Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
WSEAS TRANSACTIONS on SYSTEMS
Developing a custom cluster workflow for shape optimization with finite element analysis
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
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The decision making process in multicriterial optimization problems is in many cases based on the apriori articulation of the compromise integral criterion based on preferences which are frequently of rather intuitive nature. This reduces the comprehensive search for the Pareto front to simpler single-criterion optimization. In this paper, this approach is followed in a specific manner. Instead of the rather arbitrary setting of weight factors in a weighted sum of partial objectives, or compromise selection using the min-max concept related to distance to utopia optima, a lifetime-cost-based integral criterion is used. The GA fitness function is based on the total-lifespan-value aggregate optimality criterion. All nonrecurring investment costs (eg. production costs, mass of material) and recurring operational expenses (maintenance, labour) are discounted accordingly and aggregated into a combined best-compromise fitness metric based on the net present value (NPV) and/or internal rate of return (IRR), which construct the overall fitness function. This approach allows for coupled engineering & financial optimization which approximates real-world decision making.