Gradient methods and their value in single-objective, real-valued optimization are well-established. As such, they play a key role in tackling real-world, hard optimization problems such as deformable image registrati...
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ISBN:
(纸本)9781450334723
Gradient methods and their value in single-objective, real-valued optimization are well-established. As such, they play a key role in tackling real-world, hard optimization problems such as deformable image registration (DIR). A key question is to which extent gradient techniques can also play a role in a multi-objective approach to DIR. We therefore aim to exploit gradient information within an evolutionary-algorithm-based multi-objective optimization framework for DIR. Although an analytical description of the multi-objective gradient (the set of all Pareto-optimal improving directions) is available, it is nontrivial how to best choose the most appropriate direction per solution because these directions are not necessarily uniformly distributed in objective space. To address this, we employ a Monte-Carlo method to obtain a discrete, spatially-uniformly distributed approximation of the set of Pareto-optimal improving directions. We then apply a diversification technique in which each solution is associated with a unique direction from this set based on its multi- as well as single-objective rank. To assess its utility, we compare a state-of-the-art multi-objective evolutionary algorithm with three different hybrid versions thereof on several benchmark problems and two medical DIR problems. Results show that the diversification strategy successfully leads to unbiased improvement, helping an adaptive hybrid scheme solve all problems, but the evolutionary algorithm remains the most powerful optimization method, providing the best balance between proximity and diversity.
This paper presents the development of web logistics system to collect products, whose objective to minimize the transport cost and finding the best routes and represent them graphically on a map. The routing problem ...
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ISBN:
(纸本)9781479988709
This paper presents the development of web logistics system to collect products, whose objective to minimize the transport cost and finding the best routes and represent them graphically on a map. The routing problem studied in this paper uses vehicle capacity constraints, time windows and dynamic constraints based on the domain model of the system. The method proposed of resolution was genetic algorithm, which is widely used in complex problems of combinatorial explosion. The results show that the system is efficient to find good route and the model is flexible the restrictions imposed the problem and future.
On the design of a hybrid renewable energy system multiple objectives are in general required to be optimized simultaneously. This study presents a general multi-objective combinatorial model for optimizing the hybrid...
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ISBN:
(纸本)9781467376792
On the design of a hybrid renewable energy system multiple objectives are in general required to be optimized simultaneously. This study presents a general multi-objective combinatorial model for optimizing the hybrid PV-wind-diesel-battery system configuration. The model considers four objectives, i.e., minimizing the lifetime system cost, lifetime CO2 and SO2 emissions and maximizing the system output power. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) approach is employed to obtain a set of Pareto optimal solutions to the problem. Each solution corresponds to a non-inferior design, i.e., a good combination of PV, wind, diesel and battery. By further considering the practical situation, a satisfied design could be selected.
This paper presents new advances in development of dedicated evolutionary algorithms (EA) for large non-linear constrained optimization problems. The primary objective of our research is a significant increase of the ...
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ISBN:
(纸本)9781479974924
This paper presents new advances in development of dedicated evolutionary algorithms (EA) for large non-linear constrained optimization problems. The primary objective of our research is a significant increase of the computational efficiency of the standard EA. The EA are understood here as Genetic algorithms using decimal chromosomes, three standard operators: selection, crossover, and mutation, as well as additional new speed-up techniques. So far we have preliminarily proposed several general concepts, including smoothing and balancing, a'posteriori solution error analysis and related techniques, as well as an adaptive step-by-step mesh refinement. We discuss here the efficiency of chosen speed-up techniques using simple but demanding benchmark problems, including residual stress analysis in elastic-perfectly plastic bodies under cyclic loadings, and physically based smoothing of experimental data. Particularly, we consider a smoothing technique using average solution curvature, new criteria for selection based on global solution error, as well as a step-by-step mesh refinement combined with smoothing. Preliminary numerical results clearly indicate a possibility of significant acceleration of calculations, as well as practical application of the improved EA to the optimization problems considered.
This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing. include...
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ISBN:
(纸本)9780791846483
This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing. include inner race fault, outer race fault and a single ball defect. The fault related features are extracted experimentally by processing the acquired vibration signals in both the time and frequency domain. Techniques based on the power spectral density (PSD) and wavelet transform (WT) are utilized for feature extraction. The sensitivity of the proposed method is investigated under varying operating speeds and radial bearing load. In this study, the inverse problem of parameter identification is investigated. The problem of parameter identification is recast as an optimization problem and two well known evolutionary algorithms, differential evolution (DE) and particle swarm optimization (PSO), are used to identify system parameters given a system response. For online parameter identification, differential evolution outperforms particle both in terms of adaptability and tighter convergence properties. The distinction between the two methods is not distinctively obvious on the offline parameter identification problem.
evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity...
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ISBN:
(纸本)9781577357384
evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity analysis of evolutionary algorithms for a dynamic variant of a classical combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very effective in dynamically tracking changes made to the problem instance.
As users can have greatly different preferences, the personalization of ambient devices is of utmost importance. Several approaches have been proposed to establish such a personalization in the form of machine learnin...
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In many applications of evolutionary algorithms, the time required to evaluate the fitness of individuals is long and variable. When the variance in individual evaluation times is non-negligible, traditional, synchron...
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A recent trend in evolutionary algorithms (EAs) transfers expertise from and to other areas of machine learning. An interesting novel symbiosis considers: i) reinforcement learning (RL), which learns on-line and off-l...
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ISBN:
(纸本)9781450334884
A recent trend in evolutionary algorithms (EAs) transfers expertise from and to other areas of machine learning. An interesting novel symbiosis considers: i) reinforcement learning (RL), which learns on-line and off-line difficult dynamic elaborated tasks requiring lots of computational resources, and ii) EAs with the main strength its eloquence and computational efficiency. These two techniques address the same problem of reward maximization in difficult environments that can include stochasticity. Sometimes, they exchange techniques in order to improve their theoretical and empirical efficiency, like computational speed for on-line learning, and robust behaviour for the off-line optimisation algorithms. For example, multi-objective RL uses tuples of rewards instead of a single reward value and techniques from multi-objective EAs should be integrated for an efficient exploration/exploitation trade-off. The problem of selecting the best genetic operator is similar to the problem an agent faces when choosing between alternatives in achieving its goal of maximising its cumulative expected reward. Practical approaches select the RL method that solve the best online operator selection problem.
This paper proposes an improved performance metric for multiobjective evolutionary algorithms with user preferences. This metric uses the idea of decomposition to transform the preference information into m+1 points o...
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