Selection Of Fitness Function Research Articles

Application of Auto-Regulative Sparse Variational Mode Decomposition in Mechanical Fault Diagnosis

The variational mode decomposition (VMD) method has been widely applied in the field of mechanical fault diagnosis as an excellent non-recursive signal processing tool. The performance of VMD depends on its inherent prior parameters. Searching for the key parameters of VMD using intelligent optimization algorithms poses challenges for the internal essence and fitness function selection of intelligent optimization algorithm. Moreover, the computational complexity of optimization is high. Meanwhile, such methods are not competitive in evaluating orthogonality between intrinsic mode functions and the reconstruction error of the signal as a joint indictor for the termination of decomposition. Therefore, this paper proposes a new auto-regulative sparse variational mode decomposition method (ASparse–VMD) to achieve accurate feature extraction. The regularization term of the VMD handles sparsification by constructing an L2-norm with a damping coefficient ε, and mode number K is set adaptively in a recursive manner to ensure appropriateness. The penalty parameter α is dynamically selected according to the number of modes and sampling frequency. The update step τ of the VMD algorithm is set using the signal-to-noise ratio to ensure the singleness and orthogonality of the modal components and suppress mode aliasing. The experimental results of the simulation signal and measured signal demonstrate the effectiveness of the proposed strategies for improving the inherent defects of VMD. Extensive comparisons with state-of-the-art methods show that the proposed algorithm is more effective and practical for hybrid feature extraction in mechanical faults.α

An Improved Genetic Algorithm for the Uncapacitated Facility Location Problem and Applications in Oil and Gas Fields

Uncapacitated Facility Location Problem (UFLP) is a NP-hard Problem that to determine the optimal Location of facilities. In this paper, the general Genetic Algorithm (gGA) is first introduced and adopted to solve a small case of UFLP (case1). Then an improved genetic algorithm (iGA) based on real coding is proposed to solve the UFLP problem. This paper mainly makes appropriate adjustments in the selection of fitness function, crossover operator and mutation operator to be more suitable for UFLP. Facilities thus can be roughly allocated according to the cost of facilities and the demands of customers. Case2 was calculated by several CAP data (CAP101, CAP103, CAP104, CAP131, CAP133, CAPB and CAPC) in OR-LIBRORY. The results proved that the iGA is feasible and effective, and it is found that 80% of the results obtained by the iGA are within 0.05% of the optimal solutions. Compared with several other common algorithms, the advantages of iGA increase as the scale of calculation increases. Finally, applying iGA to an on-site oilfield pipeline network, it was found that it can find the optimal solution in a short time.

Learning how to search: generating effective test cases through adaptive fitness function selection

Search-based test generation is guided by feedback from one or more fitness functions—scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals—such as forcing the class-under-test to throw exceptions, increasing test suite diversity, and attaining Strong Mutation Coverage—do not have effective fitness function formulations. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current population of test suites. To test this hypothesis, we have implemented two reinforcement learning algorithms in the EvoSuite unit test generation framework, and used these algorithms to dynamically set the fitness functions used during generation for the three goals identified above. We have evaluated our framework, EvoSuiteFIT, on a set of Java case examples. EvoSuiteFIT techniques attain significant improvements for two of the three goals, and show limited improvements on the third when the number of generations of evolution is fixed. Additionally, for two of the three goals, EvoSuiteFIT detects faults missed by the other techniques. The ability to adjust fitness functions allows strategic choices that efficiently produce more effective test suites, and examining these choices offers insight into how to attain our testing goals. We find that adaptive fitness function selection is a powerful technique to apply when an effective fitness function does not already exist for achieving a testing goal.

Retracted] Optimization Strategy of IoT Sensor Configuration Based on Genetic Algorithm‐Neural Network

This article carries out the overall design framework of the IoT sensor data processing platform and analyzes the advantages of using the integrated construction platform. The platform is divided into two parts, a web management platform and a data communication system, and interacts with the database by integrating the business layers of the two into one. The web management platform provides configurable communication protocol customization services, equipment information, personal information, announcement information management services, and data collection information monitoring and analysis services. The collected data is analyzed by the sensor data communication service system and then provided to the web management platform for query and call. This paper discusses the theoretical basis of the combination of genetic algorithm and neural network and proposes the necessity of improving genetic algorithm. The improved level involves chromosome coding methods, fitness function selection, and genetic manipulation. We propose an improved genetic algorithm and use an improved genetic algorithm (IGA) to optimize the neural network structure. The finite element method is adopted, the finite element model is established, and the shock piezoelectric response is numerically simulated. The genetic neural network method is used to simulate the collision damage location detection problem. The piezoelectric sensor is optimized, and the optimal sensor configuration corresponding to its initial layout is obtained, which provides guidance for the optimal configuration of the actual piezoelectric sensor.

