This paper examines restart strategies for algorithms whose performance and runtime depends on a parameter λ. After each restart, λ is incremented, until the algorithm terminates successfully. It is assumed that the...
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Two simple yet powerful optimization algorithms, named the Best-Mean-Random (BMR) and Best-Worst-Random (BWR) algorithms, are developed and presented in this paper to handle both constrained and unconstrained optimiza...
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Numerous optimization algorithms have a time-varying update rule thanks to, for instance, a changing step size, momentum parameter or, Hessian approximation. In this paper, we apply unrolled or automatic differentiati...
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The model predictive control problem of linear systems with integer inputs results in an integer optimization problem. In case of a quadratic objective function, the optimization problem can be cast as an integer leas...
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ISBN:
(纸本)9781479978878
The model predictive control problem of linear systems with integer inputs results in an integer optimization problem. In case of a quadratic objective function, the optimization problem can be cast as an integer least-squares (ILS) problem. Three algorithms to solve this problem are proposed in this paper. Optimality can be traded in to reduce the computation time. An industrial case study - an inverterdriven electrical drive system - is considered to demonstrate the effectiveness of the presented techniques.
The reconfigurable intelligent surface is an emerging technology for wireless communications. We model it as an inhomogeneous boundary of surface impedance, and consider various optimization problems that offer differ...
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We study the connections between ordinary differential equations and optimization algorithms in a non-Euclidean setting. We propose a novel accelerated algorithm for minimising convex functions over a convex constrain...
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To solve dynamic multi-optimization problems, optimization algorithms are required to converge quickly in response to changes in the environment without reducing the diversity of the found solutions. Most Multi-Object...
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ISBN:
(纸本)9781479975617
To solve dynamic multi-optimization problems, optimization algorithms are required to converge quickly in response to changes in the environment without reducing the diversity of the found solutions. Most Multi-Objective Evolutionary algorithms (MOEAs) are designed to solve static multi-objective optimization problems where the environment does not change dynamically. For that reason, the requirement for convergence in static optimization problems is not as time-critical as for dynamic optimization problems. Most MOEAs use generic variables and operators that scale to static multi-objective optimization problems. Problems emerge when the algorithms can not converge fast enough, due to scalability issues introduced by using too generic operators. This paper presents an evolutionary algorithm CONTROLEUM-GA that uses domain specific variables and operators to solve a real dynamic greenhouse climate control problem. The domain specific operators only encode existing knowledge about the environment. A comprehensive comparative study is provided to evaluate the results of applying the CONTROLEUM-GA compared to NSGAII, ε-NSGAII and ε-MOEA. Experimental results demonstrate clear improvements in convergence time without compromising the quality of the found solutions compared to other state-of-art algorithms.
The monitoring of trees and vegetation near high voltage transmission power lines is a tedious job for electrical companies. There are many blackouts occur due to interfering the trees with the power transmission line...
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ISBN:
(纸本)9781479989973
The monitoring of trees and vegetation near high voltage transmission power lines is a tedious job for electrical companies. There are many blackouts occur due to interfering the trees with the power transmission lines in hilly as well as urban areas. This is a big challenge for power distribution companies to monitor the vegetation for avoiding the blackouts and flashovers. To solve these problems, there are many methods are used to monitor the trees and vegetation near transmission power poles. But the existing methods are expensive and time consuming. We proposed the new method based on satellite images to monitor the trees and vegetation. The satellite images provide the cost effective solution to solve the monitoring problem. In this paper, we proposed the stereo matching algorithms to measure the disparity map based on satellite stereo imagery. The height estimation of trees and vegetation near power poles based on depth map which is inversely proportional of the disparity map. For measuring the depth map, the dynamic programming (DP) and block matching with energy minimization has been proposed. These techniques are applied on the satellite stereo images and based on results, our proposed DP algorithm produced more accurate disparity map as compared to the block matching algorithm.
Network-based wind speed forecasting has played an important role in the power system. The network parameters optimization is an important issue, and different optimization algorithms are believed to result in differe...
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Network-based wind speed forecasting has played an important role in the power system. The network parameters optimization is an important issue, and different optimization algorithms are believed to result in different forecasting accuracies. In this paper, six network parameters optimization algorithms, including Gradient descent, Momentum, Ada Grad, RMSprop, Adam, and Adadelta, are implemented and compared in the application of wind speed forecasting. As a case study, this paper uses a wind speed data obtained from Ningxia, China. The performance is evaluated by three metrics, namely, mean absolute error（MAE）, root mean square error（RMSE）, and mean absolute percentage error（MAPE）. The experiment results show that, Adam algorithm and RMSprop algorithm achieve better forecasting accuracy and less training time than the other optimization algorithms. This study can be a guide to the selection of optimization algorithms on wind speed forecasting problems for researchers.
optimization problems are crucial in artificial *** algorithms are generally used to adjust the performance of artificial intelligence models to minimize the error of mapping inputs to *** evaluation methods on optimi...
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optimization problems are crucial in artificial *** algorithms are generally used to adjust the performance of artificial intelligence models to minimize the error of mapping inputs to *** evaluation methods on optimization algorithms generally consider the performance in terms of ***,not all optimization algorithms for all test cases are evaluated equal from quality,the computation speed should be also considered for optimization *** this paper,we investigate the quality and speed of optimization algorithms in optimization problems,instead of the one-for-all evaluation of *** select the well-known optimization algorithms(Bayesian optimization and evolutionary algorithms) and evaluate them on the benchmark test functions in terms of quality and *** results show that BO is suitable to be applied in the optimization tasks that are needed to obtain desired quality in the limited function evaluations,and the EAs are suitable to search the optimal of the tasks that are allowed to find the optimal solution with enough function *** paper provides the recommendation to select suitable optimization algorithms for optimization problems with different numbers of function evaluations,which contributes to the efficiency that obtains the desired quality with faster speed for optimization problems.
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