Abstract
Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Baluja S (1994) Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University, USA
Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp 1875–1882
Branke J, Kaubler T, Schmidt C, Schmeck H (2000) A multi-population approach to dynamic optimization problems. Adaptive Comput Des Manufact, pp 299–308
Cobb HG (1990) An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environment. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA
Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2): 124–141
Eriksson R, Olsson B (2002) On the behaviour of evolutionary global-local hybrids with dynamic fitness functions. Parrallel Problem Solving From Nature VII, pp 13–22
Eriksson R, Olsson B (2004) On the performance of evolutionary algorithms with life-time adaptation in dynamic fitness landscapes. In: Proceedings of the 2004 congress on evolutionary computation, pp 1293–1300
Gallardo JE, Cotta C, Ferndez AJ (2007) On the hybridization of memetic algorithms with branch-and-bound techniques. IEEE Trans Syst Man Cybern B Cybern 37(1): 77–83
Goh CK, Tan KC (2008) A competitive-cooperation coevolutionary paradigm for dynamic multi-objective optimization. IEEE Trans on Evol Comput
Goldberg DE, Smith RE (1987) Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Proceedings of the 2nd international conference on genetic algorithms, pp 59–68
Grefenstette JJ (1992) Genetic algorithms for changing environments. Parallel Problem Solving From Nature II, pp 137–144
Hatzakis I, Wallace D (2006) Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the 2006 Genetic and Evol Comput Conference, pp 1201–1208
Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evol Comput 7(2): 204–223
Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3): 303–317
Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9(5): 474–487
Lau TL, Tsang EPK (1996) Applying a mutation-based genetic algorithm to processor configuration problems. In: Proceedings of the 8th IEEE conference on tools with artificial intelligence, pp 17–24
Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12(3): 273–302
Liu D, Tan KC, Goh CK, Ho WK (2007) A multiobjective memetic algorithm based on particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern 37(1): 42–50
Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern B Cybern 37(1): 18–27
Man S, Liang Y, Leung KS, Lee KH, Mok TSK (2007) A memetic algorithm for multiple-drug cancer chemotherapy schedule optimization. IEEE Trans Syst Man Cybern B Cybern 37(1): 84–91
Neri F, Toivanen J, Cascella GL, Ong Y-S (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE-ACM Trans Comput Biol Bioinform 4(2): 264–278
Neri F, Toivanen J, Makinen RAE (2007) An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV. Appl Intell
Ong Y-S, Keane AJ (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2): 99–110
Oppacher F, Wineberg M (1999) The shifting balance genetic algorithm: improving the GA in a dynamic environment. Proc Genet Evol Comput Conf 1: 504–510
O’Reilly UM, Oppacher F (1994) Program search with a hierarchical variable length representation: genetic programming, simulated annealing and hill climbing. Parallel Problem Solving from Nature III, pp 397–406
O’Reilly UM, Oppacher F (1995) Hybridized crossover-based search techniques for program discovery. In: Proceedings of the 1995 IEEE international conference on evolutionary computation, pp 573–578
Parrott D, Li X (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4): 440–458
Smith JE (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1): 6–17
Talbi EG, Bachelet V (2006) Cosearch: a parallel cooperative metaheuristic. J Math Modell Algorithms 5: 5–22
Tang J, Lim MH, Ong Y-S (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput 11(10): 957–971
Tang M, Yao X (2007) A memetic algorithm for VLSI floorplanning. IEEE Trans Syst Man Cybern B Cybern 37(1): 62–69
Uyar AS, Harmanci AE (2005) A new population based adaptive dominance change mechanism for diploid genetic algorithms in dynamic environments. Soft Comput 9(11): 803–815
Vavak F, Fogarty TC, Jukes K (1996) Adaptive combustion balancing in multiple burner boilers using a genetic algorithm with variable range of local search. In: Proceedings of the 7th international conference on genetic algorithms, pp 719–726
Wang H, Wang D (2006) An improved primal-dual genetic algorithm for optimization in dynamic environments. In: Proceedings of the 13th international conference on neural information processing, Part III, pp 836–844
Wang H, Wang D, Yang S (2007) Triggered memory-based swarm optimization in dynamic environments. Applications of Evolutionary Computing, LNCS 4448, pp 637–646
William EH, Krasnogor N, Smith JE (eds) (2005) Recent advances in memetic algorithms. Springer, Berlin
Yang S (2003) Non-stationary problem optimization using the primal-dual genetic algorithm. Proc 2003 Cong Evol Comput 3: 2246–2253
Yang S (2006) Associative memory scheme for genetic algorithms in dynamic environments. Applications of Evolutionary Computing, LNCS 3907, pp 788–799
Yang S (2007) Genetic algorithms with elitism-based immigrants for changing optimization problems. Applications of Evolutionary Computing, LNCS 4448, pp 627–636
Yang S (2008) Genetic algorithms with memory and elitism based immigrants in dynamic environments. Evol Comput 16(3)
Yang S, Ong Y-S, Jin Y(eds) (2007) Evolutionary computation in dynamic and uncertain environments. Springer, Berlin
Yang S, Yao X (2005) Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput 9(11): 815–834
Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput (to appear)
Zhou Z, Ong Y-S, Lim MH (2007) Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Comput 11(9): 873–888
Zhu Z, Ong Y-S, Dash M (2007) Wrapper-Filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybern B Cybern 37(1): 70–76
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, H., Wang, D. & Yang, S. A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput 13, 763–780 (2009). https://doi.org/10.1007/s00500-008-0347-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-008-0347-3