Energy-Efficient Cooperative Spectrum Sensing Strategy for Cognitive Wireless Sensor Networks Based on Particle Swarm Optimization

Cognitive wireless sensor networks (CWSNs) can use the idle authorized frequency band to solve the problem of spectrum resource shortage in traditional wireless sensor network. By employing spectrum hole in the authorized frequency band, the spectrum sensing technology can degrade the coexistent interference and enhance the performance of whole sensor network. Due to the characteristics of limited battery energy and low processing capacity with sensor nodes, it is necessary to enhance the energy efficiency while improving spectrum sensing performance. In this paper, a cooperative spectrum sensing strategy for CWSNs based on particle swarm optimization is proposed. Firstly, the system throughput and energy consumption are quantitatively analyzed, and the mathematical model related to energy efficiency is established. Secondly, the particle swarm optimization (PSO) algorithm is used to obtain the optimal selected nodes set under the limited conditions of false alarm probability and detection probability. To avoid local optimization in the process of problem solving, Cauchy mutation method is introduced to optimize the parameter selection of fitness function. The experimental results illustrate that our proposed method can improve the throughput of the system while ensuring the sensing performance, and achieve the energy efficiency effectively.

Dynamic selection of fitness function for software change prediction using Particle Swarm Optimization

Abstract Context Over the past few years, researchers have been actively searching for an effective classifier which correctly predicts change prone classes. Though, few researchers have ascertained the predictive capability of search-based algorithms in this domain, their effectiveness is highly dependent on the selection of an optimum fitness function. The criteria for selecting one fitness function over the other is the improved predictive capability of the developed model on the entire dataset. However, it may be the case that various subsets of instances of a dataset may give best results with a different fitness function. Objective The aim of this study is to choose the best fitness function for each instance rather than the entire dataset so as to create models which correctly ascertain the change prone nature of majority of instances. Therefore, we propose a novel framework for the adaptive selection of a dynamic optimum fitness function for each instance of the dataset, which would correctly determine its change prone nature. Method The predictive models in this study are developed using seven different fitness variants of Particle Swarm Optimization (PSO) algorithm. The proposed framework predicts the best suited fitness variant amongst the seven investigated fitness variants on the basis of structural characteristics of a corresponding instance. Results The results of the study are empirically validated on fifteen datasets collected from popular open-source software. The proposed adaptive framework was found efficient in determination of change prone classes as it yielded improved results when compared with models developed using individual fitness variants and fitness-based voting ensemble classifiers. Conclusion The performance of the models developed using the proposed adaptive framework were statistically better than the models developed using individual fitness variants of PSO algorithm and competent to models developed using machine learning ensemble classifiers.

Fitness functions for evolutionary optimization of rate parameters in chemically reacting systems

Evolutionary optimization studies are mainly focused on the type of methodologies and controlling parameters of the algorithms. However, the choice of fitness functions which gives a measure on how well a dataset correlates with reference values is often not justified in a rigorous way. The work in this paper addresses this issue by investigating the performance of a set of fitness functions and their suitability according to the input data used for training the optimization algorithms. A Genetic Algorithm was applied to a chemical kinetics problem with the aim of matching the measured species concentrations through optimization of a set of Arrhenius reaction rate parameters. The challenge with this type of problem lies in the wide range of species concentrations used as the training dataset, where they can be up to fifteen orders in magnitude. This revealed large differences in optimized values arising from the various fitness function formulations, thus highlighting the need for detailed knowledge of fitness functions for a specific optimization problem. Furthermore, the quality of training dataset through error propagation and a scaling parameter were assessed bringing further insight on differences fitness functions. Such information is crucial for the appropriate selection of fitness functions for the effective optimisation of reaction rate parameters.

Particle swarm clustering fitness evaluation with computational centroids

Abstract In this paper, we propose a new way to carry out fitness evaluation in dynamic Particle Swarm Clustering (PSC) with centroid-based encoding. Generally, the PSC fitness function is selected among the clustering validity indices and most of them directly depend on the cluster centroids. In the traditional fitness evaluation approach, the cluster centroids are replaced by the centroids proposed by a particle position. We propose to first compute the centroids of the corresponding clusters and then use these computational centroids in fitness evaluation. The proposed way is called Fitness Evaluation with Computational Centroids (FECC). We conducted an extensive set of comparative evaluations and the results show that FECC leads to a clear improvement in clustering results compared to the traditional fitness evaluation approach with most of the fitness functions considered in this study. The proposed approach was found especially beneficial when underclustering is a problem. Furthermore, we evaluated 31 fitness functions based on 17 clustering validity indices using two PSC methods over a large number of synthetic and real data sets with varying properties. We used three different performance criteria to evaluate the clustering quality and found out that the top three fitness functions are Xu index, WB index, and Dunn variant DU 23 applied using FECC. These fitness functions consistently performed well for both PSC methods, for all data distributions, and according to all performance criteria. In all test cases, they were clearly among the better half of the fitness functions and, in the majority of the cases, they were among the top 4 functions. Further guidance for improved fitness function selection in different situations is provided in the paper.

