Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Bicriteria network design problems
Journal of Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Computing a Diameter-Constrained Minimum Spanning Tree in Parallel
CIAC '00 Proceedings of the 4th Italian Conference on Algorithms and Complexity
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Proceedings of the 2003 ACM symposium on Applied computing
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
A new evolutionary approach to the degree-constrained minimumspanning tree problem
IEEE Transactions on Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
Analysis of a multiobjective evolutionary algorithm on the 0-1 knapsack problem
Theoretical Computer Science
Multiobjective network design for realistic traffic models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Concurrent topology and routing optimization in automotive network integration
Proceedings of the 45th annual Design Automation Conference
Designing heterogeneous ECU networks via compact architecture encoding and hybrid timing analysis
Proceedings of the 46th Annual Design Automation Conference
Improvement of bounded-diameter MST instances with hybridization of multi-objective EA
Proceedings of the 2011 International Conference on Communication, Computing & Security
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The problem of computing spanning trees along with specific constraints is mostly NP-hard. Many approximation and stochastic algorithms which yield a single solution, have been proposed. In this paper, we formulate the generic multi-objective spanning tree (MOST) problem and consider edge-cost and diameter as the two objectives. Since the problem is hard, and the Pareto-front is unknown, the main issue in such problem-instances is how to assess the convergence. We use a multiobjective evolutionary algorithm (MOEA) that produces diverse solutions without needing a priori knowledge of the solution space, and generate solutions from multiple tribes in order to assess movement of the solution front. Since no experimental results are available for MOST, we consider three well known diameter-constrained minimum spanning tree (dc-MST) algorithms including randomized greedy heuristics (RGH) which represents the current state of the art on the dc-MST, and modify them to yield a (near-) optimal solution-fronts. We quantify the obtained solution fronts for comparison. We observe that MOEA provides superior solutions in the entire-range of the Pareto-front, which none of the existing algorithms could individually do.