The ParMetaOpt experience: performance of parallel metaheuristics on scheduling optimization

Authors:
Plamenka Borovska;George Yanchev
Affiliations:
Computer Systems Department, Technical University of Sofia, Sofia, Bulgaria;Computer Systems Department, Technical University of Sofia, Sofia, Bulgaria
Venue:
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Year:
2009

Citing 2
Cited 1

Parallel Metaheuristics: A New Class of Algorithms

Parallel Metaheuristics: A New Class of Algorithms
Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/Crc Computer & Information Science Series)

Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/Crc Computer & Information Science Series)

Exploring the efficiency of parallel bacteria foraging metaheuristics for job shop scheduling problem optimization

Proceedings of the 12th International Conference on Computer Systems and Technologies

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Abstract

Parallel metaheuristics provides innovative and powerful alternative for combinatorial optimization providing the opportunity to find out near-optimal solutions in reasonable time. The goal of this paper is to reveal the experience of utilizing the experimental parallel metaheuristics framework ParMetaOpt, developed at Computer Systems Dept., Technical University of Sofia. Parallel metaheuristics algorithm have been designed and implemented based on population based methods (evolutionary computation, artificial bee colony and ant colony optimization) and trajectory based methods (GRASP, Tabu search and simulated annealing) for the case studies of the timetabling and the job shop scheduling problems. Parallel performance evaluation and analysis have been presented on the basis of hybrid (MPI+OpenMP) parallel program implementations on compact heterogeneous computer cluster.