First EAI International Conference on Computer Science and Engineering

Research Article

Genetic Algorithms-based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries

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  • @INPROCEEDINGS{10.4108/eai.27-2-2017.152266,
        author={AbdelMonaem F.M. AbdAllah and Daryl L. Essam and Ruhul A. Sarker},
        title={Genetic Algorithms-based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries},
        proceedings={First EAI International Conference on Computer Science and Engineering},
        publisher={EAI},
        proceedings_a={COMPSE},
        year={2017},
        month={2},
        keywords={},
        doi={10.4108/eai.27-2-2017.152266}
    }
    
  • AbdelMonaem F.M. AbdAllah
    Daryl L. Essam
    Ruhul A. Sarker
    Year: 2017
    Genetic Algorithms-based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries
    COMPSE
    EAI
    DOI: 10.4108/eai.27-2-2017.152266
AbdelMonaem F.M. AbdAllah1,*, Daryl L. Essam, Ruhul A. Sarker
  • 1: School of Engineering and Information Technology, University of New South Wales Canberra (UNSW Canberra@ADFA), Canberra 2600, Australia
*Contact email: a.abdallah@student.adfa.edu.au

Abstract

In this paper, we consider a class of dynamic optimization problems in which the number of active variables and their boundaries vary as time passes (DOPUAVBs). We assume that such changes in different time periods are not known to decision makers due to certain internal and external factors. Here, we propose three variants of genetic algorithm to deal with a dynamic problem class. These proposed algorithms are compared with one another, as well as with a standard genetic algorithm based on the best of feasible generations and feasibility percentage. Experimental results and statistical tests clearly show the superiority of our proposed algorithms. Moreover, the proposed algorithm, which simultaneous addresses two sub-problems of such dynamic problems, shows superiority to other algorithms in most cases.