A hyper-heuristic based framework for dynamic optimization problems

Abstract Most of the real world problems have dynamic characteristics, where one or more elements of the underlying model for a given problem including the objective, constraints or even environmental parameters may change over time. Hyper-heuristics are problem-independent meta-heuristic techniques that are automating the process of selecting and generating multiple low-level heuristics to solve static combinatorial optimization problems. In this paper, we present a novel hybrid strategy for applicability of hyper-heuristic techniques on dynamic environments by integrating them with the memory/search algorithm. The memory/search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems. We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark. The former problem is extended from the generalized assignment problem by changing resource consumptions, capacity constraints and costs of jobs over time; and the latter one is a well-known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation performed on various instances of the given two problems validates that our hyper-heuristic integrated framework significantly outperforms the memory/search algorithm.

[1]  Haluk Topcuoglu,et al.  Performance evaluation of evolutionary heuristics in dynamic environments , 2011, Applied Intelligence.

[2]  Jürgen Branke,et al.  The Role of Representations in Dynamic Knapsack Problems , 2006, EvoWorkshops.

[3]  Enrique Alba,et al.  ABC, a new performance tool for algorithms solving dynamic optimization problems , 2010, IEEE Congress on Evolutionary Computation.

[4]  Graham Kendall,et al.  A Hyperheuristic Approach to Scheduling a Sales Summit , 2000, PATAT.

[5]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[6]  Changhe Li,et al.  A Self-Learning Particle Swarm Optimizer for Global Optimization Problems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

[8]  Fred W. Glover,et al.  An Ejection Chain Approach for the Generalized Assignment Problem , 2004, INFORMS J. Comput..

[9]  Terence C. Fogarty,et al.  Adaptive Combustion Balancing in Multiple Burner Boiler Using a Genetic Algorithm with Variable Range of Local Search , 1997, ICGA.

[10]  Graham Kendall,et al.  A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine , 2003 .

[11]  John J. Grefenstette,et al.  Genetic Algorithms for Tracking Changing Environments , 1993, ICGA.

[12]  Carlos Cruz,et al.  Optimization in dynamic environments: a survey on problems, methods and measures , 2011, Soft Comput..

[13]  Hui Cheng,et al.  IMMIGRANTS-ENHANCED MULTI-POPULATION GENETIC ALGORITHMS FOR DYNAMIC SHORTEST PATH ROUTING PROBLEMS IN MOBILE AD HOC NETWORKS , 2012, Appl. Artif. Intell..

[14]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[15]  Paolo Toth,et al.  Knapsack Problems: Algorithms and Computer Implementations , 1990 .

[16]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[17]  Shengxiang Yang,et al.  Evolutionary Computation in Dynamic and Uncertain Environments , 2007, Studies in Computational Intelligence.

[18]  Graham Kendall,et al.  Monte Carlo hyper-heuristics for examination timetabling , 2012, Ann. Oper. Res..

[19]  Günther R. Raidl,et al.  An improved hybrid genetic algorithm for the generalized assignment problem , 2004, SAC '04.

[20]  Shengxiang Yang,et al.  Continuous dynamic problem generators for evolutionary algorithms , 2007, 2007 IEEE Congress on Evolutionary Computation.

[21]  John E. Beasley,et al.  A genetic algorithm for the generalised assignment problem , 1997, Comput. Oper. Res..

[22]  Stefan Droste,et al.  Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Analysis of the (1+1) Ea for a Dynamically Bitwise Changing Onemax Analysis of the (1+1) Ea for a Dynamically Bitwise Changing Onemax , 2003 .

[23]  Michel Gendreau,et al.  A review of dynamic vehicle routing problems , 2013, Eur. J. Oper. Res..

[24]  A. Sima Etaner-Uyar,et al.  Towards an analysis of dynamic environments , 2005, GECCO '05.

[25]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[26]  Edmund K. Burke,et al.  Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems , 2009, J. Oper. Res. Soc..

[27]  Raphael T. Haftka,et al.  Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[28]  Haluk Topcuoglu,et al.  A comparative study of evolutionary optimization techniques in dynamic environments , 2006, GECCO '06.

[29]  Geraldo Robson Mateus,et al.  Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[30]  P. Cowling,et al.  A Parameter-Free Hyperheuristic for Scheduling a Sales Summit , 2002 .

[31]  Berna Kiraz,et al.  Hyper-heuristic approaches for the dynamic generalized assignment problem , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[32]  Zbigniew Michalewicz,et al.  Adaptive Business Intelligence: Three Case Studies , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[33]  Rasmus K. Ursem,et al.  Multinational GAs: Multimodal Optimization Techniques in Dynamic Environments , 2000, GECCO.

[34]  Shengxiang Yang,et al.  A self-organizing random immigrants genetic algorithm for dynamic optimization problems , 2007, Genetic Programming and Evolvable Machines.

[35]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.