HyFlex: A Benchmark Framework for Cross-Domain Heuristic Search

This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.

[1]  Michel Gendreau,et al.  Handbook of Metaheuristics , 2010 .

[2]  Michèle Sebag,et al.  Analyzing bandit-based adaptive operator selection mechanisms , 2010, Annals of Mathematics and Artificial Intelligence.

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

[4]  Natalio Krasnogor,et al.  Emergence of profitable search strategies based on a simple inheritance mechanism , 2001 .

[5]  Patrick De Causmaecker,et al.  An Intelligent Hyper-Heuristic Framework for CHeSC 2011 , 2012, LION.

[6]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[7]  Jim E. Smith,et al.  Coevolving Memetic Algorithms: A Review and Progress Report , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[9]  Frédéric Saubion,et al.  Autonomous operator management for evolutionary algorithms , 2010, J. Heuristics.

[10]  Wilfried Jakob,et al.  Towards an Adaptive Multimeme Algorithm for Parameter Optimisation Suiting the Engineers' Needs , 2006, PPSN.

[11]  Edmund K. Burke,et al.  A Time Predefined Variable Depth Search for Nurse Rostering , 2013, INFORMS J. Comput..

[12]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Ender Özcan,et al.  A Hyper-Heuristic Based on Random Gradient, Greedy and Dominance , 2011, ISCIS.

[14]  Ferrante Neri,et al.  An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  Helena Ramalhinho Dias Lourenço,et al.  Iterated Local Search , 2001, Handbook of Metaheuristics.

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

[17]  Philipp Rohlfshagen,et al.  A genetic algorithm with exon shuffling crossover for hard bin packing problems , 2007, GECCO '07.

[18]  Frédéric Saubion,et al.  A Compass to Guide Genetic Algorithms , 2008, PPSN.

[19]  Bart Selman,et al.  Evidence for Invariants in Local Search , 1997, AAAI/IAAI.

[20]  Atsuko Ikegami,et al.  A subproblem-centric model and approach to the nurse scheduling problem , 2003, Math. Program..

[21]  Thomas Stützle,et al.  A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem , 2007, Eur. J. Oper. Res..

[22]  Inyong Ham,et al.  A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem , 1983 .

[23]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[24]  Olli Bräysy,et al.  A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows , 2003, INFORMS J. Comput..

[25]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[26]  Peter I. Cowling,et al.  A Memetic Approach to the Nurse Rostering Problem , 2001, Applied Intelligence.

[27]  Christian Bierwirth,et al.  On Permutation Representations for Scheduling Problems , 1996, PPSN.

[28]  Hector J. Levesque,et al.  A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.

[29]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[30]  Chi Fai Cheung,et al.  A Hyper-Heuristic Inspired by Pearl Hunting , 2012, LION.

[31]  Toby Walsh,et al.  Towards an Understanding of Hill-Climbing Procedures for SAT , 1993, AAAI.

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

[33]  Bart Selman,et al.  Noise Strategies for Improving Local Search , 1994, AAAI.

[34]  Edmund K. Burke,et al.  A scatter search methodology for the nurse rostering problem , 2010, J. Oper. Res. Soc..

[35]  Roger L. Wainwright,et al.  Multiple Vehicle Routing with Time and Capacity Constraints Using Genetic Algorithms , 1993, ICGA.

[36]  Alex S. Fukunaga,et al.  Automated Discovery of Local Search Heuristics for Satisfiability Testing , 2008, Evolutionary Computation.

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

[38]  John Baxter,et al.  Local Optima Avoidance in Depot Location , 1981 .

[39]  Edward W. Felten,et al.  Large-step markov chains for the TSP incorporating local search heuristics , 1992, Oper. Res. Lett..

[40]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[41]  Jeffrey D. Ullman,et al.  Worst-Case Performance Bounds for Simple One-Dimensional Packing Algorithms , 1974, SIAM J. Comput..

[42]  Michel Gendreau,et al.  Adaptive iterated local search for cross-domain optimisation , 2011, GECCO '11.

[43]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[44]  Thomas Stützle,et al.  An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives , 2008, Eur. J. Oper. Res..

[45]  Graham Kendall,et al.  A simulated annealing hyper-heuristic methodology for flexible decision support , 2012, 4OR.

[46]  Mauro Brunato,et al.  Reactive Search and Intelligent Optimization , 2008 .

[47]  Edmund K. Burke,et al.  A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem , 2004, Eur. J. Oper. Res..

[48]  Michel Gendreau,et al.  Vehicle Routing and Adaptive Iterated Local Search within the HyFlex Hyper-heuristic Framework , 2012, LION.

[49]  Sanja Petrovic,et al.  Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms , 2010, IEEE Congress on Evolutionary Computation.

[50]  Luca Di Gaspero,et al.  A Reinforcement Learning approach for the Cross-Domain Heuristic Search Challenge , 2011 .

[51]  David Pisinger,et al.  A general heuristic for vehicle routing problems , 2007, Comput. Oper. Res..

[52]  Michèle Sebag,et al.  Extreme Value Based Adaptive Operator Selection , 2008, PPSN.

[53]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

[54]  Marco Laumanns,et al.  PISA: A Platform and Programming Language Independent Interface for Search Algorithms , 2003, EMO.