A multiobjective evolutionary algorithm toolbox for computer-aided multiobjective optimization

This paper presents an interactive graphical user interface (GUI) based mul tiobjective evolutionary algorithm (MOEA) toolbox for effective computer-ai ded multiobjective (MO) optimization. Without the need of aggregating multi ple criteria into a compromise function, it incorporates the concept of Par eto's optimality to evolve a family of nondominated solutions distributing along the tradeoffs uniformly. The toolbox is also designed with many usefu l features such as the goal and priority settings to provide better support for decision-making in MO optimization, dynamic population size that is co mputed adaptively according to the online discovered Pareto-front, soft/har d goal settings for constraint handlings, multiple goals specification for logical "AND"/"OR" operation, adaptive niching scheme for uniform populatio n distribution, and a useful convergence representation for MO optimization . The MOEA toolbox is freely available for download at http://vlab.ee.nus.e du.sg/similar to kctan/moea.htm, which is ready for immediate use with mini mal knowledge needed in evolutionary computing. To use the toolbox, the use r merely needs to provide a simple "model" rile that specifies the objectiv e function corresponding to his/her particular optimization problem. Other aspects like decision variable settings, optimization process monitoring an d graphical results analysis can be performed easily through the embedded G UIs in the toolbox. The effectiveness and applications of the toolbox are i llustrated via the design optimization problem of a practical ill-condition ed distillation system. Performance of the algorithm in MOEA toolbox is als o compared with other well-known evolutionary MO optimization methods upon a benchmark problem.