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In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain.
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The in-plane strength around a circular hole in a finite-width plate with Tsai-Wu failure criteria or the fundamental frequency of the square plate is the mechanical objective function, while the average value of fiber curvatures is used as the conflicting objective function.
The Pareto optimization theory is used to tackle the conflicting objectives of simultaneous maximization of the system response and the input entropy rate.
Therefore, this research uses a genetic algorithm based multi-objective optimization (MOO) approach to yield a set of Pareto solutions that can be used to find the most practical tradeoffs between the conflicting objectives.
Then, the conflicting objectives of the model are analyzed using trade-off analysis to understand conflicting aspect among objectives.
In order to design alloys with the conflicting objectives of high strength and adequate ductility, Multi-Objective Genetic Algorithm (MOGA) is used to search optimum solutions using the ANN models as the objective functions.
The trade-off between the conflicting objectives is analysed by solving the proposed multi-objective model using the ɛ-constraint method.
Then the models are used as objective functions for the multi-objective genetic algorithms for evolving the tradeoffs between the conflicting objectives of achieving improved strength, ductility and impact toughness.
By using IMO methods, a decision maker progressively provides preference information in order to find the most satisfactory compromise between the conflicting objectives.
This paper proposes a novel metrics approach to achieve the conflicting objectives with a maintenance focus.
The conflicting objectives are considered to be negatively correlated mathematically.
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