In this work, considering the solution of analog filter approximation problem, evolutionary algorithms are used to obtain nth order transfer functions. Coefficients of denominator polynomial of low pass analog filter ...
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ISBN:
(纸本)9781479948741
In this work, considering the solution of analog filter approximation problem, evolutionary algorithms are used to obtain nth order transfer functions. Coefficients of denominator polynomial of low pass analog filter are optimized and this process is carried out for three different orders of transfer functions. Simulation results show that error values obtained with evolutionary algorithms are less than that of traditional methods. The feasibility of the proposed method on circuit realization is investigated by designing passive and active analog filter circuits which realize 3rd order transfer function.
In this work, the denominator coefficients of a low-pass filter transfer function are optimized with evolutionary algorithms in order to obtain minimum approximation error and to reduce the distortion over the passban...
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ISBN:
(纸本)9781479930203
In this work, the denominator coefficients of a low-pass filter transfer function are optimized with evolutionary algorithms in order to obtain minimum approximation error and to reduce the distortion over the passband and stopband separately. For each design case, three different orders of transfer function are optimized. Simulation results show that evolutionary algorithms used in this work results in a short computation time with less approximation error than the conventional methods. Passive and active circuit realizations of filter transfer functions obtained with the most efficient EA method are also provided in order to show the feasibility of the proposed approach for circuit implementation.
In a preference-based multi-objective optimization task, the goal is to find a subset of the Pareto-optimal set close to a supplied set of aspiration points. The reference point based non-dominated sorting genetic alg...
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In a preference-based multi-objective optimization task, the goal is to find a subset of the Pareto-optimal set close to a supplied set of aspiration points. The reference point based non-dominated sorting genetic algorithm (R-NSGA-II) was proposed for such problem-solving tasks. R-NSGA-II aims to finding Pareto-optimal points close, in the sense of Euclidean distance in the objective space, to the supplied aspiration points, instead of finding the entire Pareto-optimal set. In this paper, R-NSGA-II method is modified using recently proposed Karush-Kuhn-Tucker proximity measure (KKTPM) and achievement scalarization function (ASF) metrics, instead of Euclidean distance metric. While a distance measure may not produce desired solutions, KKTPM-based distance measure allows a theoretically-convergent local or global Pareto solutions satisfying KKT optimality conditions and the ASF measure allows Pareto-compliant solutions to be found. A new technique for calculating KKTPM measure of a solution in the presence of an aspiration point is developed in this paper. The proposed modified R-NSGA-II methods are able to solve as many as 10-objective problems as effectively or better than the existing R-NSGA-II algorithm.
The main goal of this flight control system is to achieve good performance with acceptable flying quality within the specified flight envelope while ensuring robustness for model variations, such as mass variation due...
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Wood-plastic composites (WPCs) have emerged as a sustainable and cost-effective material for construction, particularly in low-cost housing solutions. However, designing WPC panels that meet structural, serviceability...
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Wood-plastic composites (WPCs) have emerged as a sustainable and cost-effective material for construction, particularly in low-cost housing solutions. However, designing WPC panels that meet structural, serviceability, and manufacturing constraints remains a challenge. This study focused on optimizing the cross-sectional shape of WPC roof panels using evolutionary algorithms to minimize material usage while ensuring compliance with deflection and stress constraints. Two evolutionary algorithms-the genetic algorithm (GA) and particle swarm optimization (PSO)-were employed to optimize sinusoidal and trapezoidal panel profiles. The optimization framework integrated finite element analysis (FEA) to evaluate structural performance under uniformly distributed loads and self-weight. The modulus of elasticity of the WPC material was determined experimentally through three-point bending tests, ensuring accurate material representation in the simulations. The trapezoidal profile proved to be the most optimal, exhibiting superior deflection performance compared with the sinusoidal profile. A comparative analysis of GA and PSO revealed that PSO outperformed GA in both solution optimality and convergence speed, demonstrating its superior efficiency in navigating the design space and identifying high-performance solutions. The findings highlight the potential of WPCs in low-cost housing applications and offer insights into the selection of optimization algorithms for similar engineering design problems.
evolutionary algorithms have emerged as powerful tools for optimization. However, striking a balance between convergence and diversity in many-objective optimization remains a significant challenge. To address this ga...
