Abstract
This chapter aims at presenting the general problem of decision making in unknown, complex or changing environment by an extension of static multiobjective optimization problem. General optimization problem is defined, which encompasses not just dynamics, but also change in the optimization problem itself, with focus on changing number of objectives used to evaluate potential solutions.
In order to solve the defined problem, a variant of multi-objective genetic algorithm was used. Since the chapter doesn’t focus on the performance of the algorithm used for solving the problem, but tends to demonstrate the approach, experimental results produced by tests with MOGA are presented. These experimental results clearly demonstrate, that MOGA successfully led the population of potential solutions to the problem for different test cases, such as homogenous, non-homogenous, and the problem with changing number of objectives. Decision-making based on ranking of the potential solutions has also been demonstrated.
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Avdagić, Z., Konjicija, S., Omanović, S. (2009). Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_9
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DOI: https://doi.org/10.1007/978-3-642-01085-9_9
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