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Julian Scott Yeomans

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Julian Scott Yeomans

WSEAS Transactions on Computers

Print ISSN: 1109-2750
E-ISSN: 2224-2872

Volume 18, 2019

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

A Multicriteria Simulation - Optimization Algorithm for Generating Sets of Alternatives using Population-based Metaheuristics

AUTHORS: Julian Scott Yeomans

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ABSTRACT: Stochastic optimization problems are often overwhelmed with inconsistent performance requirements and incompatible performance specifications that can be difficult to detect during problem formulation. Therefore, it can prove beneficial to create a set of dissimilar options that provide divergent perspectives to the problem. These alternatives should be near-optimal with respect to the specified objective(s), but be maximally different from each other in the decision region. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulationoptimization approaches are commonly employed to solve computationally difficult problems containing significant stochastic uncertainties. This paper provides a new, stochastic, multicriteria MGA approach that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based algorithm.

KEYWORDS: Multicriteria Objectives, Population-based algorithms, Modelling-to-generate-alternatives


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WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 18, 2019, Art. #9, pp. 74-81

Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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