Representation choice and the development of search operators are crucial aspects of the efficiency of evolutionary algorithms (EAs) in combinatorial problems. Several researchers have proposed representations and ope...
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Representation choice and the development of search operators are crucial aspects of the efficiency of evolutionary algorithms (EAs) in combinatorial problems. Several researchers have proposed representations and operators for EAs that manipulate spanning trees. This paper proposes a new encoding called Node-depth Phylogenetic-based Encoding (NPE). NPE represents spanning trees by the relation between nodes and their depths using a relatively simple codification/decodification process. The proposed NPE operators are based on methods used for tree rearrangement in phylogenetic tree reconstruction: subtree prune and regraft;and tree bisection and reconstruction. NPE and its operators are designed to have high locality, feasibility, low time complexity, be unbiased, and have independent weight. Therefore, NPE is a good choice of data structure for EAs applied to network design problems. (C) 2016 Elsevier B.V. All rights reserved.
This paper proposes a new evolutionary algorithm to solve transmission expansion planning problems in electric power systems. In order to increase the robustness of the search process and to facilitate its use by plan...
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This paper proposes a new evolutionary algorithm to solve transmission expansion planning problems in electric power systems. In order to increase the robustness of the search process and to facilitate its use by planners on different networks and operation conditions, the proposed method uses multi-operators and a mechanism for dynamic adaptation of the selection probabilities of these operators. Two sets of search operators are proposed: evolutionary and specialized. The mathematical formulation considers a DC network model including transmission losses and the "N-1" deterministic criterion. The proposed method is applied to a well known academic test system and a configuration of the Brazilian network. (C) 2015 Elsevier B.V. All rights reserved.
A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA)...
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A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.
Distributed evolutionary algorithms are traditionally executed on homogeneous dedicated clusters, despite most scientists have access mainly to networks of heterogeneous nodes (e.g., desktop PCs in a lab). Fitting thi...
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Distributed evolutionary algorithms are traditionally executed on homogeneous dedicated clusters, despite most scientists have access mainly to networks of heterogeneous nodes (e.g., desktop PCs in a lab). Fitting this kind of algorithms to these environments, so that they can take advantage of their heterogeneity to save running time, is still an open problem. The different computational power of the nodes affects the performance of the algorithm, and tuning or fitting it to each node properly could reduce execution time. Since the distributed evolutionary algorithms include a whole range of parameters that influence the performance, this paper proposes a study on the population size. This parameter is one of the most important, since it has a direct relationship with the number of iterations needed to find the solution, as it affects the exploration factor of the algorithm. The aim of this paper consists in validating the following hypothesis: fitting the sub-population size to the computational power of the heterogeneous cluster node can lead to an improvement in running time with respect to the use of the same population size in every node. Two parameter size schemes have been tested, an offline and an online parameter setting, and three problems with different characteristics and computational demands have been used. Results show that setting the population size according to the computational power of each node in the heterogeneous cluster improves the time required to obtain the optimal solution. Meanwhile, the same set of different size values could not improve the running time to reach the optimum in a homogeneous cluster with respect to the same size in all nodes, indicating that the improvement is due to the interaction of the different hardware resources with the algorithm. In addition, a study on the influence of the different population sizes on each stage of the algorithm is presented. This opens a new research line on the fitting (offline or online) o
Optimisation is particularly important in the case of CO2 storage in saline aquifers, where there are various operational objectives to be achieved. The storage operation design process must also take various uncertai...
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Optimisation is particularly important in the case of CO2 storage in saline aquifers, where there are various operational objectives to be achieved. The storage operation design process must also take various uncertainties into account, which result in adding computational overheads to the optimisation calculations. To circumvent this problem upscaled models with which computations are orders of magnitude less time-consuming can be used. Nevertheless, a grid resolution, which does not compromise the accuracy, reliability and robustness of the optimisation in an upscaled model must be carefully determined. In this study, a 3D geological model based on the Forties and Nelson hydrocarbon fields and the adjacent saline aquifer, is built to examine the use of coarse grid resolutions to design an optimal CO2 storage solution. The optimisation problem is to find optimal allocation of total CO2 injection rate between existing wells. A simulation template of an area encompassing proximal-type reservoirs of the Forties-Montrose High is considered. The detailed geological model construction leads to computationally intensive simulations for CO2 storage design, so that upscaling is rendered unavoidable. Therefore, an optimal grid resolution that successfully trades accuracy against computational run-time is sought after through a thorough analysis of the optimisation results for different resolution grids. The analysis is based on a back-substitution of the optimisation solutions obtained from coarse-scale models into the fine-scale model, and comparison between these back-substitution models and direct use of fine-scale model to conduct optimisation. (C) 2016 The Authors. Published by Elsevier Ltd.
