Adaptive and multi-mining versions of the DM-GRASP hybrid metaheuristic

Metaheuristics represent an important class of techniques to solve, approximately, hard combinatorial optimization problems for which the use of exact methods is impractical. Some researches have been combining machine learning techniques with metaheuristics to adaptively guide and improve the search for near optimal solutions. An example of such development is the DM-GRASP, a hybrid version of the Greedy Randomized Adaptative Search Procedures (GRASP) metaheuristic which incorporates a data mining process. In this hybrid proposal, after executing half of the total number of iterations, the data mining process extracts patterns from an elite set of sub-optimal solutions for the optimization problem. These patterns present characteristics of near optimal solutions and can be used to guide the following half GRASP iterations in the search through the solution space. In this work, we explore new versions of the DM-GRASP metaheuristic to experiment, not a single activation, but multiple and adaptive executions of the data mining process during the metaheuristic execution. We also applied the data mining technique into a reactive GRASP to show that a more sophisticated and not memoryless GRASP approach can also benefit from the use of this technique. In order to evaluate these new proposals, we adopted the server replication for reliable multicast problem since the best known results for this problem were obtained by GRASP and DM-GRASP implementations. The computational experiments, comparing traditional GRASP, DM-GRASP, and the new proposals, showed that multiple and adaptive executions of the data mining process can improve the results obtained by the DM-GRASP hybrid metaheuristic—the new proposals were able to find better results in less computational time for the reliable multicast problem.

[1]  Georg Carle,et al.  How bad is reliable multicast without local recovery? , 1998, Proceedings. IEEE INFOCOM '98, the Conference on Computer Communications. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Gateway to the 21st Century (Cat. No.98.

[2]  Alexandre Plastino,et al.  NGL01-4: A Hybrid GRASP with Data Mining for Efficient Server Replication for Reliable Multicast , 2006, IEEE Globecom 2006.

[3]  Fred Glover,et al.  Improved Constructive Multistart Strategies for the Quadratic Assignment Problem Using Adaptive Memory , 1999, INFORMS J. Comput..

[4]  Alexandre Plastino,et al.  A Hybrid GRASP with Data Mining for the Maximum Diversity Problem , 2005, Hybrid Metaheuristics.

[5]  S. L. HAKIMIt.  AN ALGORITHMIC APPROACH TO NETWORK LOCATION PROBLEMS. , 1979 .

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

[7]  Mauricio G. C. Resende,et al.  Greedy Randomized Adaptive Search Procedures , 1995, J. Glob. Optim..

[8]  Kenneth L. Calvert,et al.  Modeling Internet topology , 1997, IEEE Commun. Mag..

[9]  Gösta Grahne,et al.  Efficiently Using Prefix-trees in Mining Frequent Itemsets , 2003, FIMI.

[10]  Mauricio G. C. Resende,et al.  Grasp: An Annotated Bibliography , 2002 .

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

[12]  M. Resende,et al.  A probabilistic heuristic for a computationally difficult set covering problem , 1989 .

[13]  Fred Glover,et al.  Scatter Search and Path Relinking: Advances and Applications , 2003, Handbook of Metaheuristics.

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Celso C. Ribeiro,et al.  Adaptive memory in multistart heuristics for multicommodity network design , 2011, J. Heuristics.

[16]  Celso C. Ribeiro,et al.  Greedy Randomized Adaptive Search Procedures , 2003, Handbook of Metaheuristics.

[17]  Alexandre Plastino,et al.  Hybridization of GRASP Metaheuristic with Data Mining Techniques , 2006, J. Math. Model. Algorithms.

[18]  Alexandre Plastino,et al.  Applications of the DM-GRASP heuristic: a survey , 2008, Int. Trans. Oper. Res..

[19]  Sneha Kumar Kasera,et al.  A comparison of server-based and receiver-based local recovery approaches for scalable reliable multicast , 1998, Proceedings. IEEE INFOCOM '98, the Conference on Computer Communications. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Gateway to the 21st Century (Cat. No.98.

[20]  Celso C. Ribeiro,et al.  On the Use of Run Time Distributions to Evaluate and Compare Stochastic Local Search Algorithms , 2009, SLS.

[21]  Ian Witten,et al.  Data Mining , 2000 .

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

[23]  Bo Li,et al.  Server replication and its placement for reliable multicast , 2000, Proceedings Ninth International Conference on Computer Communications and Networks (Cat.No.00EX440).

[24]  Alexandre Plastino,et al.  Hybridization of GRASP Metaheuristics with Data Mining Techniques , 2004, Hybrid Metaheuristics.

[25]  Alex Alves Freitas,et al.  A hybrid data mining metaheuristic for the p‐median problem , 2011, Stat. Anal. Data Min..

[26]  Andrea Lodi,et al.  An evolutionary heuristic for quadratic 0-1 programming , 1999, Eur. J. Oper. Res..

[27]  O. Kariv,et al.  An Algorithmic Approach to Network Location Problems. II: The p-Medians , 1979 .

[28]  Celso C. Ribeiro,et al.  TTT plots: a perl program to create time-to-target plots , 2007, Optim. Lett..

[29]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[30]  Celso C. Ribeiro,et al.  Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment , 2000, INFORMS J. Comput..

[31]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

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