Advertisement

On Some Aspects of Nature-Based Algorithms to Solve Multi-Objective Problems

Part of the Studies in Computational Intelligence book series (SCI, volume 427)

Abstract

This chapter presents an overview of various nature-based algorithms to solve multi-objective problems with the particular emphasis on Multi-Objective Evolutionary Algorithms based on Genetic Algorithm. Some of the significant hybridization and the modification of the benchmark algorithms have also been discussed as well. The complexity issues have been outlined and various test problems to show the effectiveness of such algorithms have also been summarized. At the end, a brief discussion on the software packages used to model these type of algorithms are presented.

Keywords

Nature based algorithms Multi-Objective Evolutionary Algorithm Hybrid algorithm Complexity Test Problem 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pareto, V.: Cours d’e_conomie politique professe_ a_ l’universite_de Lausanne, vol. 1, 2. F. Rouge, Laussanne (1896) Google Scholar
  2. 2.
    Hung, S.-J.: Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach. Expert Systems with Applications 38(8), 9053–9062 (2011)CrossRefGoogle Scholar
  3. 3.
    Mirakhorli, A.: Multi-objective optimization of reverse logistics network with fuzzy demand and return-product using an interactive fuzzy goal programming approach. In: 40th International Conference on Computers and Industrial Engineering: Soft Computing Techniques for Advanced Manufacturing and Service Systems, Awaji Island, Japan (2010)Google Scholar
  4. 4.
    Wu, C., Barnes, D., Rosenberg, D., Luo, X.: An analytic network process-mixed integer multi-objective programming model for partner selection in agile supply chains. Production Planning & Control 20(3), 254–275 (2009)CrossRefGoogle Scholar
  5. 5.
    Susmita, B., Bhattacharya, R.: Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Mamnufacturing (2012), doi: 10.1007/s10845-011-0617-2Google Scholar
  6. 6.
    Eric, B., Marco, D., Guy, T.: Swarm Intelligence From Natural to Artificial Systems. Oxford University Press, New York (1999)MATHGoogle Scholar
  7. 7.
    Faro, J., Combadao, J., Gordo, I.: Did Germinal Centers Evolve Under Differential Effects of Diversity vs Affinity? In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 1–8. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Coello Coello, C.A., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, Berlin (2007)MATHGoogle Scholar
  9. 9.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  10. 10.
    Goldberg David, E.: Genetic Algorithms in Search, Optimization & Machine Learning, Fifth Indian Reprint. Pearson Education, Delhi (1989)Google Scholar
  11. 11.
    Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computations 2(3), 221–248 (1994)CrossRefGoogle Scholar
  12. 12.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computing 6(2), 182–197 (2002)CrossRefGoogle Scholar
  13. 13.
    Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar
  14. 14.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailou, P., Fogarty, T. (eds.) EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100 (2001)Google Scholar
  15. 15.
    Knowles Joshua, D., Corne David, W.: Approximating the Nondominated Front Using teh Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)CrossRefGoogle Scholar
  16. 16.
    Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Proceeding of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computatyional Intelligence, vol. 1, pp. 82–87. IEEE Service Center, Piscataway (1994)Google Scholar
  17. 17.
    Erickson, M., Mayer, A., Horn, J.: The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 681–695. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. 18.
    Corne, D.W., Knowles, J.D., Oates, M.J.: The Pareto Envelope-based Selection Algorithm for Multiobjective Optimization. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 839–848. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: Regionbased Selection in Evolutionary Multiobjective Optimization. In: Spector, L., Goosman, E.D., Wu, A., Langdon, W., Voigt, H.M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 283–290. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  20. 20.
    Veldhuizen, D.A., van Lamont, G.B.: Multiobjective Optimization with Messy Genetic Algorithms. In: Proceedings of the 2000 ACM Symposium on Applied Computing. ACM, Villa Olmo (2000)Google Scholar
  21. 21.
    Deb, K.: Binary and Floating-Point Function Optimization using Messy Genetic Algorithms. PhD Thesis, University of Alabama, Tuscaloosa, Alabama (1991)Google Scholar
  22. 22.
