Groundwater, as the key element of water resources, can play inevitably substantial role in managing groundwater aquafers. In fact, a ferocious demand for acquiring precise estimation of groundwater table is of remark...
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Groundwater, as the key element of water resources, can play inevitably substantial role in managing groundwater aquafers. In fact, a ferocious demand for acquiring precise estimation of groundwater table is of remarkable significance for analyzing water resources systems. A wide range of artificial intelligence techniques were used to predict groundwater table with highly convincing level of precision. Hence, this investigation aims to present an integration of a neuro-fuzzy (NF) system and group method of data handling (GMDH) in order to forecast the ground water table (GWT). The NF-GMDH network has been improved by means of the particle swarm optimization (PSO) and gravitational search algorithm (GSA) as evolutionary algorithms. The proposed methods were developed using records of two wells in Illinois State, USA. For this purpose, datasets related to time series of GWT have been grouped into three sections: training, testing, and validation phases. Through training and testing phases, the efficiency of the NF-GMDH methods were studied. The performances of proposed techniques were compared to the performance of radial basis function-neural network (RBF-NN). Evaluation of statistical results indicated which NF-GMDH-PSO network (R = 0.973 and RMSE = 0.545) is capable of providing higher level of precision rather than the NF-GMDH-GSA network (R = 0.969 and RMSE = 0.618) and RBF-NN (R = 0.814 and RMSE = 1.41). Also, conducting an external validation for the improved NF-GMDH models showed the most permissible level of precision.
This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiob-jective evolutionary...
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This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiob-jective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi-Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms. (C) 2019 Elsevier B.V. All rights reserved.
This study proposes a fuzzy chance-constrained programming model to include uncertainty in the biogas supply chain design problem. Uncertain parameters of the model are available workforce, biomass demand, available b...
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This study proposes a fuzzy chance-constrained programming model to include uncertainty in the biogas supply chain design problem. Uncertain parameters of the model are available workforce, biomass demand, available biomass and biomass price. A hybrid solution framework consisting of Monte Carlo simulation and evolutionary algorithms (genetic algorithm and differential evolution) is put forward to find the exact and near global optimal solution for the fuzzy chance-constrained model. The results of the test problems show that evolutionary algorithms can effectively solve the mixed integer nonlinear model of biogas location allocation within a reasonable computational time. Also, validation of the hybrid solution framework at different confidence levels is verified. The impacts of uncertainty in available biomass, biomass demand and available workforce on the overall cost of biogas supply chain are studied through sensitivity analysis. A real-world case study with real-life data available from the Province of Khorasan Razavi is performed. This is the first study that designs a biogas supply chain for a province of Iran.
This paper examines the incorporation of useful information extracted from the evolutionary process, in order to improve algorithm performance. In order to achieve this objective, we introduce an efficient method of e...
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This paper examines the incorporation of useful information extracted from the evolutionary process, in order to improve algorithm performance. In order to achieve this objective, we introduce an efficient method of extracting and utilizing valuable information from the evolutionary process. Finally, this information is utilized for optimizing the search process. The proposed algorithm is compared with the NSGAII for solving some real-world instances of the fuzzy portfolio optimization problem. The proposed algorithm outperforms the NSGAII for all examined test instances.
This paper introduces LEAC, a new C++ partitioning clustering library based on evolutionary computation. LEAC provides plenty of elements (individual encoding schemes, genetic operators, evaluation metrics, among othe...
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This paper introduces LEAC, a new C++ partitioning clustering library based on evolutionary computation. LEAC provides plenty of elements (individual encoding schemes, genetic operators, evaluation metrics, among others) which allow an easy and fast development of new clustering algorithms. Furthermore, it includes 23 algorithms which represent the state-of-the-art in evolutionary algorithms for partial clustering. The paper describes through examples the main features and the design principles of the software, as well as how to use LEAC to carry out a comparison between different proposals and how to extend it by including new algorithms. (C) 2019 Elsevier B.V. All rights reserved.
This paper firstly tries to address the issue of high computational load of a PI controller and secondly tries to find the best method among three existing methods for tuning the PI controller. The three existing meth...
