Abstract
Biological systems have often been used as the inspiration for search techniques to solve continuous and discrete combinatorial optimisation problems. One of the key aspects of biological systems is their ability to adapt to changing environmental conditions. Yet, biologically inspired optimisation techniques are mostly used to solve static problems (problems that do not change while they are being solved) rather than their dynamic counterparts. This is mainly due to the fact that the incorporation of temporal search control is a challenging task. Recently, however, a greater body of work has been completed on enhanced versions of these biologically inspired meta-heuristics, particularly genetic algorithms, ant colony optimisation, particle swarm optimisation and extremal optimisation, so as to allow them to solve dynamic optimisation problems. This survey chapter examines representative works and methodologies of these techniques on this important class of problems.
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References
Angeline, P.: Tracking extrema in dynamic environments. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 335–345. Springer, Heidelberg (1997)
Angus, D., Hendtlass, T.: Ant Colony Optimisation Applied to a Dynamically Changing Problem. In: Hendtlass, T., Ali, M. (eds.) IEA/AIE 2002. LNCS (LNAI), vol. 2358, pp. 618–627. Springer, Heidelberg (2002)
Aydin, M., Öztemel, E.: Dynamic job shop scheduling using reinforcement learning agents. Robotics and Autonomous Systems 33, 169–178 (2000)
Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: An explanation of 1/f noise. Physical Review Letters 59, 381–384 (1987)
Beasley, J.: OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society 41(11), 1069–1072 (1990)
Beasley, J., Krishnamoorthy, M., Sharaiha, Y., Abramson, D.: Scheduling aircraft landings - the static case. Transportation Science 34, 180–197 (2000)
Beasley, J., Krishnamoorthy, M., Sharaiha, Y., Abramson, D.: The displacement problem and dynamically scheduling aircraft landings. Journal of the Operational Research Society 55, 54–64 (2004)
Bendtsen, C., Krink, T.: Dynamic memory model for non-stationary optimisation. In: Proceedings of the Congress on Evolutionary Computation, pp. 992–997 (2002)
Bird, S., Li, X.: Adaptively choosing niching parameters in a PSO. In: Proceedings of the 8th Conference on Genetic and Evolutionary Computation, pp. 3–10 (2006)
Blackwell, T., Bentley, P.: Dynamic search with charged swarms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 19–26 (2002)
Blackwell, T., Branke, J.: Multi-swarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10, 459–472 (2006)
Boettcher, S., Percus, A.: Extremal optimization: Methods derived from co-evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 825–832. Morgan Kaufmann, San Francisco (1999)
Boettcher, S., Percus, A.: Nature’s way of optimizing. Artificial Intelligence 119, 275–286 (2000)
Boettcher, S., Percus, A.: Optimization with extremal dynamics. Physical Review Letters 86, 5211–5214 (2001)
Bosman, P., La Poutré, H.: Inventory management and the impact of anticipation in evolutionary stochastic online dynamic optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 268–275. IEEE Computer Society Press, Los Alamitos (2007)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the Congress on Evolutionary Computation, pp. 6–9. IEEE Computer Society Press, Los Alamitos (1999)
Branke, J.: The moving peaks benchmark (1999), http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell (2001)
Brits, R., Englebrecht, A., van der Bergh, F.: A niching particle swarm optimiser. In: Proceedings of the Asia Pacific Conference on Simulated Evolution and Learning, pp. 692–696 (2002)
Carlisle, A., Dozier, G.: Adapting particle swarm optimization to dynamic environments. In: Proceedings of the International Conference on Artificial Intelligence, pp. 429–434 (2000)
Carlisle, A., Dozier, G.: Tracking changing extrema with adaptive particle swarm optimizer. In: Proceedings of the World Automation Congress, pp. 265–270 (2002)
Chaudhry, S., Luo, W.: Application of genetic algorithms in production and operations management: A review. International Journal of Production Research 43(19), 4083–4101 (2005)