    Coello Coello, C.A., Toscano Pulido, G.: A Micro-Genetic Algorithm for Multiobjective Optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 126–140. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  23. 23.
    Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100. Lawrence Erlbaum, Hillsdale (1985)Google Scholar
  24. 24.
    Hajela, P., Lin, C.Y.: Genetic search strategies in multicriterion optimal design. Structural Optimization 4, 99–107 (1992)CrossRefGoogle Scholar
  25. 25.
    Lu, H., Yen, G.G.: Rank-density-based multiobjective genetic algorithm and benchmark test function study. IEEE Transactions on Evolutionary Computation 7(4), 325–343 (2003)CrossRefGoogle Scholar
  26. 26.
    Fourman Michael, P.: Compaction of Symbolic Layout using Genetic Algorithms. In: Grefenstette, J.J. (ed.) Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 141–153. Lawrence Erlbaum, Hillsdale, Hillsdale (1985)Google Scholar
  27. 27.
    Eberhart, R.C., Kenndy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth Symposium on Micro Machine and Human Science, pp. 39–43. IEEE Service Center, Piscataway (1995)CrossRefGoogle Scholar
  28. 28.
    Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Porto, V.W., et al. (eds.) Evolutionaey Programming, vol. VII, pp. 611–616. Springer (1998)Google Scholar
  29. 29.
    Durillo, J.J., Nebro, A.J., García-Nieto, J., Alba, E.: On the Velocity Update in Multi-Objective Particle Swarm Optimizers. In: Coello Coello, C.A., Dhaenens, C., Jourdan, L. (eds.) Advances in Multi-Objective Nature Inspired Computing. SCI, vol. 272, pp. 45–62. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  30. 30.
    Reyes-Sierra, M., Coello Coello, C.A.: Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ε-Dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  31. 31.
    Durillo, J.J., García-Nieto, J., Nebro, A.J., Coello Coello, C.A., Luna, F., Alba, E.: Multi-Objective Particle Swarm Optimizers: An Experimental Comparison. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 495–509. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  32. 32.
    Ratnaweera, A., Halgamuge, S., Watson, H.: Self-organizing hierarchical particle swarm optimizer with time-varying accelration coefficients. International Journal of Computational Intelligence Research 8(3), 240–255 (2004)Google Scholar
  33. 33.
    Storn, R., Price, K.V.: Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report, ICSI, University of California, Berkeley (1995)Google Scholar
  34. 34.
    Chang, C.S., Xu, D., Quek, H.: Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mas transit system. IEE Proceedings on Electric Power Applications 146(5), 577–583 (1999)CrossRefGoogle Scholar
  35. 35.
    Saku, K., Jouni, L.: Generalized Differential Evolution for Constrained Multi-Objective Optimization. In: Thu, B.L., Sameer, A. (eds.) Multi-Objective Optimization in Computational Intelligence Theory and Practice, pp. 43–75. Information Science Reference, USA (2008)Google Scholar
  36. 36.
    Bergey, P.K.: An agent enhanced intelligent spreadsheet solver for multicriteria decision making. In: Proceedings of teh Fifth American Conference on Information Systems (AMCIS 1999), Milwaukee, WI, pp. 966–968 (1999)Google Scholar
  37. 37.
    Abbass, H.A.: The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), Honolulu, HI, pp. 831–836. IEEE Service Center (2002)Google Scholar
  38. 38.
    Madavan, N.K.: Multi-objective optimization usaing a Pareto differential evolution approach. In: Proceedings of the 2002 Congress on Evlutionary Computation (CEC 2002), Honolulu, HI, pp. 1145–1150. IEEE Service Center (2002)Google Scholar
  39. 39.
    Zaharie, D.: Multi-objective optimization with adaptive Pareto differential evolution. In: Proceedings of Symposium on Intelligent Systems and Applications (SIA 2003), Iasi, Romania (2003)Google Scholar
  40. 40.
    Xue, F., Sanderson, A.C., Graves, R.J.: Pareto-based multi-objective differential evolution. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, pp. 862–869. IEEE Service Center (2003)Google Scholar
  41. 41.
