Dynamic optimization problems (DOPs) have attracted considerable attention due to the wide range of problems they can be applied to. Lots of efforts have been expended in modeling dynamic situations, proposing algorithms, and analyzing the results (too often in a visual way). Numeric performance measurements and their statistical validation have been however barely used in the literature. Most of works in DOPs report only the best-of-generation fitness, due to its simplicity of computation. Although this measure indicates the best algorithm in terms of fitness, it does not provide any details about the actual strength and weakness of each algorithm. In this article, we conduct a comparative study among algorithms of different search modes via several performance measures to demonstrate their relative advantages. We discuss the role of using different performance measures in drawing balanced conclusions about algorithms for DOPs.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Alba E (2005) Parallel metaheuristics. Wiley, Inc., USA
Alba E, Sarasola B (2010) Abc, a new performance tool for algorithms solving dynamic optimization problems. In: IEEE Congress on evolutionary computation, pp 1–7
Alba E, Sarasola B (2010) Measuring fitness degradation in dynamic optimization problems. In: Applications of evolutionary computation, Lecture notes in computer science, vol 6024, Springer, Heidelberg, pp 572–581
Back T (1998) On the behavior of evolutionary algorithms in dynamic environments. In: The 1998 IEEE International Conference on evolutionary computation proceedings, pp 446–451
Bierwirth C, Mattfeld D (1999) Production scheduling and rescheduling with genetic algorithms. Evol Comput 7(1):1–17
Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: IEEE Proceedings of the 1999 Congress on evolutionary computation, vol 3, pp 1875–1882
Branke J, Mattfeld DC (2000) Anticipation in dynamic optimization: the scheduling case. In: Proceedings of the 6th International Conference on parallel problem solving from nature, pp 253–262
Cheng H, Yang S (2010) Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks. In: Proceedings of the 2010 international conference on applications of evolutionary computation, Springer, Verlag, pp 562–571
Cruz C, González J, Pelta D (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput 15(7):1427–1448
Goldberg DE, Smith RE (1987) Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Proceedings of the Second International Conference on genetic algorithms and their application, pp 59–68
Grefenstette J (1992) Genetic algorithms for changing environments. In: parallel problem solving from nature 2, Elsevier, USA, pp 137–144
Hadad BS, Eick CF (1997) Supporting polyploidy in genetic algorithms using dominance vectors. In: evolutionary programming VI, lecture notes in computer science, vol 1213, Springer, Berlin, pp 223–234
Li C, Yang M, Kang L (2006) A new approach to solving dynamic traveling salesman problems. In: Proceedings of the 6th International Conference on simulated evolution and learning, pp 236–243
Mavrovouniotis M, Yang S (2011) A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15:1405–1425
Morrison R, De Jong K (2000) Triggered hypermutation revisited. In: Proceedings of the 2000 Congress on evolutionary computation 2:1025–1032
Morrison RW (2003) Performance measurement in dynamic environments. In: A.M. Barry (ed.) Proceedings of the bird of a feather workshops, genetic and evolutionary computation conference, AAAI, Chigaco, pp 99–102
Morrison RW, De Jong KA (1999) A test problem generator for non-stationary environments. In: Proceedings of the 1999 Congress on evolutionary computation, pp 2047–2053
Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6(0):1–24
Pisinger D (1997) A minimal algorithm for the 0–1 knapsack problem. Oper Res 45(5):758–767
Pisinger D (1999) Core problems in knapsack algorithms. Oper Res 47:570–575
Rand W, Riolo R (2005) Measurements for understanding the behavior of the genetic algorithm in dynamic environments: a case study using the shaky ladder hyperplane-defined functions. In: GECCO Workshops, pp 32–38
Richter H (2009) Detecting change in dynamic fitness landscapes. In: Proceedings of the eleventh conference on Congress on evolutionary computation, IEEE Press, pp 1613–1620
Rohlfshagen P, Bullinaria J (2006) Alternative splicing in evolutionary computation: adaptation in dynamic environments. In: Proceedings of the 2006 IEEE Congress on evolutionary computation, pp 2277–2284
Rohlfshagen P, Yao X (2009) The dynamic knapsack problem revisited: a new benchmark problem for dynamic combinatorial optimisation. In: applications of evolutionary computing, lecture notes in computer science 5484:745–754
Simões A, Costa E (2008) Evolutionary algorithms for dynamic environments: prediction using linear regression and markov chains. In: parallel problem solving from NaturePPSN X, lecture notes in computer science, vol 5199, Springer Berlin, pp 306–315
Simões A, Costa E (2009) Improving prediction in evolutionary algorithms for dynamic environments. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, GECCO ’09, ACM, pp 875–882
Tinós R, Yang S (2007) A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Program Evolvable Mach 8:255–286
Weicker K (2002) Performance measures for dynamic environments. In: parallel problem solving from nature PPSN VII, Springer, Verlag, pp 64–73
Yang S (2003) Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceedings of the 2003 Congress on Evol Comput, pp 2246–2253
Yang S (2003) Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceedings of the 2003 Congress on Evolutionary Computation 3:2246–2253
Yang S (2008) Genetic algorithms with memory-and elitism-based immigrants in dynamic environments. Evol Comput 16:385–416
Yang S, Tinós R (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Automat Comput 4:243–254
Yang S, Yao X (2005) Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput 9:815–834
Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on evolutionary computation, 12(5):542–561
Younes A, Calamai P, Basir O (2005) Generalized benchmark generation for dynamic combinatorial problems. In: Proceedings of the 2005 workshops on genetic and evolutionary computation, GECCO ’05, pp 25–31
Yu X, Jin Y, Tang K, Yao X (2010) Robust optimization over time—a new perspective on dynamic optimization problems. In: IEEE Congress on evolutionary computation, pp 1–6
Authors acknowledge funds from the CICE of the Junta de Andalucia, under contract P07-TIC-03044 (DIRICOM http://diricom.lcc.uma.es) and Spanish Ministry of Sciences and Innovation (MICINN) and FEDER under contracts TIN2011-28194 (RoadMe http://roadme.lcc.uma.es) and TIN2008-06491-C04-01 (M* http://mstar.lcc.uma.es). Also, from the European COADVISE project number 230833.
Communicated by A-A. Tantar.
About this article
Cite this article
Ben-Romdhane, H., Alba, E. & Krichen, S. Best practices in measuring algorithm performance for dynamic optimization problems. Soft Comput 17, 1005–1017 (2013). https://doi.org/10.1007/s00500-013-0989-7
- Dynamic optimization problems
- Evolutionary algorithms
- Genetic algorithms
- Performance measure