Cicirello, V., Smith, S.: Ant colony control for autonomous decentralized shop floor routing. In: Proceedings of the 5th International Symposium on Autonomous Decentralized Systems, pp. 383–390. IEEE Computer Society Press, Los Alamitos (2001)
Cobb, H.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA (1990)
Cobb, H., Grefenstette, J.: Genetic algorithms for tracking changing environments. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 523–530. Morgan Kaufmann, San Francisco (1993)
De Jong, K.: An analysis of the behaviour of a class of genetic adaptive systems. PhD dissertation, University of Michigan (1975)
Di Caro, G., Dorigo, M.: AntNet: A mobile agents approach to adaptive routing. Tech. Rep. IRIDIA/97-12, Université Libre de Bruxelles, Belgium (1997)
Di Caro, G., Dorigo, M.: An adaptive multi-agent routing algorithm inspired by ants behavior. In: Proceedings of 5th Annual Australasian Conference on Parallel Real Time Systems, pp. 261–272 (1998)
Di Caro, G., Dorigo, M.: Ant colonies for adaptive routing in packet-switched communications networks. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 673–682. Springer, Heidelberg (1998)
Di Caro, G., Dorigo, M.: AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research 9, 317–365 (1998)
Di Caro, G., Dorigo, M.: Mobile agents for adaptive routing. In: Proceedings of the 31st Annual Hawaii International Conference on System Sciences, pp. 74–83. IEEE Computer Society, Los Alamitos (1998)
Di Caro, G., Dorigo, M.: Two ant colony algorithms for best-effort routing in datagram networks. In: Proceedings of the 10th IASTED International Conference on Parallel and Distributed Computing and Systems, pp. 541–546. IASTED/ACTA Press (1998)
Di Caro, G., Ducatalle, F., Gambardella, L.: AntHocNet: An ant-based hybrid routing algorithm for mobile ad hoc networks. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 461–470. Springer, Heidelberg (2004)
Di Caro, G., Ducatalle, F., Gambardella, L.: AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transaction on Telecommunications - Special Issue on Self-organisation in Mobile Networking 16, 443–455 (2005)
Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)
Dreo, J., Siarry, P.: An ant colony algorithm aimed at dynamic continuous optimization. Applied Mathematics and Computation 181, 457–467 (2006)
Dror, M., Powell, W.: Stochastic and dynamic models in transportation. Operations Research 41, 11–14 (1993)
Ducatelle, F., Di Caro, G., Gambardella, L.: Ant agents for hybrid multipath routing in mobile ad hoc networks. In: Proceedings of Wireless On-demand Network Systems and Services, pp. 44–53 (2005)
Eberhart, R., Kennedy, J.: A new optimizer using particles swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)
Eyckelhof, C., Snoek, M.: Ant systems for a dynamic TSP: Ants caught in a traffic jam. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 88–99. Springer, Heidelberg (2002)
Gauthier, S.: Solving the dynamic aircraft landing problem using ant colony optimisation. Masters Thesis, School of Information Technology, Bond University (2006)
Godwin, T., Gopalan, R., Narendran, T.: Locomotive assignment and freight train scheduling using genetic algorithms. International Transactions in Operational Research 13(4), 299–332 (2006)
Goldberg, D., Smith, R.: Nonstationary function optimization using genetic algorithm with dominance and diploidy. In: Proceedings of the 2nd International Conference on Genetic Algorithms on Genetic algorithms and their application, pp. 59–68. Lawrence Erlbaum Associates, Inc., Mahwah (1987)
Grefenstette, J.: Evolvability in dynamic fitness landscapes: A genetic algorithm approach. In: Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 2031–2038. IEEE Press, Los Alamitos (1999)
Guntsch, M., Middendorf, M.: Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)
Guntsch, M., Middendorf, M.: Applying population based ACO to dynamic optimization problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 111–122. Springer, Heidelberg (2002)
Guntsch, M., Middendorf, M.: A population based approach for ACO. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 72–81. Springer, Heidelberg (2002)
Guntsch, M., Middendorf, M., Schmeck, H.: An ant colony optimization approach to dynamic TSP. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 860–867. Morgan Kaufmann, San Francisco (2001)
