Handling equality constraints with agent-based memetic algorithms

Memetic Computing

Volumn 3, Issue 1, 2011, Pages 51-72

  Barkat Ullah, Abu S S M ^a   Sarker, Ruhul ^a   Lokan, Chris ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Agent based evolutionary algorithms; Agent based memetic algorithm; Constrained optimization problems; Constraint handling technique; Memetic algorithms

Indexed keywords

AGENT BASED; AGENT-BASED EVOLUTIONARY ALGORITHMS; CONSTRAINED OPTIMIZATION PROBLEMS; CONSTRAINT HANDLING TECHNIQUE; MEMETIC ALGORITHMS;

CONSTRAINED OPTIMIZATION; MATHEMATICAL MODELS;

EVOLUTIONARY ALGORITHMS;

EID: 79952043324     PISSN: 18659284     EISSN: 18659292     Source Type: Journal    
DOI: 10.1007/s12293-010-0051-6     Document Type: Article

References (94)

1. Sarimveis H, Nikolakopoulos A (2005) A line up evolutionary algorithm for solving nonlinear constrained optimization problems. Comput Oper Res 32: 1499-1514.
2. Liang JJ, Suganthan PN (2006) Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. In: IEEE Congress on evolutionary computation, CEC 2006, pp 9-16.
3. Mezura-Montes, Ee (eds) (2009) Constraint-handling in evolutionary optimization. Springer, Berlin.
4. Ho PY, Shimizu K (2007) Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme. Inf Sci 177: 2985-3004.
5. Back T, Hammel U, Schwefel HP (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1: 3.
6. Marczyk A (2004) Genetic algorithms and evolutionary computation. TalkOrigins Archive.
7. Sarker R, Kamruzzaman J, Newton C (2003) Evolutionary optimization (EvOpt): a brief review and analysis. Int J Computat Intell Appl 3: 311-330.
8. Chootinan P, Chen A (2006) Constraint handling in genetic algorithms using a gradient-based repair method. Comput Oper Res 33: 2263-2281.
9. Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4: 1-32.
10. Venkatraman S, Yen GG (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9: 424-435.
11. Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186: 311-338.
12. Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191: 1245-1287.
13. Mezura-Montes E, Coello CAC (2002) A numerical comparison of some multiobjective-based techniques to handle constraints in genetic algorithms. Technical report EVOCINV-03-2002. Evolutionary Computation Group at CINVESTAV-IPN, Mexico.
14. Barkat Ullah ASSMB, Sarker R, Lokan C (2009) An agent-based memetic algorithm (AMA) for nonlinear optimization with equality constraints. In: The 2009 IEEE Congress on evolutionary computation (CEC 2009), Norway.
15. Takahama T, Sakai S (2009) Solving difficult constrained optimization problems by the e{open} constrained differential evolution with gradient-based mutation. In: Mezura-Montes E (eds) Constraint-handling in evolutionary optimization. Springer, Berlin, pp 51-72.
16. Merz P, Freisleben B (1997) Genetic local search for the TSP: new results. In: Evolutionary Computation, 1997. IEEE international conference, pp 159-164.
17. Cheng R, Gen M (1996) Parallel machine scheduling problems using memetic algorithms. IEEE Int Conf Syst Man Cybern 4: 2665-2670.
18. Burke EK, Smith AJ (1999) A memetic algorithm to schedule planned maintenance for the national grid. J Exp Algorithmics 4(1): 1-13.
19. Tang J, Lim MH, Ong YS, Er MJ (2005) Solving large scale combinatorial optimization using PMA-SLS. In: Proceedings of the 2005 conference on genetic and evolutionary computation. ACM Press, Washington DC, pp 621-628.
20. Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput 4: 337-352.
21. Alkan A, Ozcan E (2003) Memetic algorithms for timetabling. In: The 2003 Congress on evolutionary computation, vol 3, pp 1796-1802.
22. Vavak F, Fogarty T, Jukes K (1996) A genetic algorithm with variable range of local search for tracking changing environments. Parallel problem solving from Nature-PPSN IV. Springer, Berlin, pp 376-385.
