Fault feature selection and early fault diagnosis for planetary gearbox are important tasks and have been widely investigate. This paper proposes a novel fault diagnosis scheme for planetary gearbox using multi-criter...
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Fault feature selection and early fault diagnosis for planetary gearbox are important tasks and have been widely investigate. This paper proposes a novel fault diagnosis scheme for planetary gearbox using multi-criteria fault feature selection and heterogeneous ensemble learning classification. Vibration signals collected by the acceleration sensors are imported for fault diagnosis of planetary gearbox. High dimension fault features are extracted by analyzing the vibration signals in time domain, frequency domain and time-frequency domain. The criteria for selecting lower dimension optimal fault features of planetary gearbox are designed, and the mathematic model for fault feature selection with multi-criteria is established. After that, a new feature selection method using multi-objective evolutionary algorithm based on Decomposition (MOEA/D) is applied to obtain diverse lower dimension quasi optimal fault feature subsets. Then each quasi optimal fault feature subset is transferred to a base classifier for primary fault diagnosis. Those base classifications are performed by support vector machine and sparse Bayesian extreme learning machine respectively. Dezert-Smarandache rules are applied for classifier-level fusion to achieve and evaluate overall accuracy of the fault diagnosis for planetary gearbox. Experimental results state that the proposed method constantly gets diverse lower dimension quasi optimal fault features smoothly, and significantly improves the accuracy and robustness of fault diagnosis.
In the past decades, various effective and efficient multi-objective evolutionary algorithms (MOEAs) have been proposed for solving multi-objective optimization problems. However, existing MOEAs cannot satisfactorily ...
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In the past decades, various effective and efficient multi-objective evolutionary algorithms (MOEAs) have been proposed for solving multi-objective optimization problems. However, existing MOEAs cannot satisfactorily address multimodal multi-objective optimization problems that demand to find multiple groups of optimal solutions simultaneously. In this paper, we propose an evolution strategy to solve multimodal multi-objective optimization problems, named MMO-MOES. This paper focus on searching for well-converged and well-distributed solutions in the decision space. Firstly, a novel niching strategy in the decision space, which imitates the repulsive force among isotropic magnetic particles, is adopted to drive the individuals to preserve uniform distances from each other and spread to the whole Pareto set automatically. This strategy is effective in finding multiple groups of optimal solutions simultaneously. Secondly, MMO-MOES requires only a very small population size to obtain a well-distributed and well-converged set of Pareto optimal solutions in the decision space. The greater the population size, the clearer contour of the approximate Pareto sets and Pareto front will be. Finally, the MMO-MOES is compared against some chosen leading-edge MMOEAs. The experimental results demonstrate that MMO-MOES provides exceptional performance in searching for the complete Pareto subsets and Pareto front on Omni-test problem, Symmetrical Parts (SYM-PART) problems, and CEC 2019 multimodal multi-objective Optimization Problems (MMOPs) test suite. (C) 2020 Elsevier B.V. All rights reserved.
Parameter Sweep Experiments (PSEs) are commonplace to perform computer modelling and simulation at large in the context of industrial, engineering and scientific applications. PSEs require numerous computational resou...
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ISBN:
(纸本)9783030608842;9783030608835
Parameter Sweep Experiments (PSEs) are commonplace to perform computer modelling and simulation at large in the context of industrial, engineering and scientific applications. PSEs require numerous computational resources since they involve the execution of many CPU-intensive tasks. Distributed computing environments such as Clouds might help to fulfill these demands, and consequently the need of Cloud autoscaling strategies for the efficient management of PSEs arise. The multi-objective Intelligent Autoscaler (MIA) is proposed to address this problem, which is based on the Non-dominated Sorting Genetic algorithm III (NSGA-III), while aiming to minimize makespan and cost. MIA is assessed utilizing the CloudSim simulator with three study cases coming from real-world PSEs and current characteristics of Amazon EC2. Experiments show that MIA significantly outperforms the only PSE autoscaler (MOEA autoscaler) previously reported in the literature, to solve different instances of the problem.
We give a critical assessment of the DEAP (Distributed evolutionaryalgorithm in Python) open-source library and highly recommend it to both beginners and experts alike. DEAP supports a range of evolutionaryalgorithm...
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We give a critical assessment of the DEAP (Distributed evolutionaryalgorithm in Python) open-source library and highly recommend it to both beginners and experts alike. DEAP supports a range of evolutionaryalgorithms including both strongly and loosely typed Genetic Programming, Genetic algorithm, and multi-objective evolutionary algorithms such as NSGA-II and SPEA2. It contains most of the basic functions required by evolutionary computation, so that its users can easily construct various flavours of both single and multi-objective evolutionary algorithms and execute them using multiple processors. It is ideal for fast prototyping and can be used with an abundance of other Python libraries for data processing as well as other machine learning techniques.
evolutionary computation has shown great performance in solving many multi-objective optimization problems;in many such algorithms, non-dominated sorting plays an important role in determining the relative quality of ...