Gutjahr, W., Rauner, M.: An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Computers and Operations Research 34, 642–666 (2007)
Hadad, B., Eick, C.: Supporting polyploidy in genetic algorithms using dominance vectors. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 223–234. Springer, Heidelberg (1997)
Hendtlass, T.: WoSP: A multi-optima particle swarm algorithm. In: Proceedings of the Congress of Evolutionary Computing, pp. 727–734. IEEE Press, Los Alamitos (2005)
Heusse, M., Snyers, D., Guérin, S., Knutz, P.: Adaptive agent-driven routing and load balancing in communication networks. Adaptive Complex Systems 2, 1–15 (1998)
Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Hu, X., Eberhart, R.: Adaptive particle swarm optimisation: Detection and response to dynamic systems. In: Proceedings of the Congress on Evolutionary Computing, pp. 1666–1670. IEEE Press, Los Alamitos (2002)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer. In: Proceedings of the Congress on Evolutionary Computing, pp. 1666–1670. IEEE Press, Los Alamitos (2003)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer for dynamic optimization problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513–524. Springer, Heidelberg (2004)
Karaman, A., Uyar, S., Eryigit, G.: The memory indexing evolutionary algorithm for dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 563–573. Springer, Heidelberg (2005)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE Conference on Neural Networks, pp. 1942–1947 (1995)
Kókai, G., Christ, T., Frühauf, H.: Using hardware-based particle swarm method for dynamic optimization of adaptive array antennas. In: Proceedings of Adaptive Hardware and Systems, pp. 51–58 (2006)
Liles, W., De Jong, K.: The usefulness of tag bits in changing environments. In: Proceedings of the Congress on Evolutinary Computation, vol 3, pp. 2054–2060 (1999)
Louis, S., Johnson, J.: Solving similar problems using genetic algorithms and case-based memory. In: Bäck, T. (ed.) Proceedings of the 7th International Conference on Genetic Algorithms, pp. 283–290 (1997)
McAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 321–326 (1997)
Menai, M.: An Evolutionary Local Search Method for Incremental Satisfiability. In: Buchberger, B., Campbell, J. (eds.) AISC 2004. LNCS, vol. 3249, pp. 143–156. Springer, Heidelberg (2004)
Meng, Y., Kazeem, Q., Muller, J.: A hybrid ACO/PCO control algorithm for distributed swarm robots. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 273–280 (2007)
Morrison, R., De Jong, K.: A test problem generator for non-stationary environments. In: Proceedings of the Congress on Evolutionary Computation, pp. 2047–2053 (1999)
Moser, I.: Applying extremal optimisation to dynamic optimisation problems. PhD in information technology, Swinburne University of Technology. Faculty of Information and Communication Technologies (2008)
Moser, I., Hendtlass, T.: Solving dynamic single-runway aircraft landing problems with extremal optimisation. In: Proceedings of the IEEE Symposium on Computational Intelligence in Scheduling, pp. 206–211 (2007)
Mullen, P., Monson, C., Seppi, K.: Particle swarm optimization in dynamic pricing. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1232–1239 (2006)
Ng, K., Wong, K.: A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 159–166. Morgan Kaufmann, San Francisco (1995)
Parsopoulos, K., Vrahatis, M.: UPSO: A unified particle swarm optimization scheme. In: Proceedings of the International Conference on Computational Methods in Sciences and Engineering, pp. 868–873 (2004)
Parsopoulos, K., Vrahatis, M.: Unified particle swarm optimization in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 590–599. Springer, Heidelberg (2005)
Pekala, M., Schuster, E.: Dynamic optimization of a heterogeneous swarm of robots. In: Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control, pp. 354–359 (2007)
Perkins, C., Royer, E.: Ad-hoc on-demand distance vector routing. In: Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, pp. 90–100 (1999)
Ramsey, C., Grefenstette, J.: Case-based initialization of genetic algorithms. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, pp. 84–91 (1993)
Randall, M.: A dynamic optimisation approach for ant colony optimisation using the multidimensional knapsack problem. In: Recent Advances in Artificial Life, Advances in Natural Computation, vol. 3, pp. 215–226. World Scientific, Singapore (2005)