    Parsopoulos, K.E., Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Vector evaluated differential evolution for multiobjective optimization. In: Proceedings of the 2004 Congress on Evolutionary Computation (CEC 2004), Portland, OR, pp. 204–211. IEEE Service Center (2004)Google Scholar
  42. 42.
    Li, H., Zhang, Q.: A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 583–592. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  43. 43.
    Hernáandez-Diaz, A.G., Santana-Quintero, L.V., Coello Coello, C.A., Caballero, R., Molina, J.: A new proposal for multi-objective optimization using differenetial evolution and rough set theory. In: Proceeings of the Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, WA, pp. 675–682. ACM Press (2006)Google Scholar
  44. 44.
    Bersini, H., Varela, F.J.: A Variant of Evolution Strategies for Vector Optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  45. 45.
    Yoo, J., Hajela, P.: Immune network simulations in multicriterion design. Structural Optimization 18, 85–94 (1999)Google Scholar
  46. 46.
    Gambardella, L.M., Dorigo, M.: Ant-Q: A reinforcement learning approach to teh traveling salesman problem. In: Prieditis, A., Russell, S. (eds.) Proceedings of the 12th International Conference on Machine Learning, pp. 252–260. Morgan Kaufmann (1995)Google Scholar
  47. 47.
    Mariano, C.E., Morales, E.: MOAQ an Ant-Q algorithm for multiple objective optimization problems. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Genetic and Evolutionary Compouting Conference (GECCO 1999), vol. I, pp. 894–901. Morgan Kaufmann, San Francisco (1999)Google Scholar
  48. 48.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)MathSciNetMATHCrossRefGoogle Scholar
  49. 49.
    Serafini, P.: Simulated Annealing for Multiple Objective Optimization Problems. In: Tzeng, G., Wang, H., Wen, U., Yu, P. (eds.) Proceedings of the 10th International Conference on Multiple Criteria Decision Making: Expand and Enrich the Domains of Thinking and Application, vol. I, pp. 283–294. Springer, Berlin (1994)Google Scholar
  50. 50.
    Glover, F.: Future paths for integer programming and links to Artificial Intelligence. Computers and Opereations Research 13(5), 533–549 (1986)MathSciNetMATHCrossRefGoogle Scholar
  51. 51.
    Gandibleux, X., Mezdaoui, N., Fréville: A Tabu Search Procedure to Solve Combinatorial Optimisation Porblems. In: Caballero, R., Ruiz, F., Steuer, R.E. (eds.) Advances in Multiple Objective and Goal Programming. LNEMS, vol. 455, pp. 291–300. Springer (1997)Google Scholar
  52. 52.
    Huang, J., Huang, X., Ma, Y., Lin, Y.: On a high-dimensional objective genetic algorithm and its nonlinear dynamic properties. Communications in Nonlinear Science and Numerical Simulation 16(9), 3825–3834 (2011)MathSciNetMATHCrossRefGoogle Scholar
  53. 53.
    Kumar, R., Rockett, P.I.: Improved sampling of the Pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm. Evolutionary Computation 10(3), 283–314 (2002)CrossRefGoogle Scholar
  54. 54.
    Yang, X., Shi, Y.: A Real-coded Quantum Clone Multi-Objective Evolutionary Algorithm. In: 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet 2011), XianNing, April 16-18, pp. 4683–4687 (2011)Google Scholar
  55. 55.
    Nie, L., Gao, L., Li, P., Wang, X.: Multi-Objective Optimization for Dynamic Single-Machine Scheduling. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part II. LNCS, vol. 6729, pp. 1–9. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  56. 56.
    Pachón, V., Mata, J., Domínguez, J.L., Maña, M.J.: Multi-objective Evolutionary Approach for Subgroup Discovery. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part II. LNCS, vol. 6679, pp. 271–278. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  57. 57.
    Nicola, B., Marco, L., Günter, R.: Convergence Rates of SMS-MOEA on Continuous Bi-Objective Problem Classes. In: FOGA 2011, Schwarzenberg, Austria, January 5-9 (2011)Google Scholar
  58. 58.
    James, B., Chris, A.: The cross-entropy method in multi-objective optimization: An asessment. European Journal of Operational Research 211(1), 112–121 (2011)MathSciNetMATHCrossRefGoogle Scholar
  59. 59.
