A Concurrent Modelling to Generate Alternatives Approach Using the Firefly Algorithm

Abstract

Real world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. 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 alternatives that provide dissimilar approaches to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), 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 study demonstrates how the Firefly Algorithm can concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new approach is computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures.