A computationally efficient modelling-to-generate-alternatives method using the firefly algorithm
Raha Imanirad, Julian Scott Yeomans · 2012
In solving many practical mathematical programming applications, it is preferable to formulate numerous quantifiably good alternatives that provide very different perspectives to the problem. This is because decision-making typically involves complex problems that are riddled with incompatible and inconsistent performance objectives and possess competing design requirements which are very difficult - if not impossible - to quantify and capture at the time that the supporting decision models are constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modelling-to-generate- alternatives (MGA). This study demonstrates how the biologically-inspired, Firefly algorithm can be used to efficiently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces.