Optimal scheduling of the conventional generating units for two different dynamic test systems are percolated in this paper. This paper compares and contrasts among three types of wind profile formulations, namely lin...
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Optimal scheduling of the conventional generating units for two different dynamic test systems are percolated in this paper. This paper compares and contrasts among three types of wind profile formulations, namely linear, quadratic, and cubic, which were used to calculate wind power from hourly wind speed to find the profile with the greatest penetration of wind power. Thereafter, the wind profiles were coupled with the test system to execute dynamic economic dispatch. The optimization tool used for the study was a unique hybrid algorithm modelled by combining the properties of the recently developed crow search algorithm (CSA) and JAYA. Involvement of ramp-rate limits and the valve point effects magnified the practicality of the work thereby assessing the proposed hybrid algorithm in solving complex non-linear functions. Results infer that maximum level of wind penetration was attained by linear wind profile and a fuel cost reduction of 2.92% was realized upon incorporation of the same. Numerical results also claims that proposed hybrid CSAJAYA approach consistently yielded better quality solutions within minimum execution time without being affected by the dimension of the problem, thereby outperforming a long list of algorithms implemented for the study.
The integration and promotion of intelligent technology in the education industry has led to the introduction of more and more intelligent assistive teaching tools and platforms into the classroom, and the traditional...
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In this paper, the problem of speed tracing for automatic train operation is studied. A new Intelligent-PID controller is proposed in which four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimiza...
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In this paper, the problem of speed tracing for automatic train operation is studied. A new Intelligent-PID controller is proposed in which four optimization algorithms: Genetic Algorithm (GA), Particle Swarm optimization (PSO), Differential Evolution (DE), and Imperium Colony Algorithm (ICA) for the best parameter tuning with the integration of a novel switching function are used. The algorithms are analyzed and specialized for different driving modes including: acceleration, cruising, braking and speed profile shift. By the use of a switch, the PID controller is tuned according to the best algorithm. The switching action is done through a slight change from the current position to the best values by transient values determined by the other algorithm outputs. The simulation results indicate the excellence of the proposed method. The performance of the suggested structure is compared with a single-mode optimization algorithm without use of the switch. The results of the comparison show that the proposed method can track the trajectory on all driving modes with very high accuracy.
Pile settlement (SP) socketed to rock has taken vital regard. Despite introducing some design methods to measure SP, applying the novel and efficient prediction model with satisfactory performance is pivotal. The main...
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Pile settlement (SP) socketed to rock has taken vital regard. Despite introducing some design methods to measure SP, applying the novel and efficient prediction model with satisfactory performance is pivotal. The main goal of this study is to find out the applicability of applying two hybrid multi-layer perceptron neural network (MLP) models in predicting the SP in the Klang Valley Mass Rapid Transit (KVMRT) project constructed operated in Kuala Lumpur, Malaysia. Various hidden layers of models were examined to have comprehensive, accurate and reliable outputs. Ant lion optimizer (ALO) and grasshopper optimization algorithm (GOA) was applied to identify each hidden layer's optimal number of neurons. In this case, five parameters were considered as input variables and SP as output. Regarding ALO-MLP models, ALO-MLP1 has the lowest score (48), with R-2 stood at 0.9382 and 0.93, and PI at 0.0416 and 0.0494 for the training and testing phases, respectively. In the training phase, best values of R-2 , RMSE and PI were belonged to MLP1, while MLP2 has the smallest value of MAE. However, in the testing phase, MLP model with two hidden layer has best values for all indices, which makes it the proposed MLP model with two hidden layers. The results show that ALO is more capable than GOA for determining the optimal neuron numbers of MLP. By summation of the ranking scores obtained from performance evaluation indices, although GOA-MLP models have acceptable performance, two layers of MLP optimized with ALO could be recognized as the proposed model.
As more processing cores are added to embedded systems processors, the relationships between cores and memories have more influence on the energy consumption of the processor. In this paper, we conduct fundamental res...
