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A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems

Aydin, M. Emin; Fogarty, Terence C.


M. Emin Aydin

Terence C. Fogarty


In this paper, the Evolutionary Simulated Annealing (ESA) algorithm, its distributed implementation (dESA) and its application to two combinatorial problems are presented. ESA consists of a population, a simulated annealing operator, instead of the more usual reproduction operators used in evolutionary algorithms, and a selection operator. The implementation is based on a multi island (agent) system running on the Distributed Resource Machine (DRM), which is a novel, scalable, distributed virtual machine based on Java technology. As WAN/LAN systems are the most common multi-machine systems, dESA implementation is based on them rather than any other parallel machine. The problems tackled are well-known combinatorial optimisation problems, namely, the classical job-shop scheduling problem and the uncapacitated facility location problem. They are difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit (CPU) time spent. Both applications show that dESA solves problems finding either the optimum or a very near optimum solution within a reasonable time outperforming the recent reported approaches for each one allowing the faster solution of existing problems and the solution of larger problems.


Fogarty, T. C., & Aydin, M. E. (2004). A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems. Journal of Heuristics, 10(3), 269-292.

Journal Article Type Article
Publication Date May 1, 2004
Journal Journal of Heuristics
Print ISSN 1381-1231
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 10
Issue 3
Pages 269-292
Keywords distributed evolutionary simulated annealing, multi-agent systems, job-shop scheduling, uncapacitated facility location, distributed resource machine
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