A main focus of current research on evolutionary multiobjective optimization (EMO) is the study of the effectiveness of EMO algorithms for problems with many objectives. Among the several techniques that have led to t...
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ISBN:
(纸本)9783319159348;9783319159331
A main focus of current research on evolutionary multiobjective optimization (EMO) is the study of the effectiveness of EMO algorithms for problems with many objectives. Among the several techniques that have led to the development of more effective algorithms, decomposition and component-wise design have presented particularly good results. But how do they compare? In this work, we conduct a systematic analysis that compares algorithms produced using the MOEA/D decomposition-based framework and the AutoMOEA component-wise design framework. In particular, we identify a version of MOEA/D that outperforms the best known MOEA/D algorithm for several scenarios and confirms the effectiveness of decomposition on problems with three objectives. However, when we consider problems with five objectives, we show that MOEA/D is unable to outperform SMS-EMOA, being often outperformed by it. Conversely, automatically designed AutoMOEAs display competitive performance on three-objective problems, and the best and most robust performance among all algorithms considered for problems with five objectives.
Multi-Objective evolutionary algorithms(MOEAs) have been gaining increased popularity and usage in different fields of engineering. For real world large scale optimization problems with large variable/search space, us...
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ISBN:
(纸本)9781479974924
Multi-Objective evolutionary algorithms(MOEAs) have been gaining increased popularity and usage in different fields of engineering. For real world large scale optimization problems with large variable/search space, using a large population of individuals in proportion to the size of search space is ubiquitous. Solving such problems with current state of the art algorithms like NSGA-II [1] is pervasive. The strength of NSGA-II lies in its non-dominance selection procedure and non-dominance based sorting of a population of individuals. Although, the non-dominated sort is computationally efficient for a small population (10(2) - 10(3)) of solutions but becomes computationally expensive and slow for a large population (10(4) - 10(5)) of solutions. Also, various archive based algorithms [2], [3] have been proposed in past which make use of a large population apart from the principal population. Therefore, there is a huge need for a scalable and parallel implementation of NSGA-II. With advent of consumer level Graphics processing units(GPUs) and advancement of CUDA framework we try to fill this research gap using GPGPU architecture. In this paper we propose a parallel GPU based implementation of NSGA-II with major focus on non-dominated sorting. The proposed approach can be easily coupled with the original form of NSGA-II to solve real world problems using large populations.
Echo State Network (ESN) is a special type of neural network with a randomly generated structure called the reservoir. The performance of ESN is sensitive to the reservoir parameters, which have to be tuned for best p...
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ISBN:
(纸本)9781467378253
Echo State Network (ESN) is a special type of neural network with a randomly generated structure called the reservoir. The performance of ESN is sensitive to the reservoir parameters, which have to be tuned for best performance. Tuning of the reservoir parameters using evolutionary algorithms can be slow and produce inconsistent results. In this paper, we present a simple method for generating reservoirs based on templates that makes the reservoir matrices deterministic with respect to the parameters. Compared with the traditional method where the reservoir matrices are random, tuning of the reservoir parameters with an evolutionary algorithm needs less time, less number of cost function evaluations, and produces more reliable results using the proposed method.
In this paper we argue that to produce good optimization performances, the exploration of the solution space does not need to be carried out in the unorderly fashion most evolutionary algorithms use. Other strategies ...
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ISBN:
(纸本)9781479974924
In this paper we argue that to produce good optimization performances, the exploration of the solution space does not need to be carried out in the unorderly fashion most evolutionary algorithms use. Other strategies that seek to minimize the cost involved in successive evaluation processes should be explored. This does not imply a fundamental change on how evolutionary algorithms work, but rather, it brings some structure onto how solution spaces are explored by contemplating decoding cost as one of the elements to be minimized when sampling. The traditional implementations of most evolutionary algorithms assume that any point in the solution space can be evaluated any time and at no cost. However, this is not always the case and often each step of the process only part of the solution space is available for evaluation giving rise to a class of problems we have called Constrained Sampling optimization problems over which evolutionary algorithms are quite inefficient. To address these problems we have proposed a modification of the general strategy of evolutionary algorithms to address these constraints efficiently. Here, we study the effects of this approach when applied to problems that are not constrained, thus modifying the way the solution space is explored. This study is carried out to determine how these modification impact the performance of a set of popular evolutionary algorithms over a representative set of benchmark functions corresponding to fitness landscapes with a variety of characteristics. We show that by restricting the sampling capabilities of most algorithms, the cost of the optimization procedure is reduced for most types of fitness landscapes without affecting their results.
The Inventory Routing Problem is an important problem in logistics and known to belong to the class of NP hard problems. In the bi-criteria inventory routing problem the goal is to simultaneously minimize distance cos...