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evolutionary algorithms have emerged as powerful tools for optimization. However, striking a balance between convergence and diversity in many-objective optimization remains a significant challenge. To address this gap, we propose TSEA-OTN, an objective transformation-based and niche-based many-objective evolutionary algorithm with a two-step coordination mechanism. Uniquely, TSEA-OTN operates without relying on relaxed Pareto dominance, reference vectors, or additional indicators. Instead, it utilizes prior knowledge about the curvature of the PF (Pareto optimal front) to transform the objectives of the population and establish niches. Additionally, a niche-assisted density estimation method is designed to measure the distribution of individual. The environmental selection process incorporates a two-step mechanism: in the former step, the niche-assisted density evaluation method identifies crowded individuals to prioritize diversity;in the latter step, the Euclidean distance among transformed individuals and convergence evaluation criteria are used to eliminate individuals within the same niche for promoting convergence. Finally, TSEA-OTN is evaluated against six state-of-the-art algorithms on DTLZ (Deb-Thiele-Laumanns-Zitzler), MaF (Many-objective function), WGF (Walking Fish Group) benchmark suites, as well as an engineering case study. Experimental results demonstrate the competitive performance of TSEA-OTN in solving many-objective optimization problems. This research not only advances the field of evolutionary computation but also provides novel solutions for real-world optimization.
Electroencephalogram (EEG) plays a significant role in emotion recognition because it contains abundant information. However, due to the highly correlated EEG channels, a lot of redundant EEG features exist, which not...
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Electroencephalogram (EEG) plays a significant role in emotion recognition because it contains abundant information. However, due to the highly correlated EEG channels, a lot of redundant EEG features exist, which not only potentially degrade the emotion recognition accuracy, but also bring high computational costs. To address this challenge, this paper proposes an adaptive matrix-based evolutionary computation framework (AMEC) to select as few informative EEG features as possible for effective emotion recognition. Unlike most existing EC algorithms that utilize vector-based operations, this framework leverages matrix-based operations to reduce feature redundancy and improve classification accuracy by dynamically adjusting the feature subset size according to the characteristics of the dataset. In such a way, the selection efficiency is largely improved. To verify the effectiveness and efficiency of this framework, the classical genetic algorithm, the typical particle swarm optimization algorithm, and the classical differential evolution algorithm, are respectively embedded into this framework for EEG feature selection, and then evaluated on three widely used public EEG datasets for emotion recognition. Compared with several state-of-the-art EEG feature selection algorithms, the devised framework is much more effective in terms of the classification accuracy and the computational efficiency. In addition, the experimental results further reveal that the selected feature subsets are very different for different genders. This indicates the demand of gender-sensitive EEG feature selection for emotion recognition.
The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of t...
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The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multi-sensor placement for indoor surveillance. Our approach is focused on security surveillance of sensible indoor spaces, such as military installations, where distinct security levels can be considered. We propose an evolutionary algorithm to solve the problem, in which a novel special encoding (integer encoding with binary conversion) and effective initialization have been defined to improve the performance and convergence of the proposed algorithm. We also consider the probability of detection for each surveillance point, which depends on the distance to the sensor at hand, to better model real-life scenarios. We have tested the proposed evolutionary approach in different instances of the problem, varying both size and difficulty and obtained excellent results regarding the cost of sensors' placement and convergence time of the algorithm.
This work contributes on how a parameter optimization scheme tackles the system identification problem in type 1 diabetes (T1D) patients to derive a dynamical model with potential application on feedback control schem...
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This work contributes on how a parameter optimization scheme tackles the system identification problem in type 1 diabetes (T1D) patients to derive a dynamical model with potential application on feedback control schemes for blood glucose regulation. That is, the contribution aim is to identify values of system (sensitive) parameters such that a patient-specific model is derived towards the future design of feedback control for assisting T1D therapy. To this end, a differential equation system is proposed to model the blood glucose dynamics in T1D. A challenge in control systems regards the system identification to capture suitable response from available measurements of the blood glucose levels. T1D is particularly interesting due to the glycemia inter-variability. This fact has been recently highlighted because of the continuous glucose monitoring has revealed the need of specificity at glucose dynamics model for every single patient. Hence, a class of artificial intelligence algorithms are performed towards the identification of a control system for the individual glucose metabolism. Here, three AI algorithms perform the system identification towards future implementation. Each one of the three AI algorithms comprises two parts: a physiological model and a parameter optimization scheme to capture the time response from a set of glucose data obtained from measurements. The explored AI algorithms for the parameter optimization are respectively approached via Genetic Algorithm, Particle swarm optimization algorithm, and Taguchi sliding based differential evolution algorithm. A set of patients allows us to explore experimentally the performance of the AI algorithms.
In this work we present a comparison of several Artificial Neural Networks weights initialization methods based on evolutionary algorithms. We have tested these methods on three datasets: KEEL regression problems, ran...
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ISBN:
(纸本)9788897999324
In this work we present a comparison of several Artificial Neural Networks weights initialization methods based on evolutionary algorithms. We have tested these methods on three datasets: KEEL regression problems, random synthetic dataset and a dataset of concentration of different chemical species from the Bioethanol To Olefins process. Results demonstrated that the tuning of neural networks initial weights improves significantly their performance compared to a random initialization. In addition, several crossover algorithms were tested to identify the best one for the present objective. In the post-hoc analysis there were found significant differences between the implemented crossover algorithms when the network has four or more inputs.
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