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An economical cognitive approach for bi-objective optimization using bliss points, visualization, and interaction

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Abstract

When an optimization problem encompasses multiple objectives, it is usually difficult to define a single optimal solution. The decision maker plays an important role when choosing the final single decision. Pareto-based evolutionary multiobjective optimization (EMO) methods are very informative for the decision making process since they provide the decision maker with a set of efficient solutions to choose from. Despite that the set of efficient solutions may not be the global efficient set, we show in this paper that the set can still be informative when used in an interactive session with the decision maker. We use a combination of EMO and single objective optimization methods to guide the decision maker in interactive sessions.

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Authors and Affiliations

  1. Artificial Life and Adaptive Robotics Laboratory, School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT, 2600, Australia

    Hussein A. Abbass

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  1. Hussein A. Abbass
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Correspondence to Hussein A. Abbass.

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Abbass, H. An economical cognitive approach for bi-objective optimization using bliss points, visualization, and interaction. Soft Comput 10, 687–698 (2006). https://doi.org/10.1007/s00500-005-0530-8

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