**AUTHORS:**Yavuz Gunalay, Julian Scott Yeomans

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**ABSTRACT:**
Engineering optimization problems can be dominated by inconsistent performance requirements and
incompatible specifications that can be difficult to detect when supporting mathematical programming models
are formulated. Thus, it often proves advantageous to construct a set of options that provide dissimilar
approaches to such problems. These alternatives should satisfy the required performance criteria, but be
maximally different from each other in their decision spaces. The method for constructing maximally different
sets of solutions is referred to as modelling-to-generate-alternatives (MGA). This paper considers a
multicriteria method that can generate sets of maximally different alternatives using any population-based
solution algorithm. This MGA approach is both computationally efficient and simultaneously produces the
prescribed number of maximally different solution alternatives in a single computational run of the procedure.
The computational efficacy of this multicriteria MGA approach is demonstrated on two commonly-tested
engineering optimization problems using the population-based Firefly Algorithm metaheuristic.

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

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