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Workshop on Evolutionary Computation

Workshops on Applications of Evolutionary Computation

EvoWorkshops 1993, EvoWorkshops 1994: Progress in Evolutionary Computation pp 127–154Cite as

Development of hybrid optimisation techniques based on genetic algorithms and simulated annealing

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 956))

Abstract

This paper develops a hybrid optimisation algorithm, GAA, by combining the genetic algorithms (GAs) approach and the simulate-dannealing technique (SA). The combination facilitates the introduction of more diversity into the population and prevents the problem of premature convergence. To counter the adverse effects of mutation, two effective measures are developed and included in the combined GA/SA method. This algorithm is then further developed to minimise the memory requirement. The revised hybrid algorithm GAA2 is analysed and compared to GAs, GAA and SA. A guideline for setting the parameters for executing GAA2 is also established. The performance of the developed algorithms are demonstrated through their applications to the hydro-thermal scheduling problem in power systems.

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Authors and Affiliations

  1. Artificial Intelligence and Power Systems Research Group, Department of Electrical and Electronic Engineering, The University of Western Australia, 6009, Nedlands, Western Australia

    Kit Po Wong & Yin Wa Wong

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  1. Kit Po Wong
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  2. Yin Wa Wong
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Xin Yao

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© 1995 Springer-Verlag Berlin Heidelberg

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Wong, K.P., Wong, Y.W. (1995). Development of 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_52

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  • DOI: https://doi.org/10.1007/3-540-60154-6_52

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60154-8

  • Online ISBN: 978-3-540-49528-4

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