a wind turbine transformer (wtt) is designed using a 3d wound core while the transformer’s total owning cost (toc) and its inrush current performance realized as the two objective functions in a multi-objective optimization process. multi-objective genetic algorithm is utilized to derive pareto optimal solutions. the effects of inrush current improvement on other operating and design parameters...

Title of Dissertation: ROBUST OPTIMIZATION AND SENSITIVITY ANALYSIS WITH MULTI-OBJECTIVE GENETIC ALGORITHMS: SINGLEAND MULTIDISCIPLINARY APPLICATIONS Mian Li, Doctor of Philosophy, 2007 Dissertation directed by: Shapour Azarm, Professor Department of Mechanical Engineering Uncertainty is inevitable in engineering design optimization and can significantly degrade the performance of an optimized ...

Portfolio optimization is a multi-objective problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio requires trade-off coefficient to be specified in order balance two Erwin Engelbrecht proposed set-based approach single-objective optimization, namely, particle swarm (SBPSO). SBPSO selects sub-set assets that search space for secondary t...

Beam parameter optimization in accelerators involves multiple, sometimes competing, objectives. Condensing these individual objectives into a single figure of merit unavoidably results bias towards particular outcomes, often an undesired way the absence prior knowledge. Finding optimal objective definition then requires operators to iterate over many possible weights and definitions, process th...

In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation Distribution Algorithms (EDA) incorporate methods for automated learning of correlations between variables of the encoded solutions. The process of sampling new individuals from a probabilistic model respects these mutual dependencies such that disruption of importan...

Pareto optimization, that includes the simultaneous optimization of multiple conflict objectives, has been employed as a high level strategy to reduce the effect of local optima (Segura et al. 2013). This approach was first introduced in (Louis and Rawlins 1993), and later reinvestigated and termed as multi-objectivization in (Knowles, Watson, and Corne 2001). Since then it has been studied by ...

Lately the topic of multi-objective transportation network optimization has received increased attention in the research literature. The use of multi-objective transportation network optimization has led to a more accurate and realistic solution in comparison to scenarios where only a single objective is considered. The aim of this work is to identify the most promising multi-objective optimiza...