alamitos, CA, pp. 291-297.
53. Nawa N., Furuhashi T. Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans. Fuzzy Syst. 1999, 7(5):608-616.
54. Ozbakir L., Baykasoǧlu A., Tapkan P. Bees algorithm for generalized assignment problem. Appl. Math. Comput. 2010, 215(11):3782-3795.
55. Pacurib, J.A., Seno, G.M.M., Yusiong, J.P.T., 2009. Solving Sudoku puzzles using improved artificial bee colony algorithm. In: Fourth International Conference on Innovative Computing, Information and Control, pp. 885-888.
56. Palit, S., Sinha, S.N., Molla, M.A., Khanra, A., Kule, M., 2011. A cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm. In: Second International Conference on Computer and Communication Technology, pp. 428-432.
57. Pan Q.-Q., Tasgetiren M.F., Liang Y.-C. A discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem. Comput. Oper. Res. 2008, 35(9):2807-2839.
58. Papa, J.P., Pagnin, A., Schellini, S.A., Spadotto, A., Guido, R.C., Ponti, M., et al., 2011. Feature selection through gravitational search algorithm. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2052-2055.
59. Parpinelli R.S., Lopes H.S. New inspirations in swarm intelligence: a survey. Int. J. Bio-Inspired Comput. 2011, 3(1):1-16.
60. Passino K.M. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 2002, 22(3):52-67.
61. Pham, D., Koc, E., Lee, J., Phrueksanant, J., 2007a. Using the bees algorithm to schedule jobs for a machine. In: Proceedings of Eighth International Conference on Laser Metrology, CMM and Machine Tool Performance, pp. 430-439.
62. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M., 2005. The Bees Algorithm. Technical Note, Manufacturing Engineering Center, Cardiff University, UK.
63. Pham, D.T., Otri, S., Afify, A., Mahmuddin, M., Al-Jabbouli, H., 2007b. Data clustering using the bees algorithm. In: 40th CIRP International Seminar on Manufacturing Systems.
64. Pulikanti S., Singh A. An artificial bee colony algorithm for the quadratic knapsack problem. The 16th International Conference on Neural Information Processing. Vol. 5864 of Lecture Notes in Computer Science 2009, Springer-Verlag, Heidelberg, pp. 196-205.
65. Rabanal P., Rodríguez I., Rubio F. Using river formation dynamics to design heuristic algorithms. Unconventional Computation. Vol. 4618 of Lecture Notes in Computer Science 2007, 163-177. Springer, Heidelberg. S.G. Akl, C.S. Calude, M.J. Dinneen, G. Rozenberg, T. Wareham (Eds.).
66. Rashedi E., Nezamabadi-Pour H., Saryazdi S. GSA: a gravitational search algorithm. Inf. Sci. 2009, 179(13):2232-2248.
67. Rosendo M., Pozo A. Applying a discrete particle swarm optimization algorithm to combinatorial problems. Brazilian Symposium on Neural Networks 2010, IEEE Computer Society, Los Alamitos, CA, pp. 235-240.
68. Sayadi M.K., Ramezanian R., Ghaffari-Nasab N. A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. Int. J. Ind. Eng. Comput. 2010, 1:1-10.
69. Shah-Hosseini, H., 2007. Problem solving by intelligent water drops. In: IEEE Congress on Evolutionary Computation, pp. 3226-3231.
70. Shen X., Li Y., Chen C., Yang J., Zhang D. Greedy continuous particle swarm optimisation algorithm for the knapsack problems. Int. J. Comput. Appl. Technol. 2012, 44(2):137-144.
71. Singh V., Singh D., Tiwari R., Shukla A. RGBCA-genetic bee colony algorithm for travelling salesman problem. World Congress on Information and Communication Technologies 2011, IEEE Press, Gwalior, India, pp. 1002-1008.
72. Tasgetiren, M., Liang, Y., Sevkli, M., Gencyilmaz, G., 2004a. Particle swarm optimization algorithm for makespan and maximum lateness minimization in permutation flow shop sequencing problem. In: Fourth International Symposium on Intelligent Manufacturing Systems, pp. 237-250.
73. Tasgetiren, M., Sevkli, M., Liang, Y.-C., Gencyilmaz, G., 2004b. Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1412-1419.
74. Tasgetiren M.F., Suganthan P.N., Pan Q.-Q. A discrete particle swarm optimization algorithm for the generalized traveling salesman problem. Ninth Annual Conference on Genetic and Evolutionary Computation 2007, ACM Press, New York, NY, 158-165.
75. Teo, J., Abbass, H.A., 2001. An annealing approach to the mating-flight trajectories in the marriage in Honey Bees Optimization algorithm. Technical Report CA04/01, School of Computer Science, University of New South Wales at ADFA, Canberra, Australia.
76. Teodorovic, D., Dell'Orco, M., 2005. Bee colony optimization-a cooperative learning approach to complex transportation problems. In: 16th Mini-Euro Conference on Advanced OR and AI Methods in Transportation, pp. 51-60.
77. Ucar, H., Tasgetiren, M.F., 2006. A particle swarm optimization algorithm for permutation flow shop sequencing problem with the number of tardy jobs criterion. In: Fifth International Symposium on Intelligent Manufacturing Systems, pp. 237-250.
78. Verma, R., Kumar, S., 2012. DSAPSO: DNA sequence assembly using continuous particle swarm optimization with smallest position value rule. In: First International Conference on Recent Advances in Information Technology, pp. 410-415.
79. Wan, N.F., Nolle, L., 2009. Solving a multi-dimensional knapsack problem using hybrid particle swarm optimization algorithm. In: 23rd European Conference on Modelling and Simulation.
80. Wong L.-P., Low M.Y., Chong C.S. A bee colony optimization algorithm for traveling salesman problem. Second Asia International Conference on Modelling & Simulation 2008, IEEE Press, Singapore, pp. 818-823.
81. Wong, L.-P., Puan, C.Y., Low, M.Y.H., Chong, C.S., 2008b. Bee colony optimization algorithm with big valley landscape exploitation for job shop scheduling problems. In: Proceedings of the 40th Winter Simulation Conference, pp. 2050-2058.
82. Xiangyang S., Minghao Z., Yamin Z. Improved artificial fish school algorithm to solve knapsack problem. Comput. Eng. Appl. 2011, 47(21):43.
83. Xu S., Ji Z., Pham D.T., Yu F. Bio-inspired binary bees algorithm for a two-level distribution optimisation problem. J. Bionic Eng. 2010, 7:161-167.
84. Yang C., Chen J., Tu X. Algorithm of marriage in honey bees optimization based on the Nelder-Mead method. International Conference on Intelligent Systems and Knowledge Engineering. Advances in Intelligent Systems Research 2007, 1-7. Atlantis Press, Amsterdam, The Netherlands. T. Li, Y. Xu, D. Ruan (Eds.).
85. Yang X.S. Firefly algorithm. Nature-inspired Metaheuristic Algorithms 2008, Luniver Press, Bristol, UK, (Chapter 8).
86. Yang X.S. Firefly algorithm, Lévy flights and global optimization. XXVI Research and Development in Intelligent Systems 2009, Springer, London, UK, pp. 209-218.
87. Yang X.S. A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization. Vol. 284 of Studies in Computational Intelligence 2010, Springer, Berlin, pp. 65-74.
88. Yang, X.-S., Deb, S., 2009. Cuckoo search via Lévy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210-214.
89. Yousif A., Abdullah A.H., Nor S.M., Abdelaziz A.A. Scheduling jobs on grid computing using firefly algorithm. J. Theor. Appl. Inf. Technol. 2011, 33(2):155-164.