The Pareto optimization theory is used to tackle the conflicting objectives of simultaneous maximization of the system response and the input entropy rate.
Furthermore, this paper proposes a novel objective reduction approach named IOR to tackle the many-objective optimization model, in which the relationships among the many objectives are quantitatively analyzed and a specific reduced formulation of the original MaOP can be defined via various strategies.
In this article, we develop hybrid algorithms to tackle the bi-objective TSCP.
To further investigate its performance, the MGSO-ACL is employed to tackle the many-objective optimization model in this paper.
A particle swarm optimization (PSO -simulation based aPSO -simulationn used to tackle the multi-obasedive optimizapproachoblem.
The proposed multi-objective optimization model tackles the conflicting objectives of the emission reduction while holding-off the economic cost uplift, leading to a set of Pareto optimal solutions.
In order to tackle the first three objectives, a considerable amount of research focusing on autonomous driving have been carried out.
A number of studies can be found in the context of bilevel single objective optimization problems, but not many exist, which tackle the bilevel multi-objective problems.
Due to the inherent nature of gradient method, the proposed method is very flexible and it can tackle the designs with composite objectives, which are more challenging and often intractable for the existing methods.
The multi-objective optimization algorithm through fuzzy logic therefore, is used to tackle the optimization problem between the objective parameters (core losses and magnetic flux density) and the airgap diameter which define the machine geometry (e.g. slot and tooth dimensions, airgap thickness, core length etc).
The model is based on fuzzy multi objective decision making in order to simultaneously optimize multiple economic objectives and tackle the inherent uncertainties in the parameters and decision makers' aspiration levels for the goals.