23. Handoko S, Kwoh CK, Ong YS (2009) Feasibility structure modeling: An effective chaperon for constrained memetic algorithms. IEEE Trans Evol Comput (in press).
24. Handoko SD, Kwoh CK, Ong YS (2009) Classification-assisted memetic algorithms for equality-constrained optimization problems. AI 2009: advances in artificial intelligence, vol 5866. Springer, Berlin, pp 391-400.
25. Knowles J, Corne DW (2001) A comparative assessment of memetic, evolutionary and constructive algorithms for the multi-objective d-msat problem. In: GECCO-2001 workshop program, pp 162-167.
26. Knowles J, Corne DW (2000) M-PAES: a memetic algorithm for multiobjective optimization. In: Proceedings of the 2000 Congress on evolutionary computation, vol 1, California, pp 325-332.
27. Hu X, Huang Z, Wang Z (2003) Hybridization of the multi-objective evolutionary algorithms and the gradient-based algorithms. In: The 2003 Congress on evolutionary computation, vol 2, pp 870-877.
28. Knowles J, Corne DW (2005) Memetic algorithms for multiobjective optimization: Issues, methods and prospects. In: Recent advances in memetic algorithms, pp 313-352.
29. Tang J, Lim M, Ong Y (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput Fusion Found Methodol Appl 11: 873-888.
30. Dawkins R (1976) The selfish gene. Oxford University Press, New York.
31. Hart WE (1994) Adaptive global optimization with local search. PhD thesis, University of California, San Diego.
32. Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. PhD thesis, University of the West of England.
33. Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9: 474-488.
34. Ong YS, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8: 99-110.
35. Merz P, Freisleben B (1999) A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem. In: Proceedings of the 1999 Congress on evolutionary computation, CEC 99, vol 3, pp 2063-2070.
36. Goldberg DE, Voessner S (1999) Optimizing global-local search hybrids. In: Proceedings of the genetic and evolutionary computation conference, pp 220-228.
37. Tang J, Lim MH, Ong YS (2006) Adaptation for parallel memetic algorithm based on population entropy. In: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM Press, Seattle.
38. Davis L (1991) Handbook of genetic algorithms. van Nostrand Reinhold, New York.
39. Ferber J (1999) Multiagent systems as introduction to distributed artificial intelligence. Addision-Wesley, Boston.
40. Stan F, Art G (1997) Is it an agent, or just a program? A taxonomy for autonomous agents. Intelligent agents III agent theories, architectures, and languages. Springer, Berlin, pp 21-35.
41. Chira C, Gog A, Dumitrescu D (2008) Exploring population geometry and multi-agent systems: a new approach to developing evolutionary techniques. In: Proceedings of the 2008 GECCO conference companion on genetic and evolutionary computation. ACM, Atlanta, pp 1953-1960.
42. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9: 115-148.
43. Barkat Ullah ASSM, Sarker R, Cornforth D, Lokan C (2007) An agent-based memetic algorithm (AMA) for solving constrained optimization problems. In: IEEE Congress on evolutionary computation, CEC 2007, Singapore, pp 999-1006.
44. Barkat Ullah ASSM, Sarker R, Cornforth D, Lokan C (2008) AMA: a new approach for solving constrained real-valued optimization problems. Soft Comput Fusion Found Methodol Appl 13(8-9): 741-762. doi: 10. 1007/s00500-008-0349-1.
45. Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4: 284.
46. Himmelblau DM (1972) Applied nonlinear programming. McGraw-Hill, Boston.
47. Hillier FS, Lieberman GJ (2005) Introduction to operations research. McGraw-Hill, Boston.
48. Hock W, Schittkowski K (1981) Test examples for nonlinear programming codes. Springer-Verlag, New York.
49. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6: 182.
50. Ray T, Sarker R (2007) Genetic algorithm for solving a gas lift optimization problem. J Petroleum Sci Eng 59: 84-96.
51. Kisiel-Dorohinicki M (2002) Agent-oriented model of simulated evolution. In: SOFSEM 2002: theory and practice of informatics, pp 253-261.
52. Siwik L, Kisiel-Dorohinicki M (2006) Semi-elitist evolutionary multi-agent system for multiobjective optimization. Computational Science-ICCS 2006. Springer, Berlin, pp 831-838.
53. Zhong W, Liu J, Xue M, Jiao L (2004) A multiagent genetic algorithm for global numerical optimization. IEEE Trans Syst Man Cybern B 34: 1128-1141.
54. Byrski A, Schaefer R (2009) Formal model for agent-based asynchronous evolutionary computation. In: IEEE Congress on evolutionary computation (CEC) 2009, Norway, pp 78-85.
55. Dobrowolski G., Kisiel-Dorohinicki M, Nawarecki E (2001) Evolutionary multiagent system in multiobjective optimisation. In: Hamza M (ed) Proceedings of the IASTED international symposium: applied informatics. IASTED/ACTA Press.
56. Socha K, Kisiel-Dorohinicki M (2002) Agent-based evolutionary multiobjective optimisation. In: Proceedings of the 2002 Congress on evolutionary computation, vol 1, pp 109-114.
57. Dreżewski R, Kisiel-Dorohinicki M (2006) Maintaining diversity in agent-based evolutionary computation. In: Computational Science-ICCS 2006, pp 908-911.
58. Zhong W, Liu J, Jiao L (2005) Job-shop scheduling based on multiagent evolutionary algorithm. In: Lecture notes in computer science: advances in natural computation. Springer, Berlin, pp 925-933.
59. Yan C, Zeng-Zhi L, Zhi-Wen W (2004) Multi-agent based genetic algorithm for JSSP. In: Proceedings of 2004 international conference on machine learning and cybernetics, vol 1, pp 267-270.
60. Liu J, Zhong W, Jiao L (2006) A multiagent evolutionary algorithm for constraint satisfaction problems. IEEE Trans Syst Man Cybern B 36: 54-73.
61. Liu H, Frazer JH (2002) Supporting evolution in a multi-agent cooperative design environment. Adv Eng Softw 33: 319-328.
62. Choi SPM, Liu J, Chan S-P (2001) A genetic agent-based negotiation system. Comput Netw 37: 195-204.
63. Meng A, Ye L, Roy D, Padilla P (2007) Genetic algorithm based multi-agent system applied to test generation. Comput Educ 49: 1205-1223.
64. Lim MK, Zhang Z (2002) Iterative multi-agent bidding and co-ordination based on genetic algorithm. In: Proceeding of 3rd international symposium on multi-agent systems, large complex systems, and e-businesses, Erfurt, pp 682-689.
65. Pendharkar PC (2007) The theory and experiments of designing cooperative intelligent systems. Decis Support Syst 43: 1014-1030.
66. Smith RE, Kearney PJ, Merlat W (1999) Evolutionary adaptation in autonomous agent systems-a paradigm for the emerging enterprise. BT Technol J 17: 157-167.
67. Jeong IK, Lee JJ (1997) Evolving multi-agents using a self-organizing genetic algorithm. Appl Math Comput 88: 293.
68. Yang A, Abbass H, Sarker R (2006) Land combat scenario planning: a multiobjective approach. Simulated evolution and learning. Springer, Berlin, pp, pp 837-844.
69. Sahin CS, Urrea E, Uyar MU, Conner M, Hokelek I, Conner M, Bertoli G, Pizzo C (2008) Genetic algorithms for self-spreading nodes in MANETs. In: Proceedings of the 10th annual conference on Genetic and evolutionary computation. ACM, Atlanta, pp 1141-1142.
70. Sahin CS, Urrea E, Uyar MU, Conner M, Hokelek I, Bertoli G, Pizzo C (2008) Uniform distribution of mobile agents using genetic algorithms for military applications in MANETs. In: IEEE military communications conference, 2008. MILCOM 2008, pp 1-7.
71. Davidsson P, Persson J, Holmgren J (2007) On the integration of agent-based and mathematical optimization techniques. Agent and multi-agent systems: technologies and applications, pp 1-10.
