The subject of the study is an application of evolutionary algorithms to optical node optimization in dense wavelength division multiplexing optical networks. The wider context of the presented research is in essence ...
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ISBN:
(纸本)9781510630666
The subject of the study is an application of evolutionary algorithms to optical node optimization in dense wavelength division multiplexing optical networks. The wider context of the presented research is in essence an improvement of service flexibility and achieving savings in capital expenditures in DWDM networks. Thus, the main objective of the optimization is to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and filters used in new generation of reconfigurable optical add drop multiplexers, etc. For this purpose a model based on integer programming is proposed. The efficiency of the integer programming based software is compared with that of evolutionary algorithms. The results obtained show that there is a large advantage in using evolutionary algorithms for optimizing large optical networks when compared with integer programming and mixed integer programming, whereby the two latter algorithms fail to find the optimal solution within reasonable computational time. The numerical experiments were carried out for realistic networks of different dimensions and traffic demand sets.
Event Takeover Values (ETV) measure the impact of each individual in the population dynamics of evolutionary algorithms (EA). Previous studies argue that ETV distribution of panmictic EAs fit power laws with exponent ...
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ISBN:
(纸本)9781450367486
Event Takeover Values (ETV) measure the impact of each individual in the population dynamics of evolutionary algorithms (EA). Previous studies argue that ETV distribution of panmictic EAs fit power laws with exponent between 2.2 and 2.5 and that this property is insensitive to fitness landscapes and design choices of the EAs. One exception is cellular EAs, for which there are deviations of the power law for large values. In this paper, ETVs for structured and panmictic EAs with different population size and mutation probability on several fitness landscapes were computed. Although the ETVs distribution of pamictic EAs is heavy-tailed, the log-log plot of the complementary cumulative distributed function shows no linearity. Furthermore, Vuong's tests on the distributions generated by several instances of the problems conclude that power law models cannot be favored over log-normal models. On the other hand, the tests confirm that cEAs impose significant deviations to the distribution tail.
An experimental comparison of evolutionary algorithms and random search algorithms for the optimal control problem is carried out. The problem is solved separately by several representatives of each type of algorithms...
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An experimental comparison of evolutionary algorithms and random search algorithms for the optimal control problem is carried out. The problem is solved separately by several representatives of each type of algorithms. The simulation is performed on a mobile robot model. The results of each algorithm performance are compared according to the best found value of the fitness function, the mean value and the standard deviation. (C) 2019 The Authors. Published by Elsevier B.V.
Service organizations such as telecom or utility companies require uninterrupted availability of spare parts to maintain their service. It is very important for them to have right spares available at the right time at...
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ISBN:
(纸本)9781728124858
Service organizations such as telecom or utility companies require uninterrupted availability of spare parts to maintain their service. It is very important for them to have right spares available at the right time at the right place. Spare parts are normally kept in a warehouse and it is crucial that the warehouses are also located in the right place, where they can provide maximum value to the business. Warehouses are increasingly getting smaller and mobile in their nature, which can be quickly deployed and redeployed to different locations in a very short time. Finding the optimal deployment locations of the warehouses quickly can be challenging, especially when there are a large number of storages involved. In this work, we present an evolutionary algorithm based approach to deploy a large number of mobile warehouses. The resulting tool can be periodically utilized to relocate them to different locations according to changing business requirements, at the same time providing a significantly better quality of design in comparison to a typical traditional manual mechanism. The presented solution is incorporated into a system called Intuit Strategic Planner that is operational at a major UK telecom and has contributed to a significant cost saving for the business.
With' the continuous application of evolutionary algorithms in various combinatorial optimization problems, the traditional evolutionary algorithms are prone to premature convergence and fall into local optimizati...
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ISBN:
(纸本)9781450362948
With' the continuous application of evolutionary algorithms in various combinatorial optimization problems, the traditional evolutionary algorithms are prone to premature convergence and fall into local optimization solutions as the complexity of the problems increases. To solve this problem, this paper proposes a hybrid algorithm combining the Generative adversarial nets (GAN) and Genetic Algorithm (GA). The algorithm is based on Genetic Algorithm and introducted the GAN sample as another sample to the generated model. The algorithm expected more abundant sample information through GAN mining, got the advantage of sample training GAN through the GA. It makes GAN learn from the edge of sample information, which can generate more advantages of samples. The generated sample is injected into the evolution of the next generation, increasing the diversity of samples and increasing the opportunity to find the optimal solution. In this paper, the hybrid algorithm is used to solve the Permutation Flow Shop Problem to verify the algorithm's solution ability. Experimental results show that the hybrid algorithm can avoid premature local optimal solution compared with the traditional evolutionary algorithm.
