Multi-criteria Optimization Using the AMALGAM Software Package: Theory, Concepts, and MATLAB Implementation

The evolutionary algorithm AMALGAM implements the novel concept of adaptive multimethod search to ensure a fast, reliable and computationally ecient solution to multiobjective optimization problems. The method finds a well-distributed set of Pareto solutions within a single optimization run, and achieves an excellent performance compared to commonly used methods such as SPEA2, NSGA-II and MOEA/D. In this paper, I review the basic elements of AMALGAM, provide a pseudo-code of the algorithm, and introduce a MATLAB toolbox which provides scientists and engineers with an arsenal of options and utilities to solve multiobjective optimization problems involving (among others) multimodality, high-dimensionality, bounded parameter spaces, dynamic simulation models, and distributed multi-core computation. The AMALGAM toolbox supports parallel computing to permit inference of CPU-intensive system models, and provides convergence diagnostics and graphical output. Four dierent case studies are used to illustrate the main

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