72. Nakashima T, Ariyama T, Yoshida T, Ishibuchi H (2003) Performance evaluation of combined cellular genetic algorithms for function optimization problems. In: Proceedings of 2003 IEEE international symposium on computational intelligence in robotics and automation, vol 1, pp 295-299.
73. De Jong KA (2008) Evolving intelligent agents: A 50 year quest. Comput Intell Mag IEEE 3: 12-17.
74. Vasile M, Locatelli M (2008) A hybrid multiagent approach for global trajectory optimization. J Glob Optim 44: 461-479.
75. Thornton C, Boulay Bd (1999) Artificial intelligence: strategies, applications, and models through SEARCH. AMACOM, New York.
76. Bajo J, Corchado J (2006) Multiagent architecture for monitoring the North-Atlantic carbon dioxide exchange rate. In: Current topics in artificial intelligence, pp 321-330.
77. Hasan SMK, Sarker R, Essam D, Cornforth D (2008) Memetic algorithms for solving job-shop scheduling problems. Memetic Comp 1: 69-83.
78. Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9: 126-142.
79. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York.
80. Barkat Ullah ASSM, Sarker R, Cornforth D (2007) An evolutionary agent system for mathematical programming. In: Advances in computation and intelligence. Lecture notes in computer science. Springer, Berlin, pp 187-196.
81. Barkat Ullah ASSM, Sarker R, Cornforth D (2008) Search space reduction technique for constrained optimization with tiny feasible space. In: Proceedings of the 10th annual conference on genetic and evolutionary computation. ACM, Atlanta, pp 881-888.
82. Elfeky EZ, Sarker RA, Essam DL (2006) A simple ranking and selection for constrained evolutionary optimization. In: Lecture notes in computer science: simulated evolution and learning, pp 537-544.
83. Koziel S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol Comput 7: 19-44.
84. Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. In: Proceedings of the first IEEE conference on evolutionary computation, vol 2, pp 579-584.
85. Surry PD, Radcliffe NJ (1997) The COMOGA method: constrained optimization by multi-objective genetic algorithms. Control Cybern 26: 391-412.
86. Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st international conference on genetic algorithms. L. Erlbaum Associates Inc., pp 93-100.
87. Coello CAC (2000) Treating constraints as objectives for single-objective evolutionary optimization. Eng Optim 32: 275-308.
88. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms. Morgan Kauffman Publishers, San Mateo, California University of Illinois at Urbana-Champaign, pp 416-423.
89. Coello CAC (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civil Eng Environ Syst 17: 319-346.
90. Horn J, Nafploitis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE Press, pp 82-87.
91. Coello CAC, Mezura-Montes E (2002) Handling constraints in genetic algorithms using dominance-based tournaments. In: Parmee IC (ed) Proceeding of the fifth international conference on adaptive computing design and manufacture (ACDM 2002), vol 5. Springer-Verlag, Devon, pp 273-284.
92. Wanner EF, Guimaraes FG, Saldanha RR, Takahashi RHC, Fleming PJ (2005) Constraint quadratic approximation operator for treating equality constraints with genetic algorithms. In: The 2005 IEEE Congress on evolutionary computation, vol 3, pp 2255-2262.
93. Sarker R, Ray T (2005) Multiobjective evolutionary algorithms for solving constrained optimization problems. In: International conference on computational intelligence for modelling, control and automation (CIMCA2005). IEEE Press, Vienna, pp 197-202.
94. Michalewicz Z, Nazhiyath G, Michalewicz M (1996) A note on usefulness of geometrical crossover for numerical optimization problems. In: Proceeding of the 5th annual conference on evolutionary programming. MIT Press, Cambridge, pp 305-312.