Saleh, M., Ghani, A.: Adaptive routing in packet-switched networks using agents updating methods. Malaysian Journal of Computer Science 16, 1–10 (2003)
Schoonderwoerd, R., Holland, O., Bruten, J., Rothkrantz, L.: Ant-based load balancing in telecommunications networks. Adaptive Behavior 2, 169–207 (1996)
Simões, A., Costa, E.: An immune-system-based genetic algorithm to deal with dyamic environments: Diversity and memory. In: Proceedings of the 6th International Conference on Artificial Neural Networks, pp. 168–174 (2003)
Simões, A., Costa, E.: Improving memory-based evolutionary algorithms in changing environments. Technical Report TR2007/004, CISUC (2007)
Simões, A., Costa, E.: Improving memory’s usage in evolutionary algorithms for changing environments. In: Proceedings of the Congress on Evolutionary Computation, pp. 276–283. IEEE Press, Los Alamitos (2007)
Simões, A., Costa, E.: Variable-size memory evolutionary algorithm to deal with dynamic environments. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 617–626. Springer, Heidelberg (2007)
Simões, A., Costa, E.: VMEA: Studies of the impact of different replacing strategies in the algorithm’s performance and in the population’s diversity when dealing with dynamic environments. Technical Report TR2007/001, CISUC (2007)
Subramanian, D., Druschel, P., Chen, J.: Ants and reinforcement learning: A case study in routing in dynamic networks. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence, pp. 832–838 (1997)
Svenson, P.: Extremal optimization for sensor report pre-processing. In: Proceedings of Signal Processing, Sensor Fusion, and Target Recognition XIII, pp. 162–171 (2004)
Tinós, R., Yang, S.: Genetic algorithms with self-organized criticality for dynamic optimisation problems. The IEEE Congress on Evolutionary Computation 3, 2816–2823 (2005)
Tinós, R., Yang, S.: A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines 8(3), 255–286 (2007)
Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 1843–1850. IEEE Press, Los Alamitos (1999)
Ursem, R.: Multinational GAs: Multimodal optimization techniques in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 19–26 (2000)
Varela, G., Sinclair, M.: Ant colony optimisation for virtual-wavelength-path routing and wavelength allocation. In: Proceedings of the Congress on Evolutionary Computation (1999)
White, T., Pagurek, B., Oppacher, F.: Connection management by ants: An application of mobile agents in network management. In: Proceedings of Combinatorial Optimization (1998)
Xia, Y., Chen, J., Meng, X.: On the dynamic ant colony algorithm optimization based on multi-pheromones. In: Proceedings of the 7th IEEE/ACIS International Conference on Computer and Information Science, pp. 630–635 (2008)
Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1115–1122. ACM, New York (2005)
Yang, S.: Associative memory scheme for genetic algorithms in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 788–799. Springer, Heidelberg (2006)
Yang, S.: A comparative study of immune system based genetic algorithms in dynamic environments. In: Proceedings of the 8th Conference on Genetic and Evolutionary Computation, pp. 1377–1384. ACM, New York (2006)
Yang, S.: Genetic algorithms with elitism-based immigrants for changing optimization problems. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 627–636. Springer, Heidelberg (2007)
Yang, S., Tinós, R.: A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing 4, 243–254 (2007)
Zhang, X., Li, X., Li, Y., Zhou, Y., Zhang, J., Zhang, N., Wu, B., Yuan, T., Chen, L., Zhang, H., Yao, M., Yang, B.: Two-stage adaptive PMD compensation in 40 Gb/s OTDM optical communication system using PSO algorithm. Optical and Quantum Electronics 36, 1089–1104 (2004)
Zhang, Y., Kuhn, L., Fromherz, M.: Improvements on ant routing for sensor networks. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 154–165. Springer, Heidelberg (2004)
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Hendtlass, T., Moser, I., Randall, M. (2009). Dynamic Problems and Nature Inspired Meta-heuristics. In: Lewis, A., Mostaghim, S., Randall, M. (eds) Biologically-Inspired Optimisation Methods. Studies in Computational Intelligence, vol 210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01262-4_4
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