An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures

Authors

  • Julian Scott Yeomans York University, Schulich School of Business

DOI:

https://doi.org/10.14738/tmlai.65.5184

Keywords:

Modelling-to-generate-alternatives, Metaheuristics, Population-based algorithms

Abstract

“Real world” problems typically possess complex performance conditions peppered with inconsistent performance requirements. This situation occurs because multifaceted problems are often riddled with incompatible performance objectives and contradictory design requirements which can be difficult – if not impossible – to specify when the requisite decision models are formulated. Thus, it is often desirable to generate a set of disparate alternatives that provide diverse approaches to the problem. These dissimilar options should be close-to-optimal with respect to any specified objective(s), but remain maximally different from all other solutions in the decision space. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). This paper outlines an MGA algorithmic approach that can simultaneously generate a set of maximally different alternatives using any population-based metaheuristic.

Author Biography

Julian Scott Yeomans, York University, Schulich School of Business

Operations Management and Information Systems Area, Professor

References

(1) Brugnach, M., A. Tagg, F. Keil, and W.J. De Lange, Uncertainty matters: computer models at the science-policy interface. Water Resources Management, 2007. 21: p. 1075-1090.

(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-

(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

Downloads

Published

2018-11-03

How to Cite

Yeomans, J. S. (2018). An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures. Transactions on Engineering and Computing Sciences, 6(5), 01. https://doi.org/10.14738/tmlai.65.5184