Abstract
Memetic algorithms (MA) have recently been applied successfully to solve decision and optimization problems. However, selecting a suitable local search technique remains a critical issue of MA, as this significantly affects the performance of the algorithms. This paper presents a new agent based memetic algorithm (AMA) for solving constrained real-valued optimization problems, where the agents have the ability to independently select a suitable local search technique (LST) from our designed set. Each agent represents a candidate solution of the optimization problem and tries to improve its solution through co-operation with other agents. Evolutionary operators consist of only crossover and one of the self-adaptively selected LSTs. The performance of the proposed algorithm is tested on five new benchmark problems along with 13 existing well-known problems, and the experimental results show convincing performance.
Similar content being viewed by others
References
Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9: 126–142
Alkan A, Ozcan E (2003) Memetic algorithms for timetabling. In: The 2003 congress on evolutionary computation, vol 3, pp 1796–1802
Anderson DR, Sweeney DJ, Williams TA, Harrison NJ, Rickard JA (1996) Essentials of statistics for business and economics. Harper Educational (Australia) Pty. Limited, Publisher West Group
Barkat Ullah ASSM, Sarker R, Cornforth D (2007a) An evolutionary agent system for mathematical programming. In: Advances in computation and intelligence. Springer, Heidelberg, pp 187–196
Barkat Ullah ASSM, Sarker R, Cornforth D, Lokan C (2007b) An agent-based memetic algorithm (AMA) for solving constrained optimization problems. In: IEEE congress on evolutionary computation, 2007. CEC 2007, pp 999–1006
Barkat Ullah ASSM, Sarker R, Cornforth D (2007c) A combined MA-GA approach for solving constrained optimization problems. In: 6th IEEE/ACIS international conference on computer and information science, 2007. ICIS 2007, pp 382–387
Burke EK, Smith AJ (1999) A memetic algorithm to schedule planned maintenance for the national grid. J Exp Algorithmics 4: 1–13
Cheng R, Gen M (1996) Parallel machine scheduling problems using memetic algorithms. IEEE Int Conf Syst Man Cybern 4: 2665–2670
Chootinan P, Chen A (2006) Constraint handling in genetic algorithms using a gradient-based repair method. Comput Oper Res 33: 2263–2281
Coello CAC (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civil Eng Environ Syst 17: 319–346
Coello CAC (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
Conover WJ (1980) Practical nonparametric statistics. Wiley, New York
Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York
Dawkins R (1976) The selfish gene. Oxford University Press, New York
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186: 311
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York
Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9: 115–148
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6: 182
Dobrowolski G, Kisiel-Dorohinicki M, Nawarecki E (2001) Evolutionary multiagent system in multiobjective optimisation. In: Proceedings of the IASTED international symposium: applied informatics. IASTED/ACTA Press
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
Farmani R, Wright JA (2003) Self-adaptive fitness formulation for constrained optimization. IEEE Trans Evol Comput 7: 445
Ferber J (1999) Multiagent systems as introduction to distributed artificial intelligence. Addison-Wesley, Reading
Floudas C (1999) Handbook of test problems in local and global optimization. Nonconvex optimization and its applications. Kluwer, The Netherlands
Floudas CA, Pardalos PM (1990) A collection of test problems for constrained global optimization algorithms. In: Lecture notes in computer science, vol 455. Springer, Berlin
Folino G, Pizzuti C, Spezzano G (2001) Parallel hybrid method for SAT that couples genetic algorithms and local search. IEEE Trans Evol Comput 5: 323–334
Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
Goldberg DE, Voessner S (1999) Optimizing global-local search hybrids. In: Proceedings of the genetic and evolutionary computation conference, pp 220–228
Guimaraes FG, Wanner EF, Campelo F, Takahashi RHC, Igarashi H, Lowther DA, Ramirez JA (2006) Local learning and search in memetic algorithms. In: IEEE congress on evolutionary computation, 2006. CEC 2006, pp 2936–2943