    Shin, K.S., Park, J.-O., Kim, Y.K.: Multi-Objective FMS process planning with variuous flexibilities using a symbiotic evolutionary algorithm. Computers and Operations Research 38(3), 702–712 (2011)MathSciNetMATHCrossRefGoogle Scholar
  60. 60.
    Taher, N., Ehsan, A.F., Majid, N.: An efficient multi-objective modified shuffled frog leaping algorithm for distribution feeder configuration problem. European Transactions on Electrical Power 21(1), 721–739 (2010)Google Scholar
  61. 61.
    Li, Z.-Y., Chen, C., Ren, C.-A., Mohammed Esraa, M.: Novel Objective-Space Dividing Multi-Objectives Evolutionary Algorithm and its Convergence Property. In: 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), Changsha, September 23-26, pp. 372–379 (2010)Google Scholar
  62. 62.
    Zhang, G., Li, Y., Marian, G.: A Multi-Objective Membrane Algorithm for Knapsack Problems. In: 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), Changsha, September 23-26, pp. 604–609 (2010)Google Scholar
  63. 63.
    Mo, L., Dai, G., Zhu, J.: The RM-MEDA Based on Elitist Strategy. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds.) ISICA 2010. LNCS, vol. 6382, pp. 229–239. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  64. 64.
    Li, H., Landa-Silva, D.: An Elitist GRASP Metaheuristic for the Multi-objective Quadratic Assignment Problem. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 481–494. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  65. 65.
    Taher, N.: An efficient multi-objective HBMO algorithm for distribution feeder configuration. Expert Systems with Applications 38(3), 2878–2887 (2011)CrossRefGoogle Scholar
  66. 66.
    Tabatabaei, S.M., Vahidi, B., Hosseinian, S.H., Madani, S.M.: Bacterial Foraging-Based Solution for Optimal Capacitor Allocation in Distribution Systems. In: 2010 IEEE International Conference on Power and Energy (PECon 2010), Kuala Lumpur, Malaysia, November 29-December 1, pp. 253–258 (2010)Google Scholar
  67. 67.
    Reynolds, R.G.: An Introduction to Cultural Algorithms. In: Sebald, A.V., Fogel, L.J. (eds.) Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131–139. World Scientific, River Edge (1994)Google Scholar
  68. 68.
    Coello Coello, C.A., Landa, B.R.: Evolutionary Multiobjective Optimization using A Cultural Algorithm. In: 2003 IEEE Swarm Intelligence Symposium Proceedings, Indianapolis, Indiana, USA, pp. 6–13. IEEE Service Center (April 2003)Google Scholar
  69. 69.
    Yang, X.-S.: Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  70. 70.
    Yang, X.-S., Deb, S.: Cuckoo Search via Lévy Flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), India, pp. 210–214. IEEE, USA (2009)CrossRefGoogle Scholar
  71. 71.
    Esmat, R., Hossein, N.-P., Saeid, S.: GSA: A Gravitational Search Algorithm. Information Sciences 179(13), 2232–2248 (2009)MATHCrossRefGoogle Scholar
  72. 72.
    Hadi, N., Mahdi, N., Patrick, S.: Non-dominated Sorting Gravitational Search Algorithm. In: International Conference on Swarm Intelligence (ICSI 2011), Cergy, France, June 14-15, pp. 1–10 (2011)Google Scholar
  73. 73.
    Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mechanica 213(3-4), 267–289 (2010)MATHCrossRefGoogle Scholar
  74. 74.
    Shah-Hosseini: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation 1(1-2), 71–79 (2009)CrossRefGoogle Scholar
  75. 75.
    Pablo, R., Ismael, R., Fernando, R.: Using River Formation Dynamics to Design Heuristic Algorithms. Springer (2007) ISBN 978-3-540-73553-3Google Scholar
  76. 76.
    Vicsek, T., Czirok, A., Ben-Jacob, E., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Physical Reviews Letters 75, 1226–1229 (1995)CrossRefGoogle Scholar
  77. 77.
