Abstract
This paper develops coarse-grained parallel algorithms for hybrid optimisation techniques based on genetic algorithms and simulated annealing. The design of the algorithms takes into consideration load balancing, processor synchronisation reduction, communication overhead reduction and memory contention elimination. In addition, the algorithms are designed to avoid the problem of premature convergence which exists in some previous parallel genetic algorithms. The algorithms are implemented on an i860 processor in a simulated environment and are applied to a short-term hydrothermal scheduling problem. The scheduling results are presented and are compared to those found by sequential GAs, by parallel simulated-annealing algorithm and by some earlier parallel genetic algorithms.
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Wong, K.P., and Wong, Y.W.:’ Development of hybrid optimisation techniques based on Genetic Algorithms and Simulated Annealing', companion paper in Proc. of AI'94 Workshop on Evolutionary Computation, Armidale, Australia, Nov. 1994.
HOLLAND, J.H.:’ Adaptation in natural and artificial systems', (Ann Arbor: University of Michigan Press, 1975)
GOLDBERG, D.E.:’ Genetic algorithms in search, optimisation and machine learning’ (Addison-Wesley, Reading, 1989)
KIRKPATRICK, S., GELATT, C.D., Jr., and VECCHI, M.P.:’ Optimisation by simulated annealing', Science, 1983, 220(4598), pp. 671–680.
AARTS, E., and KORST, J.M.:’ Simulated annealing and boltzmann machines: a stochastic approach to combinatorial optimisation and neural computing’ (John Wiley, New York, 1989).
Sannier, A.V. and Goodman, E.D.:’ Genetic learning procedures in distributed environments', Proceedings of the 2nd International Conference on Genetic Algorithm, pp. 162–169.
VIGNAUX, G.A. and MICHALEWICZ, Z:’ A genetic algorithm for the linear transportation problem', IEEE Transactions on Systems, Man and Cybernetics, 1989, Vol 21 (2), pp. 321–326.
Kadaba, N. and Nygard, K.E.:,’ Improving the performance of genetic algorithms in automated discovery of parameters', Proceedings of the Seventh International Conference on Machine Learning, 1990, pp. 140–148.
WONG, K.P., and WONG, Y.W.:’ Development of parallel genetic algorithms’ Australian Journal of Intelligent Information Processing Systems, 1994, Vol. 1, (1) pp. 51–57.
Pettey, C.B., Leuze, M.R. and Grefenstette, J. J:’ A parallel genetic algorithm', Proceedings of the 2nd International Conference on Genetic Algorithm, 1987, pp. 155–161.
COHOON, J. P., HEDGE, S.U., MARTIN, W.N., and RICHARDS, D.S.:’ Distributed genetic algorithms for the floorplan design problem', IEEE Transactions on Computer Design, Vol, 1991, 10(4), pp. 483–492.
Tanese, R.:’ Parallel genetic algorithm for a hypercube', Proceedings of the 2nd International Conference on Genetic Algorithm, 1987, pp. 177–183.
IEEE Committee Report:’ Parallel processing in power systems computation’ IEE/PES, Summer Meeting 1991, Paper Number 91 SM 503-3 PWRS.
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Wong, K.P., Wong, Y.W. (1995). Development of parallel hybrid optimisation techniques based on genetic algorithms and simulated annealing. In: Yao, X. (eds) Progress in Evolutionary Computation. EvoWorkshops EvoWorkshops 1993 1994. Lecture Notes in Computer Science, vol 956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60154-6_53
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DOI: https://doi.org/10.1007/3-540-60154-6_53
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