Automatic fitness function selection for compartment model optimization

During normal aging, layer 3 pyramidal neurons of the rhesus monkey prefrontal cortex (PFC) exhibit significant morphological changes, as well as higher action potential firing rates in vitro [1]. Computational modeling of individual neurons can provide insight into the ionic mechanisms underlying the increased excitability, which are currently unknown. A unique database of electrophysiological recordings and morphologic reconstructions from the same neurons, gathered through whole-cell patch clamp recording, confocal microscopy and 3D digital tracing, constrains the models. Initial modeling of six young and six aged neurons demonstrated that morphological features alone do not account entirely for the electrophysiological changes with aging [2]. It is now necessary to explore the parameter space of passive cable properties and active membrane channel conductances and kinetics, to uncover parameter combinations that reproduce the firing patterns observed in neurons of each age group. Differential Evolution (DE) is an evolutionary optimization method capable of identifying a population of candidate models throughout parameter space that closely match empirically observed firing patterns. The quality of fit achieved by an optimization is reliant on the ‘fitness functions’ used to measure the accuracy of the model. Previous neuronal compartment modeling studies using parameter optimization have introduced multiple types of fitness measurement [3], but have not described a general method to determine weights for each type. Here we introduce a novel method for automatically establishing weights of minimally correlated fitness functions, and apply it to optimization of models of young and aged PFC neurons. First, a Latin hypercube design (< 1000 points) provides a space-filling sampling of parameter space; the candidate fitness functions are then evaluated at each of these points. Second, clusters of fitness functions that are highly correlated across the hypercube are pruned to leave one representative member. Third, a principal component analysis of the remaining fitness functions across the hypercube identifies a set of fitness functions representing most of the variability in the parameter space, which are selected for use in the optimization. Fourth, weights for each selected fitness function are calculated based on the combination of coefficients for principal components and variance explained by those components. Finally, DE is conducted on the Neuroscience Gateway [4] using this automatically constructed optimization protocol. We demonstrate the method with a compartment model comprising a simplified pyramidal neuron morphology and three ion channels, optimized to data from representative young and aged neurons. Compared to a manual approach involving iterative generation of fitness functions, our novel method produces better fitting models using a tenth of the computation time. Future work will extend the automatic protocol generation to prioritize which parameters to optimize, a critical step as more ion channels are added to the model to improve fitness. This method will be used to generate morphologically detailed models of 20+ young and aged PFC neurons, predicting which ionic mechanisms underlie age-related physiological changes.

Image thresholding based on evolutionary algorithms

The objective of this paper is to propose an adaptive-evolutionary method for thresholding which is used as an artificial intelligent algorithm for image segmentation especially for object segmentation. This method employs resistant versus mixed histograms because of its suitable fitness function selection that consists of the histogram details. As things develop in the paper, three evolutionary methods known as genetic algorithm (GA), imperial competitive algorithm (ICA) and adaptive particle swarm optimization are used to minimize the error function. Finally, a powerful algorithm for image thresholding is found. The comparisons and experimental results show that this system is better than other methods particularly Otsu’s, GA and even new algorithms like ICA. Key words: Segmentation, adaptive particle swarm optimization (APSO), genetic algorithm (GA), imperialist competitive algorithm (ICA), threshold, fitness function.

Evolution of Fuzzy Controllers for Robotic Vehicles: The Role of Fitness Function Selection

An important issue not addressed in the literature, is related to the selection of the fitness function parameters which are used in the evolution process of fuzzy logic controllers for mobile robot navigation. The majority of the fitness functions used for controllers evolution are empirically selected and (most of times) task specified. This results to controllers which heavily depend on fitness function selection. In this paper we compare three major different types of fitness functions and how they affect the navigation performance of a fuzzy logic controlled real robot. Genetic algorithms are employed to evolve the membership functions of these controllers. Further, an efficiency measure is introduced for the systematic analysis and benchmarking of overall performance. This measure takes into account important performance results of the robot during experimentation, such as the final distance from target, the time needed to reach its final position, the time of sensor activation, the mean linear velocity e.t.c. In order to examine the validity of our approach a low cost mobile robot has been developed, which is used as a testbed.

A comparison of fitness functions for the identification of a piezoelectric hysteretic actuator based on the real-coded genetic algorithm

The main objective of this study is to identify the piezoelectric actuator (PA) with hysteresis, which is described by Bouc-Wen model. The identification method based on real-coded genetic algorithm (RGA) has the advantages not only to identify the parameters of the hysteresis model but also the mass, stiffness, damping coefficient and piezoelectric coefficient of the PA simultaneously. Furthermore, the selection of fitness functions in the RGA is the key point to influence the final identified results. In this paper, three types of fitness functions are designed to identify the hysteretic PA and their differences are discussed. The numerical simulations and experimental results demonstrate that the identification method based on the RGA with suitable fitness functions is feasible.