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Simulated Annealing Meta-heuristic for Addition Chain Optimization

Author

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  • Silvestre Ascencion Garcia Sanchez

    (Instituto Politecno Nacional, Mexico.)

  • Luis Calderon Luis Calderon Osorno

    (Instituto Politecno Nacional, Mexico.)

  • Edmundo Rene Duran Camarillo

    (Instituto Politecno Nacional, Mexico.)

Abstract

In this work, a simulated annealing (SA) algorithm is implemented in the Python programming language with the aim of minimizing addition chains of the "star-chain" type. The strategies for generating and mutating individuals are similar to those used by the evolutionary programming (EP) and genetic algorithms (GA) methods found in the literature [1]-[3]. The proposed variant is the acceptance mechanism that is based on the simulated annealing meta-heuristic (SA). The hypothesis is that with the proposed acceptance mechanism, diversity is obtained in the search-space through a simple strategy that allows finding better solutions compared to the deterministic method Optimized Window. The simulations were performed with exponents in the range 218-234 and were compared with the results reported in [3], where a GA is proposed to get optimal addition chains. It is concluded that the proposed algorithm is able to find chains of shorter length than those found with the Optimized Window method and with a performance similar to that of the GA proposed in [3].

Suggested Citation

  • Silvestre Ascencion Garcia Sanchez & Luis Calderon Luis Calderon Osorno & Edmundo Rene Duran Camarillo, 2019. "Simulated Annealing Meta-heuristic for Addition Chain Optimization," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 3(6), November.
    • Handle: RePEc:epw:ejece0:v:3:y:2019:i:6:id:19159
    DOI: 10.24018/ejece.2019.3.6.159
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