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As more processing cores are added to embedded systems processors, the relationships between cores and memories have more influence on the energy consumption of the processor. In this paper, we conduct fundamental research to explore the effects of memory sharing on energy in a multicore processor. We study the Memory Arrangement (MA) Problem. We prove that the general case of MA is NP-complete. We present an optimal algorithm for solving linear MA and optimal and heuristic algorithms for solving rectangular MA. On average, we can produce arrangements that consume 49% less energy than an all shared memory arrangement and 14% less energy than an all private memory arrangement for randomly generated instances. For DSP benchmarks, we can produce arrangements that, on average, consume 20% less energy than an all shared memory arrangement and 27% less energy than an all private memory arrangement.
We consider gradient descent and quasi-Newton algorithms to optimize the full configuration interaction (FCI) ground state wavefunction starting from an arbitrary reference state |0. We show that the energies obtained...
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We consider gradient descent and quasi-Newton algorithms to optimize the full configuration interaction (FCI) ground state wavefunction starting from an arbitrary reference state |0. We show that the energies obtained along the optimization path can be evaluated in terms of expectation values of |0, thus avoiding explicit storage of intermediate wavefunctions. This allows us to find the energies after the first few steps of the FCI algorithm for systems much larger than what standard deterministic FCI codes can handle at present. We show an application of the algorithm with reference wavefunctions constructed as linear combinations of non-orthogonal determinants.
This paper studies the application of proper orthogonal decomposition (POD) to reduce the order of distributed reactor models with axial and radial diffusion and the implementation of model predictive control (MPC) ba...
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This paper studies the application of proper orthogonal decomposition (POD) to reduce the order of distributed reactor models with axial and radial diffusion and the implementation of model predictive control (MPC) based on discrete-time linear time invariant (LTI) reduced-ordermodels. In this paper, the control objective is to keep the operation of the reactor at a desired operating condition in spite of the disturbances in the feed flow. This operating condition is determined by means of an optimization algorithm that provides the optimal temperature and concentration profiles for the system. Around these optimal profiles, the nonlinear partial differential equations (PDEs), that model the reactor are linearized, and afterwards the linear PDEs are discretized in space giving as a result a high-order linear model. POD and Galerkin projection are used to derive the low-order linear model that captures the dominant dynamics of the PDEs, which are subsequently used for controller design. An MPC formulation is constructed on the basis of the low-order linear model. The proposed approach is tested through simulation, and it is shown that the results are good with regard to keep the operation of the reactor.
The notion of fitness landscape (FL) has shown promise in terms of optimization. In this paper we propose a machine learning (ML) prediction approach to quantify FL ruggedness by computing the entropy. The approach ai...
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The notion of fitness landscape (FL) has shown promise in terms of optimization. In this paper we propose a machine learning (ML) prediction approach to quantify FL ruggedness by computing the entropy. The approach aims to build a model that could reveal information about the ruggedness of unseen instances. Its contribution is attractive in many cases like black-box optimization and in case we can rely on the information of small instances to discover the features of larger and timeconsuming ones. The experiment consists in evaluating multiple ML models for the prediction of the ruggedness of the traveling salesman problem (TSP). The results show that ML can provide, for instances of a similar problem, acceptable predictions and that it can help to estimate ruggedness of large instances in that case. However, the inclusion of several features is necessary to have a more predictable landscape, especially when dealing with different TSP instances.
It is demonstrated that substantial savings in the computer storage space and calculation can be effected by a close inspection of the subsearch phase of a multivariate optimization algorithm. This is particularly imp...
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It is demonstrated that substantial savings in the computer storage space and calculation can be effected by a close inspection of the subsearch phase of a multivariate optimization algorithm. This is particularly important in implementing algorithms on small computers. A significant compaction of the golden section search is developed. Coincidentally, it is found that the actual bracketing and convergence properties are better than those conventionally used in the literature.
1. IntroductionThe conjugate gradient (CG) method has played a special role for solving large-scale nonlinear optimization due to the simplicity of their iteration and their very low memory requirements. In fact, the ...
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1. IntroductionThe conjugate gradient (CG) method has played a special role for solving large-scale nonlinear optimization due to the simplicity of their iteration and their very low memory requirements. In fact, the CG method is not among the fastest or more robust optimization algorithms for nonlinear problems available today,but it remains very popular for engineers and mathematicians who are interested in solving large problems [16,17].
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