335,056 research outputs found
Simple Problems: The Simplicial Gluing Structure of Pareto Sets and Pareto Fronts
Quite a few studies on real-world applications of multi-objective
optimization reported that their Pareto sets and Pareto fronts form a
topological simplex. Such a class of problems was recently named the simple
problems, and their Pareto set and Pareto front were observed to have a gluing
structure similar to the faces of a simplex. This paper gives a theoretical
justification for that observation by proving the gluing structure of the
Pareto sets/fronts of subproblems of a simple problem. The simplicity of
standard benchmark problems is studied.Comment: 10 pages, accepted at GECCO'17 as a poster paper (2 pages
Optimal Design of Switched Reluctance Motor Using Genetic Algorithm
Switched reluctance motor (SRM) is gaining more interest in both research and industry. Its simple structure without windings or permanent magnets on the rotor makes the motor robust and reliable with reduced manufacturing cost. The SRM also provides high starting torque and high efficiency over a wide range of speeds, which is strongly desired for electric vehicles’ applications. However, these advantages of switched reluctance motors come with some challenges. Torque ripples, low power density, and temperature rise are common questions about SRM. This paper utilizes multi-objective optimization of SRM design to get most of the SRM desired characteristics with minimization of the machine’s common drawbacks. The optimization process has considered twelve variables and five objective functions. These functions include average torque, efficiency, iron weight, torque-ripples, and maximum temperature rise. The electromagnetic analysis of each candidate is performed by the finite elements method (FEA). The performance indices of SRM are calculated based on FEA analysis results via calculations that compensate for accuracy and computation time. The multi-objective genetic algorithm technique (MOGA) combines the objective functions into a single objective function. Verifying the optimal design comprises generating the efficiency map, torque profile, and dynamic simulation of the motor. This paper mainly focuses on the design and optimization of SRM to fulfill the general requirements of electric vehicle applications
Optimal Design of Switched Reluctance Motor Using Genetic Algorithm
Switched reluctance motor (SRM) is gaining more interest in both research and industry. Its simple structure without windings or permanent magnets on the rotor makes the motor robust and reliable with reduced manufacturing cost. The SRM also provides high starting torque and high efficiency over a wide range of speeds, which is strongly desired for electric vehicles’ applications. However, these advantages of switched reluctance motors come with some challenges. Torque ripples, low power density, and temperature rise are common questions about SRM. This paper utilizes multi-objective optimization of SRM design to get most of the SRM desired characteristics with minimization of the machine’s common drawbacks. The optimization process has considered twelve variables and five objective functions. These functions include average torque, efficiency, iron weight, torque-ripples, and maximum temperature rise. The electromagnetic analysis of each candidate is performed by the finite elements method (FEA). The performance indices of SRM are calculated based on FEA analysis results via calculations that compensate for accuracy and computation time. The multi-objective genetic algorithm technique (MOGA) combines the objective functions into a single objective function. Verifying the optimal design comprises generating the efficiency map, torque profile, and dynamic simulation of the motor. This paper mainly focuses on the design and optimization of SRM to fulfill the general requirements of electric vehicle applications
An Integrated Probability-Based Approach for Multiple Response Surface Optimization
Nearly all real life systems have multiple quality characteristics where individual modeling and optimization approaches can not provide a balanced compromising solution. Since performance, cost, schedule, and consistency remain the basics of any design process, design configurations are expected to meet several conflicting requirements at the same time. Correlation between responses and model parameter uncertainty demands extra scrutiny and prevents practitioners from studying responses in isolation. Like any other multi-objective problem, multi-response optimization problem requires trade-offs and compromises, which in turn makes the available algorithms difficult to generalize for all design problems. Although multiple modeling and optimization approaches have been highly utilized in different industries, and several software applications are available, there is no perfect solution to date and this is likely to remain so in the future. Therefore, problem specific structure, diversity, and the complexity of the available approaches require careful consideration by the quality engineers in their applications
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization
In this paper, we study multi-block min-max bilevel optimization problems,
where the upper level is non-convex strongly-concave minimax objective and the
lower level is a strongly convex objective, and there are multiple blocks of
dual variables and lower level problems. Due to the intertwined multi-block
min-max bilevel structure, the computational cost at each iteration could be
prohibitively high, especially with a large number of blocks. To tackle this
challenge, we present a single-loop randomized stochastic algorithm, which
requires updates for only a constant number of blocks at each iteration. Under
some mild assumptions on the problem, we establish its sample complexity of
for finding an -stationary point. This matches the
optimal complexity for solving stochastic nonconvex optimization under a
general unbiased stochastic oracle model. Moreover, we provide two applications
of the proposed method in multi-task deep AUC (area under ROC curve)
maximization and multi-task deep partial AUC maximization. Experimental results
validate our theory and demonstrate the effectiveness of our method on problems
with hundreds of tasks
Soft and transferable pseudopotentials from multi-objective optimization
Ab initio pseudopotentials are a linchpin of modern molecular and condensed
matter electronic structure calculations. In this work, we employ
multi-objective optimization to maximize pseudopotential softness while
maintaining high accuracy and transferability. To accomplish this, we develop a
formulation in which softness and accuracy are simultaneously maximized, with
accuracy determined by the ability to reproduce all-electron energy differences
between Bravais lattice structures, whereupon the resulting Pareto frontier is
scanned for the softest pseudopotential that provides the desired accuracy in
established transferability tests. We employ an evolutionary algorithm to solve
the multi-objective optimization problem and apply it to generate a
comprehensive table of optimized norm-conserving Vanderbilt (ONCV)
pseudopotentials (https://github.com/SPARC-X/SPMS-psps). We show that the
resulting table is softer than existing tables of comparable accuracy, while
more accurate than tables of comparable softness. The potentials thus afford
the possibility to speed up calculations in a broad range of applications areas
while maintaining high accuracy.Comment: 13 pages, 4 figure
Playing Ping Pong with Light: Directional Emission of White Light
Over the last decades, light-emitting diodes (LED) have replaced common light bulbs in almost every application, from flashlights in smartphones to automotive headlights. Illuminating nightly streets requires LEDs to emit a light spectrum that is perceived as pure white by the human eye. The power associated with such a white light spectrum is not only distributed over the contributing wavelengths but also over the angles of vision. For many applications, the usable light rays are required to exit the LED in forward direction, namely under small angles to the perpendicular. In this work, we demonstrate that a specifically designed multi-layer thin film on top of a white LED increases the power of pure white light emitted in forward direction. Therefore, the deduced multi-objective optimization problem is reformulated via a real-valued physics-guided objective function that represents the hierarchical structure of our engineering problem. Variants of Bayesian optimization are employed to maximize this non-deterministic objective function based on ray tracing simulations. Eventually, the investigation of optical properties of suitable multi-layer thin films allowed to identify the mechanism behind the increased directionality of white light: angle and wavelength selective filtering causes the multi-layer thin film to play ping pong with rays of light
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