Memory Adaptive Reasoning and Greedy Assignment Techniques for the Capacitated Minimum Spanning Tree Problem

Posted: 2 Jul 2013

Date Written: June 1, 1998

Abstract

It is the purpose of this paper to investigate effects of adding randomization to a memory-based heuristic. The algorithms we propose are applied to the Capacitated Minimum Spanning Tree problem (CMST), and we study the combined effects of simultaneously applying a memory-based and a random-based heuristic to the CMST. This paper uses the Adaptive Reasoning Technique (ART) and concepts from the greedy randomized adaptive search procedure for solving the CMST. The resulting hybrid procedure is tested against the stand-alone Esau-Williams heuristic procedure, as well as the stand-alone greedy assignment technique. We find that randomization does not constructively add to the memory-based procedure, as ART alone typically outperforms all other approaches in terms of solution quality, while expending a modest amount of computational error.

Suggested Citation

Rolland, Erik and Patterson, Raymond and Pirkul, Hasan, Memory Adaptive Reasoning and Greedy Assignment Techniques for the Capacitated Minimum Spanning Tree Problem (June 1, 1998). Meta-Heuristics, 1999, pp 487-498, University of Alberta School of Business Research Paper No. 2013-1084, Available at SSRN: https://ssrn.com/abstract=2282097

Erik Rolland (Contact Author)

Ohio State University ( email )

2100 Neil Avenue
Columbus, OH 43210
United States
614-292-7692 (Phone)
614-292-2118 (Fax)

Raymond Patterson

Independent

Hasan Pirkul

Independent ( email )

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