Gupta H, Deb K (2005) Handling constraints in robust multi-objective optimization. The 2005 IEEE congress on evolutionary computation, 2005, vol 1, p 25
Hart WE (1994) Adaptive global optimization with local search. Ph.D thesis. University of California, San Diego, CA
Himmelblau DM (1972) Applied nonlinear programming. McGraw-Hill, USA
Hock W, Schittkowski K (1981) Test examples for nonlinear programming codes. In: Lecture notes in economics and mathematical systems. Springer, Heidelberg
Houck CR, Joines JA, Kay MG (1996) Utilizing Lamarckian evolution and the Baldwin effect in hybrid genetic algorithms. Meta-heuristic research and applications group. NCSU-IE Technical Report 96-01. Department of Industrial Engineering, North Carolina State University
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
Ishibuchi H, Kaige S, Narukawa K (2005) Comparison between Lamarckian and Baldwinian repair on multiobjective 0/1 Knapsack problems. Evol Multicriterion Optim 3410: 370–385
Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA’s. Proc First IEEE Conf Evol Comput 2: 579–584
Knowles JD, Corne DW (2000) M-PAES: a memetic algorithm for multiobjective optimization. In: Proceedings of the 2000 congress on evolutionary computation, vol 1, pp 325–332
Knowles J, Corne D (2001) A comparative assessment of memetic, evolutionary and constructive algorithms for the multi-objective d-msat problem. GECCO-2001 workshop program, pp 162–167
Knowles J, Corne D (2005) Memetic algorithms for multiobjective optimization: issues, methods and prospects. In: Recent advances in memetic algorithms, pp 313–352
Koziel S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol Comput 7: 19–44
Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. Ph.D thesis, University of the West of England
Krasnogor N, Smith J (2001) Emergence of profitable search strategies based on a simple inheritance mechanism. In: Proceedings of the genetic and evolutionary computation conference
Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9: 474–488
Leung Y-W (2001) An orthogonal genetic algorithm with quantization for global numerical optimization optimization. IEEE Trans Evol Comput 5: 41–53
Liang JJ, Suganthan PN (2006) Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. In: IEEE congress on evolutionary computation, 2006. CEC 2006, pp 9–16
Liu J, Jing H, Tang YY (2002) Multi-agent oriented constraint satisfaction. Artif Intell 136: 101–144
Liu J, Zhong W, Jiao L (2006) A multiagent evolutionary algorithm for constraint satisfaction problems. IEEE Trans Syst Man Cybern B 36: 54–73
Merz P, Freisleben B (1997) Genetic local search for the TSP: new results. In: IEEE international conference on evolutionary computation, 1997, pp 159–164
Merz P, Freisleben B (1999) A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem. In: Proceedings of the congress on evolutionary computation, vol 3, p 2070
Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput 4: 337–352
Michalewicz Z (1994) Genetic algorithms + data structures = evolution programs. Springer, Heidelberg
Michalewicz Z (1995) Genetic algorithms, numerical optimization and constraints. In: Proceedings of the 6th international conference on genetic algorithms, pp 151–158
Michalewicz Z, Janikow CZ (1996) GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints. Commun ACM 39, Article No. 175
Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4: 1–32
Michalewicz Z, Nazhiyath G, Michalewicz M (1996) A note on usefulness of geometrical crossover for numerical optimization problems. In: 5th Annual conference on evolutionary programming. MIT Press, Cambridge/San Diego, pp 305–312
Molina D, Herrera F, Lozano M (2005) Adaptive local search parameters for real-coded memetic algorithms. In: The 2005 IEEE congress on evolutionary computation, vol 1, pp 888–895
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts towards memetic algorithms. Caltech concurrent computation program report 826. California Institute of Technology, Pasadena, CA, USA
Muruganandam A, Prabhaharan G, Asokan P, Baskaran V (2005) A memetic algorithm approach to the cell formation problem. Int J Adv Manuf Technol 25: 988–997
Nakashima T, Ariyama T, Yoshida T, Ishibuchi H (2003) Performance evaluation of combined cellular genetic algorithms for function optimization problems. In: Proceedings of the 2003 IEEE international symposium on computational intelligence in Robotics and Automation, 2003. vol 1, pp 295–299
