A Population-Based Multicriteria Algorithm for Alternative Generation
DOI:
https://doi.org/10.14738/tmlai.74.6733Keywords:
Multicriteria Objectives, Population-based algorithms, Modelling-to-generate-alternativesAbstract
Complex problems are frequently overwhelmed by inconsistent performance requirements and incompatible specifications that can be difficult to identify at the time of problem formulation. Consequently, it is often beneficial to construct a set of different options that provide distinct approaches to the problem. These alternatives need to be close-to-optimal with respect to the specified objective(s), but be maximally different from each other in the solution domain. The approach for creating maximally different solution sets is referred to as modelling-to-generate-alternatives (MGA). This paper introduces a computationally efficient, population-based multicriteria MGA algorithm for generating sets of maximally different alternatives.
References
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[24]. Yeomans, J.S., An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures. Transactions on Machine Learning and Artificial Intelligence, 2018. 6(5): p. 1-9.
[25]. Yeomans, J.S., A Bicriterion Approach for Generating Alternatives Using Population-Based Algorithms. WSEAS Transactions on Systems, 2019. 18(4): p. 29-34.
[2]. Janssen, J.A.E.B., M.S. Krol, R.M.J. Schielen, and A.Y Hoekstra, The effect of modelling quantified expert knowledge and uncertainty information on model based decision making. Environmental Science and Policy, 2010. 13(3): p. 229-238.
[3]. Matthies, M., C. Giupponi, and B. Ostendorf, Environmental decision support systems: Current issues, methods and tools. Environmental Modelling and Software, 2007. 22(2): p. 123-127.
[4]. Mowrer, H.T., Uncertainty in natural resource decision support systems: Sources, interpretation, and importance. Computers and Electronics in Agriculture, 2000. 27(1-3): p. 139-154.
[5]. Walker, W.E., P. Harremoes, J. Rotmans, J.P. Van der Sluis, M.B.A. Van Asselt, P. Janssen, and M.P. Krayer von Krauss, Defining uncertainty – a conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 2003. 4(1): p. 5-17.
[6]. Loughlin, D.H., S.R. Ranjithan, E.D. Brill, and J.W. Baugh, Genetic algorithm approaches for addressing unmodelled objectives in optimization problems. Engineering Optimization, 2001. 33(5): p. 549-569.
[7]. Yeomans, J.S., and Y Gunalay, Simulation-Optimization Techniques for Modelling to Generate Alternatives in Waste Management Planning. Journal of Applied Operational Research, 2011. 3(1): p. 23-35.
[8]. Brill, E.D., S.Y. Chang, and L.D Hopkins, Modelling to generate alternatives: the HSJ approach and an illustration using a problem in land use planning. Management Science. 1982. 28(3): p. 221-235.
[9]. Baugh, J.W., S.C. Caldwell, and E.D Brill, A Mathematical Programming Approach for Generating Alternatives in Discrete Structural Optimization. Engineering Optimization. 1997, 28(1): p. 1-31.
[10]. Zechman, E.M., and S.R. Ranjithan, An Evolutionary Algorithm to Generate Alternatives (EAGA) for Engineering Optimization Problems. Engineering Optimization. 2004, 36(5): p. 539-553.
[11]. Gunalay, Y., J.S. Yeomans, and G.H. Huang, Modelling to generate alternative policies in highly uncertain environments: An application to municipal solid waste management planning. Journal of Environmental Informatics, 2012. 19(2): p. 58-69.
[12]. Imanirad, R., and J.S. Yeomans, Modelling to Generate Alternatives Using Biologically Inspired Algorithms. in Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, X.S. Yang, Editor 2013. Amsterdam: Elsevier (Netherlands). p. 313-333.
[13]. Imanirad, R., X.S. Yang, and J.S. Yeomans, A Computationally Efficient, Biologically-Inspired Modelling-to-Generate-Alternatives Method. Journal on Computing. 2012, 2(2): p. 43-47.
[14]. Yeomans, J.S., An Efficient Computational Procedure for Simultaneously Generating Alternatives to an Optimal Solution Using the Firefly Algorithm, in Nature-Inspired Algorithms and Applied Optimization, Yang, X.S. Editor 2018. Heidelberg (Springer), Germany. p. 261-273.
[15]. Imanirad, R., X.S. Yang, and J.S. Yeomans, A Co-evolutionary, Nature-Inspired Algorithm for the Concurrent Generation of Alternatives. Journal on Computing. 2012, 2(3): p. 101-106.
[16]. Imanirad, R., X.S. Yang, and J.S. Yeomans, Modelling-to-Generate-Alternatives Via the Firefly Algorithm. Journal of Applied Operational Research. 2013. 5(1): p. 14-21.
[17]. Imanirad, R., X.S. Yang, and J.S. Yeomans, A Concurrent Modelling to Generate Alternatives Approach Using the Firefly Algorithm. International Journal of Decision Support System Technology. 2013, 5(2): p. 33-45.
[18]. Imanirad, R., X.S. Yang, and J.S. Yeomans, A Biologically-Inspired Metaheuristic Procedure for Modelling-to-Generate-Alternatives. International Journal of Engineering Research and Applications. 2013, 3(2): p. 1677-1686.
[19]. Yeomans, J.S., Simultaneous Computing of Sets of Maximally Different Alternatives to Optimal Solutions. International Journal of Engineering Research and Applications, 2017. 7(9): p. 21-28.
[20]. Yeomans, J.S., An Optimization Algorithm that Simultaneously Calculates Maximally Different Alternatives. International Journal of Computational Engineering Research, 2017. 7(10): p. 45-50.
[21]. Yeomans, J.S., Computationally Testing the Efficacy of a Modelling-to-Generate-Alternatives Procedure for Simultaneously Creating Solutions. Journal of Computer Engineering, 2018. 20(1): p. 38-45.
[22]. Yeomans, J.S., A Computational Algorithm for Creating Alternatives to Optimal Solutions. Transactions on Machine Learning and Artificial Intelligence, 2017. 5(5): p. 58-68.
[23]. Yeomans, J.S., A Simultaneous Modelling-to-Generate-Alternatives Procedure Employing the Firefly Algorithm, in Technological Innovations in Knowledge Management and Decision Support, Dey, N. Editor, 2019. Hershey, Pennsylvania (IGI Global), USA. p. 19-33.
[24]. Yeomans, J.S., An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures. Transactions on Machine Learning and Artificial Intelligence, 2018. 6(5): p. 1-9.
[25]. Yeomans, J.S., A Bicriterion Approach for Generating Alternatives Using Population-Based Algorithms. WSEAS Transactions on Systems, 2019. 18(4): p. 29-34.
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Published
2019-09-08
How to Cite
Yeomans, J. S. (2019). A Population-Based Multicriteria Algorithm for Alternative Generation. Transactions on Engineering and Computing Sciences, 7(4), 01–08. https://doi.org/10.14738/tmlai.74.6733
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