    María, L.J., Raúl, R.J., Sebastián, V.: G3PARM: A Grammar Guided Genetic Programming Algorithm for Mining Association Rules. In: 2010 IEEE Congress on Evolutionary Computation (CEC), Barcelona, July 18-23, pp. 1–8 (2010)Google Scholar
  78. 78.
    Baños, R., Gil, C., Reca, J., Ortega, J.: A Pareto-based memetic algorithm for optimization of looped water distribution systems. Engineering Optimization 42(3), 223–240 (2010)CrossRefGoogle Scholar
  79. 79.
    Usman, F., Lam, C.P.: A Max-Min Multiobjective Technique to Optimize Model Based Test Suite. In: 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, Daegu, May 27-29, pp. 569–574 (2009)Google Scholar
  80. 80.
    Wang, X., Yu, S.-H., Dai, J., Luo, T.: A Multiple Constraint Quality of Service Routing Algorithm Base on Dominating Tree. In: International Conference on Computational Intelligence and Software Engineering (CISE 2009), Wuhan, December 11-13, pp. 1–4 (2009)Google Scholar
  81. 81.
    Juan, T.J., Vallego Edgar, E., Enrique, M.: MOCEA: A Multi Objective Clustering Evolutionary Algorithm for Inferring Protein-Protein Functional Interactions. In: GECCO 2009, Montréal, Québec, Canada, July 8-12, pp. 1793–1794 (2009)Google Scholar
  82. 82.
    Basgalupp Márcio, P., Barros Rodrigo, C., Carvalho André, C.P.L.F., de Freitas Alex A., Ruiz Duncan, D.: LEGAL-Tree: A Lexicographic Multi-Objective Genetic Algorithm for Decision Tree Induction. In: SAC 2009, Honolulu, Hawaii, USA, March 8-12, pp. 1085–1090 (2009)Google Scholar
  83. 83.
    Li, M., Zheng, J., Li, K., Wu, J., Xiao, G.: An Spanning Tree Based Method for Pruning Non-Dominated Solutions in Multi-Objective Optimization Problems. In: Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA, pp. 4882–4887 (October 2009)Google Scholar
  84. 84.
    Fallah-Jamshidi, S., Karimi, N., Zandieh, M.: A hybrid multi-objective genetic algorithm for planning order release date in two-level assembly system with random lead times. Expert Systems with Applications 38(11), 13549–13554 (2011)Google Scholar
  85. 85.
    Andreas, K., Kun, Y.: Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Applied Soft Computing 11(6), 4117–4134 (2011)CrossRefGoogle Scholar
  86. 86.
    Behnamian, J., Zandieh, M., Ghomi, S.M.T., Fatemi: Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min-max technique. Soft Computing 15(7), 1313–1331 (2011)Google Scholar
  87. 87.
    Noman, Q.S., Mariyam, S.S.: Memetic Elitist Pareto Differential Evolution Algorithm based Radial Basis Function Networks for Classification Problems. Applied Soft Computing 11(8), 5565–5581 (2011)CrossRefGoogle Scholar
  88. 88.
    Lu, Y., Zhou, J., Qin, H., Wang, Y., Zhang, Y.: A hybrid multi-objective cultural algorithm for short-term environmental/economic hydrothermal scheduling. Energy Conversion and Management 52(5), 2121–2134 (2011)CrossRefGoogle Scholar
  89. 89.
    Vidal Juan, C., Manuel, M., Alberto, B., Manuel, L.: Machine scheduling in custom furniture industry through neuro-evolutionary hybridization. Applied Soft Computing 11(2), 1600–1613 (2011)CrossRefGoogle Scholar
  90. 90.
    Sivakumar, K., Balamurugan, C., Ramabalan, S.: Concurrent multi-objective tolerance allocation of mechanical asemblies considering alternative manufacturing process selection. International Journal of Advanced Manufacturing Technology 53(5-8), 711–732 (2011)CrossRefGoogle Scholar
  91. 91.
    Chen, W., Shi, Y.-J., Teng, H.-F.: A Generalized Differential Evolution Combined with EDA for Multi-objective Optimization Problems. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 140–147. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  92. 92.
    Fernández, J.C., Hervás, C., Martínez-Estudillo, F.J., Gutiérrez, P.A.: Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology. Applied Soft Computing 11(1), 534–550 (2011)CrossRefGoogle Scholar
  93. 93.