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ISBN:
(纸本)9783319188331;9783319188324
The Inventory Routing Problem is an important problem in logistics and known to belong to the class of NP hard problems. In the bi-criteria inventory routing problem the goal is to simultaneously minimize distance cost and inventory costs. This paper is about the application of indicator-based evolutionary algorithms and swarm algorithms for finding an approximation to the Pareto front of this problem. We consider also robust vehicle routing as a tricriteria version of the problem.
In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-call...
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In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-called grey box identification where we search for the characteristic of the system's valve. the second problem is black box identification where we search the model of the system with the usage of system's measurements and the third one is a system's controller design. We solved these problems with the usage of genetic algorithms differential evolution, evolutionary strategies, genetic programming and a de eloped approach called AMEBA algorithm. All methods have been proven to be very useful for solving problems of the grey box identification and design of the controller for the mentioned system but AMEBA algorithm have also been successfully used in black box identification problem where it generated a suitable model. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The optimization of many objectives requires a set of optimal solutions known as Pareto solutions. Similarly to the optimization of single objective in evolutionary algorithms (EAs), the Multiobjective evolutionary Al...
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ISBN:
(纸本)9781479986965
The optimization of many objectives requires a set of optimal solutions known as Pareto solutions. Similarly to the optimization of single objective in evolutionary algorithms (EAs), the Multiobjective evolutionary algorithms (MOEAs) also suffer from loss of genetic diversity, allowing the appearance of sparse regions along the Pareto frontier. A mechanism to maintain the population diversity along generations is needed. It is expected that, if diversity is controlled effectively, at the end of the evolutionary process, the Pareto Front optimum will be as uniformly distributed as possible. This paper proposes a new diversity operator that generates artificial solutions to fill sparse regions of the non-dominated set of solutions found by the MOEA. It uses artificial neural networks (ANN) to perform a reverse mapping from the phenotype to the corresponding genotype of an inserted artificial solution. This mechanism was tested with NSGA-II and SPEA2 algorithms. The addition of the diversity operator reached significant improvements in the hypervolume and the spread metrics of the obtained set of solutions non-dominated.
This paper focuses on the process of generating a sequence of sector configurations composed of two airspace component types Sharable Airspace Modules (SAMs) and Sectors Building Blocks (SBBs). An algorithm has been d...
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ISBN:
(纸本)9781479989409
This paper focuses on the process of generating a sequence of sector configurations composed of two airspace component types Sharable Airspace Modules (SAMs) and Sectors Building Blocks (SBBs). An algorithm has been developed that manages the main features of the dynamic sectors configuration (including sector design criteria). In order to make it run efficiently a pre-processing step will be presented to create a graph modelling of the inputs. Based on this initial graph, a mathematical model is defined which can be summarized by a multi-periods geometric graph partitioning problem. State, space, objective function and constraints will be also presented. Due to the induced complexity, a stochastic optimization algorithm based on artificial evolution is then proposed. A two layer chromosome is used for such a genetic algorithm for which recombination operators are proposed. Evaluation of the algorithm will be presented with a comparison to existing tools and operational approach.
In this article, a comparative study between population based optimization methods with random and restricted search space definition applied in the pattern synthesis of linear antenna arrays is presented. Synthesis p...
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ISBN:
(纸本)9781479983490
In this article, a comparative study between population based optimization methods with random and restricted search space definition applied in the pattern synthesis of linear antenna arrays is presented. Synthesis problem of reduced side lobe level and narrow beamwidth is considered. The design objective further considers the optimization of excitation amplitude and uniform inter element spacing using random and restricted search space definition by particle swarm optimization and differential evolution methods. As examples simulation of 12 and 21 elements have been considered. Effectiveness of the restriction in search space is proved through statistical and parametric analysis. Further comparison with published work has been carried out to prove the superiority of restricted search Particle Swarm Optimization.
Multimodal Optimization (MMO) aims at identifying several best solutions to a problem whereas classical optimization converge often to only one good solution. MMO has been an active research area in the past years and...
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ISBN:
(纸本)9781479975600
Multimodal Optimization (MMO) aims at identifying several best solutions to a problem whereas classical optimization converge often to only one good solution. MMO has been an active research area in the past years and several new evolutionary algorithms have been developed to tackle multimodal problems. In this work, we compare extensively three recent evolutionary algorithms (MoBiDE, Multimodal NSGAII and MOMMOP). Each algorithm uses multiobjectivization, together with niching techniques to address single objective MMO problems. We have fully re-implemented MoBiDE and MM-NSGAII in order to better evaluate their sensitivity to parameter changes and their strengths and weaknesses. We have carefully evaluated all algorithms on the same benchmark functions and with the same parameters settings. The algorithms are also compared to a non-multimodal evolutionary algorithm to better highlight the impact of the multimodal adaptations. Moreover, full access to the detailed results and source code is granted on our website for the ease of reproducibility.
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