A multiobjective evolutionary algorithm toolbox for computer-aided multiobjective optimization

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 31; no. 4; pp. 537 - 556
• Main Authors: Tan, K.C., Lee, T.H., Khoo, D., Khor, E.F.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2001
• Subjects: Computation; Computer interfaces; Constraint optimization; Convergence; Decision making; Design optimization; Distributed computing; Evolutionary; Evolutionary algorithms; Evolutionary computation; Graphical user interfaces; Mathematical analysis; Mathematical models; Monitoring; Optimization; Performance analysis
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/3477.938259

This paper presents an interactive graphical user interface (GUI) based multiobjective evolutionary algorithm (MOEA) toolbox for effective computer-aided multiobjective (MO) optimization. Without the need of aggregating multiple criteria into a compromise function, it incorporates the concept of Pareto's optimality to evolve a family of nondominated solutions distributing along the tradeoffs uniformly. The toolbox is also designed with many useful features such as the goal and priority settings to provide better support for decision-making in MO optimization, dynamic population size that is computed adaptively according to the online discovered Pareto-front, soft/hard goal settings for constraint handlings, multiple goals specification for logical "AND"/"OR" operation, adaptive niching scheme for uniform population distribution, and a useful convergence representation for MO optimization. The MOEA toolbox is freely available for download at http://vlab.ee.nus.edu.sg/-kctan/moea.htm which is ready for immediate use with minimal knowledge needed in evolutionary computing. To use the toolbox, the user merely needs to provide a simple "model" file that specifies the objective function corresponding to his/her particular optimization problem. Other aspects like decision variable settings, optimization process monitoring and graphical results analysis can be performed easily through the embedded GUIs in the toolbox. The effectiveness and applications of the toolbox are illustrated via the design optimization problem of a practical ill-conditioned distillation system. Performance of the algorithm in MOEA toolbox is also compared with other well-known evolutionary MO optimization methods upon a benchmark problem.