4,040 research outputs found
A Coordinate-Descent Algorithm for Tracking Solutions in Time-Varying Optimal Power Flows
Consider a polynomial optimisation problem, whose instances vary continuously
over time. We propose to use a coordinate-descent algorithm for solving such
time-varying optimisation problems. In particular, we focus on relaxations of
transmission-constrained problems in power systems.
On the example of the alternating-current optimal power flows (ACOPF), we
bound the difference between the current approximate optimal cost generated by
our algorithm and the optimal cost for a relaxation using the most recent data
from above by a function of the properties of the instance and the rate of
change to the instance over time. We also bound the number of floating-point
operations that need to be performed between two updates in order to guarantee
the error is bounded from above by a given constant
Discovering an active subspace in a single-diode solar cell model
Predictions from science and engineering models depend on the values of the
model's input parameters. As the number of parameters increases, algorithmic
parameter studies like optimization or uncertainty quantification require many
more model evaluations. One way to combat this curse of dimensionality is to
seek an alternative parameterization with fewer variables that produces
comparable predictions. The active subspace is a low-dimensional linear
subspace defined by important directions in the model's input space; input
perturbations along these directions change the model's prediction more, on
average, than perturbations orthogonal to the important directions. We describe
a method for checking if a model admits an exploitable active subspace, and we
apply this method to a single-diode solar cell model with five input
parameters. We find that the maximum power of the solar cell has a dominant
one-dimensional active subspace, which enables us to perform thorough parameter
studies in one dimension instead of five
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Applying multiobjective evolutionary algorithms in industrial projects
During the recent years, multiobjective evolutionary algorithms have matured as a flexible optimization tool which can be used in various areas of reallife applications. Practical experiences showed that typically the algorithms need an essential adaptation to the specific problem for a successful application. Considering these requirements, we discuss various issues of the design and application of multiobjective evolutionary algorithms to real-life optimization problems. In particular, questions on problem-specific data structures and evolutionary operators and the determination of method parameters are treated. As a major issue, the handling of infeasible intermediate solutions is pointed out. Three application examples in the areas of constrained global optimization (electronic circuit design), semi-infinite programming (design centering problems), and discrete optimization (project scheduling) are discussed
A Review of Bayesian Methods in Electronic Design Automation
The utilization of Bayesian methods has been widely acknowledged as a viable
solution for tackling various challenges in electronic integrated circuit (IC)
design under stochastic process variation, including circuit performance
modeling, yield/failure rate estimation, and circuit optimization. As the
post-Moore era brings about new technologies (such as silicon photonics and
quantum circuits), many of the associated issues there are similar to those
encountered in electronic IC design and can be addressed using Bayesian
methods. Motivated by this observation, we present a comprehensive review of
Bayesian methods in electronic design automation (EDA). By doing so, we hope to
equip researchers and designers with the ability to apply Bayesian methods in
solving stochastic problems in electronic circuits and beyond.Comment: 24 pages, a draft version. We welcome comments and feedback, which
can be sent to [email protected]
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