Facility layout problems, i.e., optimal placement of production units in a plant, become an inseparable part of manufacturing systems design and management. They are known to greatly impact the system performance. Thi...
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ISBN:
(纸本)9783030278786;9783030278779
Facility layout problems, i.e., optimal placement of production units in a plant, become an inseparable part of manufacturing systems design and management. They are known to greatly impact the system performance. This paper proposes a new formulation of the facility layout problem where workstations are to be placed into a hall. Within the hall, obstacles and communications can be defined. Each workstation can have multiple handling spaces attached to its sides and oriented links can be defined between workstations. A new evolutionary-based approach to solve this facility layout problem is proposed in single-objective as well as multi-objective variant. The method is experimentally evaluated on a set of standard VLSI floorplanning benchmarks as well as on the data set created specifically for the proposed facility layout problem. Results show the method is both competitive to the state-of-the-art floor-planners on the VLSI benchmarks and produces high-quality solutions to the proposed facility layout problem.
This article introduces a path-finding method based on evolutionary algorithms and a fully vectorized GPU implementation of it. The algorithm runs on real-time and it can handle dynamic obstacles in maps of arbitrary ...
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ISBN:
(纸本)9783030227500;9783030227494
This article introduces a path-finding method based on evolutionary algorithms and a fully vectorized GPU implementation of it. The algorithm runs on real-time and it can handle dynamic obstacles in maps of arbitrary size. The experiments show the proposed approach outperforms other traditional path-finding algorithms (e.g. A*). The conclusions present further improvement possibilities to the proposed approach like the application of multi-objective algorithms to represent full crowd models.
Classifying the porosity of sedimentary information is an important field of study with applications to tasks such as oil reservoir characterisation. Classifying porosity into groups based on Petrographical characteri...
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ISBN:
(纸本)9783030368081;9783030368074
Classifying the porosity of sedimentary information is an important field of study with applications to tasks such as oil reservoir characterisation. Classifying porosity into groups based on Petrographical characteristics has been attempted in the past using: expert systems, supervised clustering techniques and neural networks. In this paper, we expand upon the usage of neural networks for this classification task by applying evolutionary algorithms to determine optimal parameters. Despite recent advances in techniques to select hyperparameters it is still difficult to determine the optimal parameters for a given dataset. We further apply network reduction techniques to further improve classification accuracy. We produce results similar to the work done by Gedeon et al. [1] on this dataset.
Despite a large interest in real-world problems from the research field of evolutionary optimisation, established benchmarks in the field are mostly artificial. We propose to use game optimisation problems in order to...
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ISBN:
(纸本)9781450361118
Despite a large interest in real-world problems from the research field of evolutionary optimisation, established benchmarks in the field are mostly artificial. We propose to use game optimisation problems in order to form a benchmark and implement function suites designed to work with the established COCO benchmarking framework. Game optimisation problems are real-world problems that are safe, reasonably complex and at the same time practical, as they are relatively fast to compute. We have created four function suites based on two optimisation problems previously published in the literature (TopTrumps and MarioGAN). For each of the applications, we implemented multiple instances of several scalable single- and multi-objective functions with different characteristics and fitness landscapes. Our results prove that game optimisation problems are interesting and challenging for evolutionary algorithms.
The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. ...
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ISBN:
(纸本)9783030345006;9783030344993
The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. The aim of this research study is an automatic construction of a collection of FCM models based on various criteria depending on the structure of the model and forecasting capabilities. The simulation analysis was performed with the use of the developed multiobjective Individually Directional evolutionary Algorithm. Experiments show that the collection of fuzzy cognitive maps, in which each element is built on the basis of particular patient data, allows us to receive higher forecasting accuracy compared to the standard approach. Moreover, by appropriate aggregation of these collections we can also obtain satisfactory accuracy of forecasts for the new patient.
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