A tree-based algorithm for distributed mutual exclusion
ACM Transactions on Computer Systems (TOCS)
Compression of correlated bit-vectors
Information Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computing a Diameter-Constrained Minimum Spanning Tree in Parallel
CIAC '00 Proceedings of the 4th Italian Conference on Algorithms and Complexity
Computing a diameter-constrained minimum spanning tree
Computing a diameter-constrained minimum spanning tree
Proceedings of the 2003 ACM symposium on Applied computing
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Improved heuristics for the bounded-diameter minimum spanning tree problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Greedy heuristics for the bounded diameter minimum spanning tree problem
Journal of Experimental Algorithmics (JEA)
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Multiobjective EA approach for improved quality of solutions for spanning tree problem
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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The Bounded Diameter (a.k.a Diameter Constraint) Minimum Spanning Tree (BDMST/DCMST) is a well-known combinatorial optimization problem. In this work, we recast a few well-known heuristics, which are evolved for BDMST problem, to a Bi-Objective Minimum Spanning Tree (BO-MST) problem and then obtain Pareto fronts. On visualizing the Pareto fronts, it is observed that none of the heuristics provides the best solution across the complete range of the diameter. We have used a Multi-Objective Evolutionary Algorithm (MOEA) approach to improve the Pareto front for BOMST, which in turn provides better solution for BDMST instances. We observe that the MOEA provides improved Pareto front solutions across the complete range of the diameter over Pareto front solutions generated from individual heuristics.