In current decades, various evolutionary algorithms(EAs) raise as well as many kinds of benchmarks are popular in evaluations of EAs' performances. Since there exists randomness in EAs' performances, the evalu...
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In current decades, various evolutionary algorithms(EAs) raise as well as many kinds of benchmarks are popular in evaluations of EAs' performances. Since there exists randomness in EAs' performances, the evaluations are made by a large number of runs in simulations or experiments in order to present a relatively fair comparison. However, there still exit several problems that have not been well explained. Does it make sense to deem two algorithms have equal ability if they have same final results? Is it convinced to decide winners or losers in comparisons just by tiny difference in performances? Besides the final results, how to compare algorithms' performances during the optimization iterations? In this paper, a neural network classifier based on extreme learning machine (ELM) is proposed to solve these problems. A novel role of classifier is first proposed to convince the differences between algorithms. If the classifier succeeds to classify algorithms based on their performances recorded in all generations, we deem the two algorithms have so convinced difference that comparisons of two algorithms can reflect algorithms' disparity. Therefore, the conclusions to judge the two algorithms are feasible and acceptable. Otherwise, if classifiers cannot distinguish two algorithms, we deem the two have similar performances so that it is meaningless to differ two algorithms just by tiny differences. By employing a set of classical benchmarks and six EAs, the simulations and computations are conducted. According to the analysis results, the proposed classifier can provide more information to reflect true abilities of algorithms, which is a novel view to compare EAs. (C) 2015 Elsevier B.V. All rights reserved.
This paper presents a technique that incorporates preference information within the framework of multi-objective evolutionary algorithms for the solution of many-objective optimization problems. The proposed approach ...
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This paper presents a technique that incorporates preference information within the framework of multi-objective evolutionary algorithms for the solution of many-objective optimization problems. The proposed approach employs a single reference point to express the preferences of a decision maker, and adaptively biases the search procedure toward the region of the Pareto-optimal front that best matches its expectations. Experimental results suggest that incorporating preferences within these algorithms leads to improvements in several quality criteria, and that the proposed approach is capable of yielding competitive results when compared against existing algorithms. (C) 2015 Elsevier Inc. All rights reserved.
Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic componen...
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Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of these evolutionary algorithms can be classified and combined to produce new algorithmic designs. The motivation for studies of this latter type stem from the development of flexible software frameworks and the usage of automatic algorithm configuration methods to find novel algorithm designs. In this paper, we propose an MOEA template and a new conceptual view of its components that surpasses existing frameworks in both number of algorithms that can be instantiated from the template and flexibility to produce novel algorithmic designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for continuous and combinatorial optimization problems. The automatically designed algorithms are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters.
Two evolutionary algorithms are introduced as universal approaches for the identification of water transport characteristics of inorganic porous materials in both absorption and desorption phases. At first, genetic al...
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Two evolutionary algorithms are introduced as universal approaches for the identification of water transport characteristics of inorganic porous materials in both absorption and desorption phases. At first, genetic algorithm and genetic programming are applied for the inverse analysis of water content profiles measured in an absorption experiment. A comparison of results with the output of the commonly used Boltzmann-Matano approach shows that the calculated diffusivities can reproduce experimental data with a similarly good or even slightly better accuracy. In the second part of investigations, a water desorption experiment is realized for autoclaved aerated concrete, a typical representative of inorganic porous materials used in the construction sector. The genetic algorithm and genetic programming exhibit an excellent performance also in this case. Both approaches can thus be considered as viable, more universal alternatives to the traditional methods.
In orientation to new developments in evolutionary biology we propose an extension of evolutionary algorithms in two dimensions, the regulatory algorithm (RGA). It consists of two levels of vectors, the regulatory vec...
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In orientation to new developments in evolutionary biology we propose an extension of evolutionary algorithms in two dimensions, the regulatory algorithm (RGA). It consists of two levels of vectors, the regulatory vector and the structural vector. Each element of the regulatory vector is connected with one or several elements of the structural vector, but not vice versa. The connections can be interpreted as steering connections, the switching on or off of the structural elements and/or as switching orders for the structural elements. An RGA that operates with the usual genetic operators of mutation and crossover can be used for avoiding rules like penalty or default operators, it is in certain problems significantly faster than a standard genetic algorithm, and it is very suited when modeling and optimizing systems that consist themselves of different levels. Examples of RGA usage are shown, namely, the optimal dividing of socially deviant youths in a hostel, the optimal introduction of communication standards in information systems, and the allocation of employees to superiors by taking into regard the different personality types.
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