Ong YS, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8: 99–110
Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36: 141–152
Paredis J (1994) Co-evolutionary constraint satisfaction. In: Proceedings of the 3rd conference on parallel problem solving from nature. Springer, New York, pp 46–55
Powell D, Skolnick MM (1993) Using genetic algorithms in engineering design optimization with non-linear constraints. In: Proceedings of the 5th international conference on genetic algorithms. Morgan Kaufmann, San Francisco, pp 424–431
Ray T, Kang T, Chye SK (2000) An evolutionary algorithm for constrained optimization. In: Proceedings of the genetic and evolutionary computation conference (GECCO’2000), San Francisco, CA
Richardson J, Palmar M, Liepus G, Hillard M (1989) Some guidelines for genetic algorithms with penalty functions. In: Proceedings international conference on genetic algorithms, pp 191–197
Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4: 284
Sarker R (2001) A comparative study of different penalty function-based genetic algorithms for constrained optimization. Aust J Intell Inf Process Syst 7: 58–63
Sarker R, Runarsson T, Newton C (2001) Genetic algorithms for solving a class of constrained nonlinear integer programs. Int Trans Oper Res 8: 61–74
Sarker R, Kamruzzaman J, Newton C (2003) Evolutionary optimization (EvOpt): a brief review and analysis. Int J Comput Intell Appl 3: 311–330
Siwik L, Kisiel-Dorohinicki M (2006) Semi-elitist evolutionary multi-agent system for multiobjective optimization. In: Lecture notes in computer science: computational science, ICCS, pp 831–838
Smith JE (2003) Co-evolving memetic algorithms: a learning approach to robust scalable optimisation. In: The 2003 congress on evolutionary computation, 2003. CEC 2003, vol 1, pp 498–505
Spalanzani A (2000) Lamarckian vs Darwinian evolution for the adaptation to acoustical environment change. Artif Evol 1829: 136–144
Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2: 221–248
Srinivasan D, Rachmawati L (2006) An efficient multi-objective evolutionary algorithm with steady-state replacement model. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM Press, Seattle
Stan F, Art G (1997) Is it an agent, or just a program?: a taxonomy for autonomous agents. In: Intelligent agents III agent theories, architectures, and languages. Lecture notes in computer science, vol 3. Springer, Berlin, pp 21–35
Surry P, Radcliffe N, Boyd I (1995) A multi-objective approach to constrained optimization of gas supply networks: the COMOGA method. In: Proceedings of Evol. Comput. AISB workshop, pp 166–180
Sycara KP (1998) Multiagent systems. The American Association for Artificial Intelligence, Al Magazine 10(2): 79–93
Takahama T, Sakai S (2006) Constrained optimization by the e constrained differential evolution with gradient-based mutation and feasible elites. In: IEEE congress on evolutionary computation, CEC 2006, pp 1–8
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, USA
Tang J, Lim MH, Ong YS (2006) Adaptation for parallel memetic algorithm based on population entropy. Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM Press, Seattle, Washington, USA
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
Tasgetiren MF, Suganthan PN (2006) A multi-populated differential evolution algorithm for solving constrained optimization problem. In: IEEE congress on evolutionary computation, 2006. CEC 2006, pp 33–40
Torn A, Zilinskas A (1989) Global optimization, vol 350. Springer, New York
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/Heidelberg, pp 376–385
Venkatraman S, Yen GG (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9: 424–435
Whitley LD (1993) Cellular genetic algorithms. In: Proceedings of the 5th international conference on genetic algorithms. Morgan Kaufmann, San Francisco
Whitley D, Gordon V, Mathias K (1994) Lamarckian evolution, the Baldwin effect and function optimization. In: Lecture notes in computer science: parallel problem solving from nature—PPSN III. Springer, Berlin/Heidelberg, pp 5–15
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Barkat Ullah, A.S.S.M., Sarker, R., Cornforth, D. et al. AMA: a new approach for solving constrained real-valued optimization problems. Soft Comput 13, 741–762 (2009). https://doi.org/10.1007/s00500-008-0349-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-008-0349-1