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evolutionary computation has shown great performance in solving many multi-objective optimization problems;in many such algorithms, non-dominated sorting plays an important role in determining the relative quality of solutions in a population. However, the implementation of non-dominated-sorting can be computationally expensive, especially for populations with a large number of solutions and many objectives. The main reason is that most existing non-dominated sorting algorithms need to compare one solution with almost all others to determine its front, and many of these comparisons are redundant or unnecessary. Another reason is that as the number of objectives increases, more and more candidate solutions become non-dominated solutions, and most existing time-saving approaches cannot work effectively. In this paper, we present a novel non-dominated sorting strategy, called Hierarchical Non Dominated Sorting (HNDS). HNDS first sorts all candidate solutions in ascending order by their first objective. Then it compares the first solution with all others one by one to make a rapid distinction between different quality solutions, thereby avoiding many unnecessary comparisons. Experiments on populations with different numbers of solutions, different numbers of objectives and different problems have been done. The results show that HNDS has better computational efficiency than fast non-dominated sort, Arena's principle and deductive sort. (C) 2017 Elsevier B.V. All rights reserved.
To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen sto...
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To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionaryalgorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionaryalgorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology.
Nowadays, an increasing number of vehicle routing problem with stochastic demands (VRPSD) models have been studied to meet realistic needs in the field of logistics. In this paper, a bi-objective vehicle routing probl...
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Nowadays, an increasing number of vehicle routing problem with stochastic demands (VRPSD) models have been studied to meet realistic needs in the field of logistics. In this paper, a bi-objective vehicle routing problem with stochastic demands (BO-VRPSD) was investigated, which aims to minimize total cost and customer dissatisfaction. Different from traditional vehicle routing problem (VRP) models, both the uncertainty in customer demands and the nature of multiple objectives make the problem more challenging. To cope with BO-VRPSD, a membrane-inspired multi-objectivealgorithm (MIMOA) was proposed, which is characterized by a parallel distributed framework with two operation subsystems and one control subsystem, respectively. In particular, the operation subsystems leverage a multi-objective evolutionary algorithm with clustering strategy to reduce the chance of inferior solutions. Meanwhile, the control subsystem exploits a guiding strategy as the communication rule to adjust the searching directions of the operation subsystems. Experimental results based on the ten 120-node instances with real geographic locations in Beijing show that, MIMOA is more superior in solving BO-VRPSD to other classical multi-objective evolutionary algorithms.
This paper introduces the mathematical development and algorithm of the Improvement-Directions Mapping (IDM) method, which computes improvement directions to "push" the current solutions toward the true Pare...
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ISBN:
(纸本)9781450371285
This paper introduces the mathematical development and algorithm of the Improvement-Directions Mapping (IDM) method, which computes improvement directions to "push" the current solutions toward the true Pareto front. The main idea is to compute normal vectors to the front, as improvement directions in the objective space, to be then transformed into search directions in the variable space through a transformation tensor. The main contributions of the IDM as a local search operator versus previous approaches are the following: 1) It does not require of a priori information about improvement directions or location of the true Pareto front, 2) It uses a local quadratic approximation of the Pareto front to compute the transformation tensor, thus, reducing numerical problems and avoiding abrupt changes in the search direction which could lead to erratic searches. These features allow the IDM to be implemented as a local search operator within any multi-objective evolutionary algorithm (MOEA). The potential of the IDM is shown by hybridizing two well-known multi-objectivealgorithms: a) MOEA/D + IDM;b) NSGA-II + IDM. In the first approach, IDM "pushes" the offspring population in each iteration. A similar experiment is performed with the second approach. Furthermore, one more experiment evaluates the IDM as a refinement step that is applied to the last Pareto front delivered by NSGA-II.
Surrogate-assisted multi-objective evolutionary algorithms have been commonly used to solve multi-objective expensive problems. In this paper, we investigate whether the surrogate-assisted offspring generation method ...
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ISBN:
(纸本)9781728169293
Surrogate-assisted multi-objective evolutionary algorithms have been commonly used to solve multi-objective expensive problems. In this paper, we investigate whether the surrogate-assisted offspring generation method can improve the optimization efficiency of multi-objective evolutionary algorithms. We first construct a surrogate model for each objective function. After that, some candidate solutions from the surrogate models are used to produce promising offspring for the multi-objective evolutionary algorithm. In addition, a pre-screening criterion based on reference vectors and the non-dominated rank is used to select the surviving offspring and exactly evaluated individuals. The pre-screening criterion can ensure the diversity and convergence of the offspring, and reduce function evaluations. Benchmark problems with their dimensions varying from 8 to 30 are used to test the effects of the surrogate-assisted offspring generation method under the framework of using the pre-screening criterion. Experimental results show that using the candidate solutions from surrogate models can enhance the performance of its basic algorithm on most of the problems.
Power systems are crucial for low-carbon energy applications. Condition maintenance plays a vital role in reducing the maintenance cost of renewable power systems without sacrificing system reliability. This paper pro...
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Power systems are crucial for low-carbon energy applications. Condition maintenance plays a vital role in reducing the maintenance cost of renewable power systems without sacrificing system reliability. This paper proposes a hybrid method to effectively deal with the operational changes and uncertainties of state maintenance within the power system of renewable energy applications. Specifically, a multi-objective evolutionary algorithm is first adopted to maintain key components when only considering system variables and overall performance. During operation, numerous variations in offshore substations are detected from power grids and other equipment, such as continuous aging, weather, load factors, measurement, and human-judgment factors. Then, the advisor implements a system optimization maintenance plan in the substation, which can predict changes in load reliability based on the type 2 fuzzy logic and hidden Markov model technology. The reliability of the load point of each substation would also be obtained. Illustrative results indicate that these serious deteriorations would cause substation for the re-optimization maintenance and optimization activities to meet expected reliability. Through connecting an offshore substation to a medium-sized offshore substation, the uncertainties in condition-based maintenance of renewable energy applications can be well handled.
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