A New Evolutionary Approach for the Optimal Communication Spanning Tree Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
New insights into the OCST problem: integrating node degrees and their location in the graph
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the bias and performance of the edge-set encoding
IEEE Transactions on Evolutionary Computation
A memetic algorithm for the optimum communication spanning tree problem
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
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This paper considers the Euclidean variant of the optimal communication spanning tree (OCST) problem. Previous work analyzed features of high-quality solutions and found that edges in optimal solutions have low weight and point towards the center of a tree. Consequently, integrating this problem-specific knowledge into a metaheuristic increases its performance. In this paper, we present an approach to dynamically change the objective function to guide the search process into promising areas. Our approach is based on guided local search. The resulting problem-specific guided local search method considering weight and orientation of edges outperforms standard variants considering only edge weights as well as state-of-the-art evolutionary algorithms using edge-sets for larger problems.