A Biologically-Inspired Metaheuristic Approach for the Simultaneous Generation of Alternatives

A Biologically-Inspired Metaheuristic Approach for the Simultaneous Generation of Alternatives

Julian Scott Yeomans (York University, Toronto, Canada)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/IJCCP.2018070101

Abstract

Decision-making in the “real world” involves complex problems that tend to be riddled with competing performance objectives and possess requirements which are very difficult to incorporate into any underlying decision support models. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it is preferable to generate numerous dissimilar alternatives that provide disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known objectives, but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This article provides an efficient biologically-inspired algorithm that simultaneously generates multiple, maximally different alternatives by employing the Firefly Algorithm metaheuristic. The effectiveness of this algorithm is demonstrated on an engineering optimization benchmark test problem
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Introduction

Decision-making in the “real world” involves complex problems that tend to be riddled with competing performance objectives and possess requirements which are very difficult to incorporate into any underlying decision support models (Belarbi et al., 2017; Matallah et al., 2017; Brugnach et al., 2007; Janssen et al., 2010; Matthies et al., 2007; Mowrer, 2000; Walker et al., 2003). While an optimal solution might provide the theoretically best answer to a mathematical model, in general, it will not be the best solution to the fundamental “real” problem since there are invariably unmodelled objectives and unquantifiable issues not incorporated in the problem formulation (Acharjya & Anitha, 2017; Brugnach et al., 2007; Janssen et al., 2010; Loughlin et al., 2001). Consequently, it is preferable to generate a number of different alternatives that provide multiple, disparate perspectives to any particular problem (Matthies et al., 2007; Yeomans & Gunalay, 2011). Preferably these alternatives should all possess good (i.e. near-optimal) objective measures with respect to the modelled objective(s), but be as fundamentally different as possible from each other in terms of the system structures characterized by their decision variables. Several approaches collectively referred to as modelling-to-generate-alternatives (MGA) have been developed in response to this multi-solution creation requirement (Brill et al., 1982; Loughlin et al., 2001; Yeomans & Gunalay, 2011).

The primary motivation behind MGA is to construct a manageably small set of alternatives that are good with respect to all measured objective(s) yet are as fundamentally different as possible from each other within the prescribed decision space. By adopting a maximally different method, the resulting alternative solution set is likely to provide very different perspectives with respect to any unmodelled issues, while simultaneously providing different choices that all perform somewhat similarly with respect to the modelled objectives (Walker et al., 2003). Obviously, the decision-makers must then conduct a subsequent comprehensive comparison of these alternatives to determine which options would most closely satisfy their very specific circumstances (Arrais-Castro et al., 2015). Consequently, MGA approaches should necessarily be classified as a decision support processes rather than the role of explicit solution determination methods assumed, in general, for optimization (see, also: Benatia et al., 2016; Sharma & Virmani, 2017; Strand et al., 2017).

Previous MGA methods have employed direct, iterative processes for generating alternatives by incrementally re-running their solution algorithms whenever new alternatives must be produced (Baugh et al., 1997; Brill et al., 1982; Loughlin et al., 2001; Yeomans & Gunalay, 2011; Zechman & Ranjithan, 2004). These iterative approaches follow the seminal MGA approach of Brill et al. (1982) in which, once an initial problem formulation has been optimized, the supplementary alternatives are created one-by-one. Consequently, these iterative approaches all require n+1 runnings of their respective algorithms to optimize the initial problem and to subsequently create their n alternatives (Imanirad & Yeomans, 2013; Imanirad et al., 2012a; Yeomans & Gunalay, 2011).

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