A Knowledge-Based Evolution Strategy for the Multi-Objective Minimum Spanning Tree Problem

Author

Moradkhan, M. Davis ; Browne, Will N.

Author_Institution

Univ. of Reading, Reading

fYear

0

fDate

0-0 0

Firstpage

1391

Lastpage

1398

Abstract

A fast knowledge-based evolution strategy, KES, for the multi-objective minimum spanning tree, is presented. The proposed algorithm is validated, for the bi-objective case, with an exhaustive search for small problems (4-10 nodes), and compared with a deterministic algorithm, EPDA and NSGA-II for larger problems (up to 100 nodes) using benchmark hard instances. Experimental results show that KES finds the true Pareto fronts for small instances of the problem and calculates good approximation Pareto sets for larger instances tested. It is shown that the fronts calculated by KES are superior to NSGA-II fronts and almost as good as those established by EPDA. KES is designed to be scalable to multi-objective problems and fast due to its small complexity.

Keywords

Pareto optimisation; computational complexity; deterministic algorithms; evolutionary computation; minimisation; set theory; tree searching; trees (mathematics); Pareto set theory; combinatorial optimization problem; deterministic algorithm; graph theory; knowledge-based evolution strategy design; multiobjective minimum spanning tree problem; polynomial time algorithm; search problem; Application software; Benchmark testing; Computer networks; Costs; Cybernetics; Design optimization; Evolutionary computation; Pipelines; Polynomials; Tree graphs;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2006. CEC 2006. IEEE Congress on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9487-9

Type

conf

DOI

10.1109/CEC.2006.1688471

Filename

1688471