A Unified View on Hybrid Metaheuristics

Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and classifies them based on various characteristics. In particular with respect to low-level hybrids of different metaheuristics, a unified view based on a common pool template is described. It helps in making similarities and different key components of existing metaheuristics explicit. We then consider these key components as a toolbox for building new, effective hybrid metaheuristics. This approach of thinking seems to be superior to sticking too strongly to the philosophies and historical backgrounds behind the different metaheuristic paradigms. Finally, particularly promising possibilities of combining metaheuristics with constraint programming and integer programming techniques are highlighted.

[1]  M. Kamel,et al.  A Taxonomy of Cooperative Search Algorithms , 2005, Hybrid Metaheuristics.

[2]  Jörg Denzinger,et al.  On cooperation between evolutionary algorithms and other search paradigms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[4]  G. P. McKeown,et al.  Optimization Software Class Libraries , 2002, Operations Research/Computer Science Interfaces Series.

[5]  Pablo Moscato,et al.  Memetic algorithms: a short introduction , 1999 .

[6]  Enrique Alba,et al.  Parallel Metaheuristics: A New Class of Algorithms , 2005 .

[7]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Rama Akkiraju,et al.  Asynchronous Teams , 2003, Handbook of Metaheuristics.

[10]  Carlos Cotta,et al.  Embedding Branch and Bound within Evolutionary Algorithms , 2003, Applied Intelligence.

[11]  Stefan Voß,et al.  Controlled Pool Maintenance for Metaheuristics , 2005 .

[12]  Günther R. Raidl,et al.  Models and algorithms for three-stage two-dimensional bin packing , 2007, Eur. J. Oper. Res..

[13]  Thomas Stützle,et al.  Stochastic Local Search , 2007, Handbook of Approximation Algorithms and Metaheuristics.

[14]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[15]  Andrea Lodi,et al.  Local Search and Constraint Programming , 2003, Handbook of Metaheuristics.

[16]  Carlos Cotta,et al.  Proceedings of the 5th International Workshop on Hybrid Metaheuristics , 2008 .

[17]  Stefan Voß,et al.  Hotframe: A Heuristic Optimization Framework , 2003 .

[18]  Peter J. Stuckey,et al.  Programming with Constraints: An Introduction , 1998 .

[19]  Günther R. Raidl,et al.  Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification , 2005, IWINAC.

[20]  Matteo Fischetti,et al.  Local branching , 2003, Math. Program..

[21]  Laurence A. Wolsey,et al.  Integer and Combinatorial Optimization , 1988 .

[22]  Alain Hertz,et al.  A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization , 1999, J. Heuristics.

[23]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[24]  Carlos Cotta.  A Study of Hybridisation Techniques and Their Application to the Design of Evolutionary Algorithms , 1998, AI Commun..

[25]  Silvano Martello,et al.  Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization , 2012 .

[26]  Jan Karel Lenstra,et al.  A local search template , 1998, Comput. Oper. Res..

[27]  F. Glover,et al.  Fundamentals of Scatter Search and Path Relinking , 2000 .

[28]  Günther R. Raidl,et al.  Cooperating Memetic and Branch-and-Cut Algorithms for Solving the Multidimensional Knapsack Problem , 2005 .

[29]  Pedro S. de Souza,et al.  Asynchronous Teams: Cooperation Schemes for Autonomous Agents , 1998, J. Heuristics.

[30]  R. Storer,et al.  New search spaces for sequencing problems with application to job shop scheduling , 1992 .

[31]  Carlos Cotta dash,et al.  A study of hybridisation techniques and their application to the design of evolutionary algorithms , 1998 .

[32]  Christian Blum,et al.  Hybrid Metaheuristics, Second International Workshop, HM 2005, Barcelona, Spain, August 29-30, 2005, Proceedings , 2005, Hybrid Metaheuristics.

[33]  F. Glover.  HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

[34]  Abraham P. Punnen,et al.  A survey of very large-scale neighborhood search techniques , 2002, Discret. Appl. Math..

[35]  B. Müller,et al.  Solution of the Traveling-Salesman Problem , 1995 .

[36]  Enrique Alba,et al.  Parallel Hybrid Metaheuristics , 2005 .

[37]  Enrique Alba,et al.  An Introduction to Metaheuristic Techniques , 2005 .

[38]  El-Ghazali Talbi,et al.  A Taxonomy of Hybrid Metaheuristics , 2002, J. Heuristics.