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This paper firstly tries to address the issue of high computational load of a PI controller and secondly tries to find the best method among three existing methods for tuning the PI controller. The three existing methods are Ziegler-Nichols (Z-N), genetic algorithm (GA), and particle swarm optimization (PSO). To address the first issue, that is reducing the computational load of a PI controller, the event-based approach is proposed. To find the best tuning method of the three, three PI controllers, each using one of the methods, are simulated;the data is collected and comparison of results is made. The paper first introduces the event-based approach, PI controller tuning, GA, and PSO. Next, three different controllers, each tuned using one of the methods, are investigated. To assess each controller, two benchmarks are used. Furthermore, data for each controller is collected at first without implementing the event-based approach and then with the event-based approach implemented in the controllers. The controlled system in this experiment is the simulated model of a DC motor;therefore, all the data and figures generated are based on this model. Finally, the results are analyzed and discussed in all the event-based controllers;a significant reduction in computational effort is observed. Moreover, compared to the conventional Z-N method, a noticeable improvement in key performance areas, especially with the use of PSO, is observed and the data indicates the superiority of the PSO method over Z-N and GA.
The reduced Tomgro model is one of the popular biophysical models, which can reflect the actual growth process and model the yields of tomato-based on environmental parameters in a greenhouse. It is commonly integrate...
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The reduced Tomgro model is one of the popular biophysical models, which can reflect the actual growth process and model the yields of tomato-based on environmental parameters in a greenhouse. It is commonly integrated with the greenhouse environmental control system for optimally controlling environmental parameters to maximize the tomato growth/yields under acceptable energy consumption. In this work, we compare three mainstream evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution-ary (DE)) for calibrating the reduced Tomgro model, to model the tomato mature fruit dry matter (DM) weights. Different evolutionary algorithms have been applied to calibrate 14 key parameters of the reduced Tomgro model. And the performance of the calibrated Tomgro models based on different evolutionary algorithms has been evaluated based on three datasets obtained from a real tomato grower, with each dataset containing greenhouse environmental parameters (e.g., carbon dioxide concentration, temperature, photosynthetically active radiation (PAR)) and tomato yield information at a particular greenhouse for one year. Multiple metrics (root mean square errors (RMSEs), relative root mean square errors (r-RSMEs), and mean average errors (MAEs)) between actual DM weights and model-simulated ones for all three datasets, are used to validate the performance of calibrated reduced Tomgro model.
In this work a procedure namely Findpeaks2 is proposed to detect the maximum sidelobe level (SLL) from the samples of three dimensional radiation pattern. This procedure detects all sidelobe peaks form the samples of ...
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In this work a procedure namely Findpeaks2 is proposed to detect the maximum sidelobe level (SLL) from the samples of three dimensional radiation pattern. This procedure detects all sidelobe peaks form the samples of the radiation pattern in the entire visible region. For illustration, a low sidelobe radiation pattern synthesis problem is formulated for two concentric regular hexagonal antenna array (CRHAA) geometries, having 6- and 8- rings. To verify the extent of applicability of the proposed procedure, both broadside and scanned array configurations are considered. Feed current amplitudes are considered as the optimizing variables. Two variations of current distributions are considered, i) identical feed for all the elements on a ring (hence the one variable per ring needs to be optimized), and ii) asymmetric excitation distribution (set of excitation amplitude of all elements as optimizing variables). The design objective has been considered to optimize the radiation patterns with very low interference from the entire sidelobe region. To restrict the fall of directivity value, a constraint on the lower limit of directivity value is considered. The impacts of symmetry and the constraint on directivity on the search of these algorithms are studied. evolutionary algorithms like Real Coded Genetic Algorithm (RGA), Firefly Algorithm (FFA), Flower Pollination Algorithm (FPA), an adaptive variant of Particle Swarm Optimization Algorithm namely (APSO), and two recently proposed variants of DE namely Exponentially Weighted Moving Average Differential Evolution (EWMA-DE), and Differential Evolution with Individual Dependent Mechanism (IDE) are employed for this pattern optimization problem.
In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned with the installation or remo...
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In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned with the installation or removal of reactive power equipment in power system. This article proposes an effective way to find out the optimal parameter settings related to the system active power loss as well as operational cost. Genetic Algorithm (GA)-based strategy is applied to determine the optimal values of reactive power generation of generators, size of shunt capacitors, transformer tap settings prior to minimizing the system operating cost due to active power loss, installation cost of shunt capacitors at the weak nodes, and the cost of line-charging elements. The proposed approach is applied to IEEE 30 and IEEE 57 bus test system. Finally, a comparative analysis has been done to validate the efficacy of GA-based approach with some well-established meta-heuristic algorithms.
Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs...
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Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA's underlying searching behaviours.
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