    Zhang, J., Zhang, Y., Qin, P.: Immune Clonal Differential Evolution Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problem. In: 2010 International Conference on Artificial Intelligence and Education (ICAIE), Hangzhou, October 29-30, pp. 73–76 (2010)Google Scholar
  94. 94.
    Jarosz, P., Burczyski, T.: Coupling of Immune Algorithms and Game Theory in Multiobjective Optimization. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 500–507. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  95. 95.
    Xiao, G., China, G., Mei, J.: Reactive Power Optimization Based on Hybrid Particle Swarm Optimization Algorithm. In: 2010 Asia-Pacific Conference on Wearable Computing Systems, pp. 173–177 (2010)Google Scholar
  96. 96.
    Almeida Leandro, M., Ludermir Teresa, B.: A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks. Neurocomputing 73(7-9), 1438–1450 (2010)CrossRefGoogle Scholar
  97. 97.
    Abhay, K., Deepak, S., Kalyanmoy, D.: A Hybrid Multi-Objective Optimization Procedure Using PCX Based NSGA-II and Sequential Quadratic Programming. In: IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, September 25-28, pp. 3011–3018 (2007)Google Scholar
  98. 98.
    Murugan, P., Kannan, S., Baskar, S.: Application of NSGA-II Algorithm to Single-Objective Transmission Constrained Generation Expansion Planning. IEEE Transactions on Power Systems 24(4), 1790–1797 (2009)CrossRefGoogle Scholar
  99. 99.
    Wang, M., Dai, G., Hu, H.: Improved NSGA-II algorithm for optimization of constrained functions. In: 2010 International Conference on Machine Vision and Human-Machine Interface (MVHI), Kaifeng, China, April 24-25, pp. 673–675 (2010)Google Scholar
  100. 100.
    Masahiko, S., Aguirre Hernán E., Kiyoshi, T.: Effects of δ-Similar Elimination and Controlled Elitism in the NSGA-II Multiobjective Evolutionary Algorithm. In: 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, July 16-21, pp. 1164–1171 (2006)Google Scholar
  101. 101.
    Yu, L., Wang, P., Zhu, H.: A Novel Diversity Preservation Strategy based on Ranking Integration for Solving Some Specific Multi-Objective Problems. In: 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Hong Kong, August 10-12, pp. 97–101 (2010)Google Scholar
  102. 102.
    Qiang, Y., Zhao, J.-J., Chen, J.-J., Wang, X.-G.: Workload Control of Autonomic Database. In: 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS), Shenzhen, December 19-20, pp. 263–267 (2009)Google Scholar
  103. 103.
    Mansour, M.R., Santos, A.C., London Jr., J.B., Delbem, A.C.B., Bretas, N.G.: Node-depth Encoding and Evolutionary Algorithms Applied to Service Restoration in Distribution Systems. In: 2010 IEEE Power and Energy Society General Meeting, Minneapolis, MN, July 25-29, pp. 11–18 (2010)Google Scholar
  104. 104.
    Lakashminarasimman, N., Baskar, S., Alphones, A.: Multiobjective Mobile Antenna Location Identification using Evolutionary Optimization Algorithm. In: 2010 Second International Conference on Computing, Communication and Networking Technologies, Karur, July 29-31, pp. 1–4 (2010)Google Scholar
  105. 105.
    dos Santos, C.L., Piergiorgio, A.: Multiobjective Electromagnetic Optimization Based on a Nondominated Sorting Genetic Approach with a Chaotic Crossover Operator. IEEE Transactions on Magnetics 44(6), 1078–1081 (2008)CrossRefGoogle Scholar
  106. 106.
    Hernán, A., Kiyoshi, T.: Adaptive ε-Ranking on MNK-Landscapes. In: 2009 IEEE Symposium on Computational Intelligence in Miulti-Criteria Decision-Making (MCDM 2009), Nashville, TN, March 30-April 2, pp. 104–111 (2009)Google Scholar
  107. 107.
    Sun, Y., Shen, G.: Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism. Chinese Journal of Aeronautics 21(6), 540–549 (2008)CrossRefGoogle Scholar
  108. 108.
