This paper proposes a novel adaptive nesting evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure's design and the way it is operate...
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This paper proposes a novel adaptive nesting evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure's design and the way it is operated in time (specifically, the charging and discharging scheduling of the Energy Storage System, ESS, elements). For this purpose, a real MG scenario consisting of a wind and a photovoltaic generator, an ESS made up of one electrochemical battery, and residential and industrial loads is considered. Optimization is addressed by nesting a two-steps procedure [the first step optimizes the structure using an evolutionary Algorithm (EA), and the second step optimizes the scheduling using another EA] following different adaptive approaches that determine the number of fitness function evaluations to perform in each EA. Finally, results obtained are compared to non-nesting 2-steps algorithm evolving following a classical scheme. Results obtained show a 3.5 % improvement with respect to the baseline scenario (the non-nesting 2-steps algorithm), or a 21 % improvement when the initial solution obtained with the Baseline Charge and Discharge Procedure is used as reference.
Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling t...
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Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities;our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation.
In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for solving numerical and combinatorial optimization problems. This model does not use migration as the solution dispersion...
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In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for solving numerical and combinatorial optimization problems. This model does not use migration as the solution dispersion mechanism, in its place a more efficient population-merging mechanism is used that dynamically reduces the population size as well as the total number of parallel evolving populations. Even more relevant is the fact that the proposed model incorporates an entropy measure to determine how to merge the populations such that no valuable information is lost during the evolutionary process. Extensive experimentation, using genetic algorithms over a well-known set of classical problems, shows the proposed model to be faster and more accurate than the traditional one.
Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maxi...
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Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of lowlevel (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives. (C) 2016 Elsevier Ltd. All rights reserved.
The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes ...
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The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes several important cases. The Steiner tree problem in graphs (GSTP) is one of them. Many heuristics have been proposed for STP, and some of them have proved to be performance guarantee approximation algorithms for this problem. Since evolutionary algorithms (EAs) are general and popular randomized heuristics, it is significant to investigate the performance of EAs for STP. Several empirical investigations have shown that EAs are efficient for STP. However, up to now, there is no theoretical work on the performance of EAs for STP. In this article, we reveal that the (1+1) EA achieves 3/2-approximation ratio for STP in a special class of quasi-bipartite graphs in expected runtime O(r(r + s - 1) . w(max)), where r, s, and w(max) are, respectively, the number of Steiner nodes, the number of special nodes, and the largest weight among all edges in the input graph. We also show that the (1+1) EA is better than two other heuristics on two GSTP instances, and the (1+1) EA may be inefficient on a constructed GSTP instance.
Business process management (BPM) is an acknowledged source of corporate performance. Despite the mature body of knowledge, computational support is considered as a highly relevant research gap for redesigning busines...
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Business process management (BPM) is an acknowledged source of corporate performance. Despite the mature body of knowledge, computational support is considered as a highly relevant research gap for redesigning business processes. Therefore, this paper applies evolutionary algorithms (EAs) that, on a conceptual level, mimic the BPM lifecycle - the most popular BPM approach - by incrementally improving the status quo and bridging the trade-off between maintaining well-performing design structures and continuously evolving new designs. Beginning with describing process elements and their characteristics in matrices to aggregate process information, the EA then processes this information and combines the elements to new designs. These designs are then assessed by a function from value-based management. This economic paradigm reduces designs to their value contributions and facilitates an objective prioritization. Altogether, our triad of management science, BPM and information systems research results in a promising tool for process redesign and avoids subjective vagueness inherent to current redesign projects.
Multi-objective (or multi-criteria) optimization (MOO) is useful for gaining deeper insights into trade-offs among objectives of interest and then selecting one of the many optimal solutions found. It has attracted nu...
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Multi-objective (or multi-criteria) optimization (MOO) is useful for gaining deeper insights into trade-offs among objectives of interest and then selecting one of the many optimal solutions found. It has attracted numerous applications in chemical engineering. Common techniques for MOO are adaptations of stochastic global optimization methods, which include metaheuristics and evolutionary methods, for single-objective optimization. These techniques have been used mostly with maximum number of generations (MNG) as the termination criterion for stopping the iterative search. This criterion is arbitrary and computationally inefficient. Hence, this study investigates two termination criteria based on search progress (i.e., performance or improvement in solutions), for MOO of three complex chemical processes modeled by process simulators, namely, Aspen Plus and Aspen HYSYS. They are Chi-Squared test based Termination Criterion (CSTC) and Steady-State Detection Termination Criterion (SSDTC). Both these criteria are evaluated in two evolutionary algorithms for MOO. Results show that CSTC and SSDTC are successful in giving optimal solutions close to those after MNG but well before MNG. Of the two criteria, CSTC is more reliable and terminates the search earlier, thus reducing computational time substantially. (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Inspiring metaphors play an important role in the beginning of an investigation, but are less important in a mature research field as the real phenomena involved are understood. Nowadays, in evolutionary computation, ...
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Inspiring metaphors play an important role in the beginning of an investigation, but are less important in a mature research field as the real phenomena involved are understood. Nowadays, in evolutionary computation, biological analogies should be taken into consideration only if they deliver significant advantages.
This paper proposes a new battery swapping station (BSS) model to determine the optimized charging scheme for each incoming Electric Vehicle (EV) battery. The objective is to maximize the BSS's battery stock level...
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This paper proposes a new battery swapping station (BSS) model to determine the optimized charging scheme for each incoming Electric Vehicle (EV) battery. The objective is to maximize the BSS's battery stock level and minimize the average charging damage with the use of different types of chargers. An integrated objective function is defined for the multi-objective optimization problem. The genetic algorithm (GA), differential evolution (DE) algorithm and three versions of particle swarm optimization (PSO) algorithms have been implemented to solve the problem, and the results show that GA and DE perform better than the PSO algorithms, but the computational time of GA and DE are longer than using PSO. Hence, the varied population genetic algorithm (VPGA) and varied population differential evolution (VPDE) algorithm are proposed to determine the optimal solution and reduce the computational time of typical evolutionary algorithms. The simulation results show that the performances of the proposed algorithms are comparable with the typical GA and DE, but the computational times of the VPGA and VPDE are significantly shorter. A 24-h simulation study is carried out to examine the feasibility of the model. (C) 2017 Elsevier B.V. All rights reserved.
Evaluating and comparing multi-objective optimizers is an important issue. But, when doing a comparison, it has to be noted that the results can be influenced highly by the selected Quality Indicator. Therefore, the i...
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Evaluating and comparing multi-objective optimizers is an important issue. But, when doing a comparison, it has to be noted that the results can be influenced highly by the selected Quality Indicator. Therefore, the impact of individual Quality Indicators on the ranking of Multi-objective Optimizers in the proposed method must be analyzed beforehand. In this paper the comparison of several different Quality Indicators with a method called Chess Rating System for evolutionary algorithms (CRS4EAs) was conducted in order to get a better insight on their characteristics and how they affect the ranking of Multi-objective evolutionary algorithms (MOEAs). Although it is expected that Quality Indicators with the same optimization goals would yield a similar ranking of MOEAs, it has been shown that results can be contradictory and significantly different. Consequently, revealing that claims about the superiority of one MOEA over another can be misleading. (c) 2017 Elsevier B.V. All rights reserved.
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