    Jia, J., Chen, J., Chang, G.-R.: Efficient Cover Set Selection in Wireless Sensor Networks. Acta Automatica Sinica 34(9), 1157–1162 (2008)CrossRefGoogle Scholar
  109. 109.
    Nawaz, R.K.S., Siddique, N.H., Jim, T.: Improved precedence preservation crossover for multi-objective job shop scheduling problem. Evolving Systems 2, 119–129 (2011)CrossRefGoogle Scholar
  110. 110.
    Onety, R.E., Moreira, G.J.P., Neto, O.M., Takahashi, R.H.C.: Variable Neighborhood Multiobjective Genetic Algorithm for the Optimization of Routes on IP Networks. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 433–447. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  111. 111.
    Santosh, T., Georges, F., Kalyanmoy, D.: AMGA2: improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization. Engineering Optimization 43(4), 377–401 (2011)CrossRefGoogle Scholar
  112. 112.
    Eduardo, F., Edy, L., Fernando, L., Coello Coello, C.A.: Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method. Information Sciences 181(1), 44–56 (2011)MathSciNetMATHCrossRefGoogle Scholar
  113. 113.
    Wang, L., Liang, Y., Yang, J.: Improved Multi-Objective PSO Algorithm for Optimization Problems. In: 2010 IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, December 10-12, pp. 195–198 (2010)Google Scholar
  114. 114.
    Sun, C.: An improved differential evolution and novel crowding distance metric for multi-objective optimization. In: 2010 3rd International Symposium on Knowledge Acquisition and Modeling, Wuhan, October 20-21, pp. 265–268 (2010)Google Scholar
  115. 115.
    Hisao, I., Noritaka, T., Yusuke, N.: Diversity/improvement by Non-Geometric Binary Crossover in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 14(6), 985–998 (2010)CrossRefGoogle Scholar
  116. 116.
    Kalyanmoy, D.: Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Technical Report No. CI-49/98, Department of Computer Science/XI, University of Dortmund, Germany (October 1998)Google Scholar
  117. 117.
    Viennet, R., Fontiex, C., Marc, I.: Multicriteria Optimization Using a Genetic Algorithm for Determining a Pareto Set. Journal of Systems Science 27(2), 255–260 (1996)MATHCrossRefGoogle Scholar
  118. 118.
    Saxena, D.K., Zhang, Q., Duro, J.A., Tiwari, A.: Framework for Many-Objective Test Problems with Both Simple and Complicated Pareto-Set Shapes. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 197–211. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  119. 119.
    Trautmann, H., Ligges, U., Mehnen, J., Preuß, M.: A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 825–836. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  120. 120.
    Wagner, T., Trautmann, H., Naujoks, B.: OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 198–215. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  121. 121.
    Liang, J.J., Suganthan, P.N., Deb, K.: Novel Composition Test Functions for Numerical Global Optimization. In: Proceedings of the 2005 IEEE Symposium on Swarm Intelligence (SIS 2005), June 8-10, pp. 68–75 (2005)Google Scholar
  122. 122.
    Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), Honolul, HI, USA, May 12-17, pp. 825–830 (2002)Google Scholar
  123. 123.
    Arnaud, L., Laetitia, J., El-Ghazali, T.: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO. European Journal of Operational Research 209(2), 104–112 (2011)MathSciNetCrossRefGoogle Scholar
  124. 124.
    Gao, G., Zhang, G., Huang, G., Gu, P., Liu, F.: Improved Multi-objective Evolutionary Algorithm Based on Three-way Radix Quicksort. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Chengdu, July 9-11, pp. 378–382 (2010)Google Scholar
  125. 125.
    Sun, H., Ding, Y.: A Scalable Method of E-Service Workflow Emergence Based on the Bio-Network. In: Fourth International Conference on Natural Computation (ICNC 2008), October 18-20, pp. 165–169 (2008)Google Scholar
  126. 126.
    Liu, L., Zhang, X., Xie, L., Du, J.: A Novel Multi-Objective Particle Swarm Optimization based on Dynamic Crowding Distance. In: IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009), November 20-22, pp. 481–485 (2009)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Production EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations