In this article, a novel approach to deal with the design of in-building wireless networks deployments is proposed. This approach known as MOQZEA (Multiobjective Quality Zone Based evolutionary Algorithm) is a hybrid ...
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In this article, a novel approach to deal with the design of in-building wireless networks deployments is proposed. This approach known as MOQZEA (Multiobjective Quality Zone Based evolutionary Algorithm) is a hybrid evolutionary algorithm adapted to use a novel fitness function, based on the definition of quality zones for the different objective functions considered. This approach is conceived to solve wireless network design problems without previous information of the required number of transmitters, considering simultaneously a high number of objective functions and optimizing multiple configuration parameters of the transmitters.
This paper introduces and studies the application of Constrained Sampling evolutionary algorithms in the framework of an UAV based search and rescue scenario. These algorithms have been developed as a way to harness t...
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This paper introduces and studies the application of Constrained Sampling evolutionary algorithms in the framework of an UAV based search and rescue scenario. These algorithms have been developed as a way to harness the power of evolutionary algorithms (EA) when operating in complex, noisy, multimodal optimization problems and transfer the advantages of their approach to real time real world problems that can be transformed into search and optimization challenges. These types of problems are denoted as Constrained Sampling problems and are characterized by the fact that the physical limitations of reality do not allow for an instantaneous determination of the fitness of the points present in the population that must be evolved. A general approach to address these problems is presented and a particular implementation using Differential Evolution as an example of CS-EA is created and evaluated using teams of UAVs in search and rescue missions. The results are compared to those of a Swarm Intelligence based strategy in the same type of problem as this approach has been widely used within the UAV path planning field in different variants by many authors. (C) 2013 Elsevier B.V. All rights reserved.
Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy s...
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Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the CO 2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods.
An original method is presented to classify nano-particle aggregates into one of the morphological classes that were previously proposed in the literature. Carbon black was selected as a study case in this work, since...
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An original method is presented to classify nano-particle aggregates into one of the morphological classes that were previously proposed in the literature. Carbon black was selected as a study case in this work, since it is a nanoreinforcement used massively in many industries, but the proposed method can be applied to other particulate aggregates as well. This methodology consists of three steps: (i) transmission electron microscopy image processing to compute a set of twenty-one morphological characteristics for each aggregate including the new attributes proposed herein, (ii) a multivariate analysis of the dataset to reduce the problem dimensionality and (iii) creation and assessment of decision trees based on evolutionary algorithms to classify the aggregates. The effectiveness of the method was proven for a balanced-per-class sample of forty-eight selected aggregates. The original dimension of the problem is reduced to three principal components, which explain 90% of the total variance. The best model classifies an aggregate into one of the four morphological classes in a simple comparison process. The accuracy of the applied model to classify new aggregates was 75%. (C) 2014 Elsevier B.V. All rights reserved.
evolutionary multi-objective algorithms have been applied to beam angle optimization (called BAO) for generating diverse trade-off radiotherapy treatment plans. However, their performance is not so effective due to th...
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evolutionary multi-objective algorithms have been applied to beam angle optimization (called BAO) for generating diverse trade-off radiotherapy treatment plans. However, their performance is not so effective due to the ignorance of using the specific clinical knowledge that can be obtain intuitively by clinical physicist. To address this issue, we suggest a pattern mining based evolutionary multi-objective algorithm called PM-EMA, in which two strategies for using the knowledge are proposed to accelerate the speed of population convergence. Firstly, to discover the potential beam angle distribution and discard the worse angles, the pattern mining strategy is used to detect the maximum and minimum sets of beam angles in non-dominated solutions of the population and utilize them to generate offspring to enhance the convergence. Moreover, to improve the quality of initial solutions, a tailored population initialization strategy is proposed by using the score of beam angles defined by this study. The experimental results on six clinical cancer cases demonstrate the superior performance of the proposed algorithm over six representative algorithms.
The orienteering problem (OP) is widely applied in real life. However, as the scale of real-world problem scenarios grows quickly, traditional exact, heuristics, and learning-based methods have difficulty balancing op...
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The evolutionary algorithms with shuffling concept divide a population into several groups and then each group try to evolve its members in an independent evolutionary process. In an attempt to increase and diversify ...
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evolutionary algorithms have been widely used to solve dynamic optimization problems. Memory-based evolutionary algorithms are often used when the dynamics of the environment follow some repeated behavior. Over the la...
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evolutionary algorithms have been widely used to solve dynamic optimization problems. Memory-based evolutionary algorithms are often used when the dynamics of the environment follow some repeated behavior. Over the last few years, the use of prediction mechanisms combined with memory has been explored. These prediction techniques are used to avoid the decrease of the algorithm's performance when a change occurs. This paper investigates the use of prediction methods in memory-based evolutionary algorithms for two distinct situations: to predict when the next change will happen and how the environment will change. For the first predictor two techniques are explored, one based on linear regression and another supported by nonlinear regression. For the second, a technique based on Markov chains is explored. Several experiments were carried out using different types of dynamics in two benchmark problems. Experimental results show that the incorporation of the proposed prediction techniques efficiently improves the performance of evolutionary algorithms in dynamic optimization problems.
Currently, the oil and gas industry faces numerous challenges in addressing geosteering issues in horizontal drilling. To optimize the extraction of hydrocarbon resources and to avoid penetration in aquifers, industry...
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Currently, the oil and gas industry faces numerous challenges in addressing geosteering issues in horizontal drilling. To optimize the extraction of hydrocarbon resources and to avoid penetration in aquifers, industry experts frequently modify the drilling trajectory using real-time measurements. This approach involves quantifying subsurface uncertainties in real-time, enhancing operational decision-making with more informed insights but also adding to its complexity. This paper demonstrates an approach to decision making for trajectory correction based on real-time formation evaluation data and the differential evolution algorithm. The approach uses volumetric resistivity log data and data from reservoir models, such as porosity. The provided methodology suggests corrections for planned well trajectories by maximization of the objective function. The objective function operates with a calculated hydrocarbon saturation environment as the decision-making system in a virtual sequential drilling process. To demonstrate the accuracy and reliability of our approach, we compared the simulations of the corrected trajectory with the preliminary trajectory drilled in the same area. In addition, we conducted several experiments to tune the hyper-parameters of the differential evolution algorithm to select the optimal parameter set for our case study and compared proposed differential evolution algorithm with particle swarm optimization and pattern search algorithms. The results of our experiments showed that the real-time formation evaluation data combined with the differential evolution algorithm outperformed a trajectory provided by the drilling engineers. Differential evolution algorithm demonstrated strong performance compared to others optimization algorithms. We have implemented a complete pipeline from generating resistivity and porosity cubes, using the Archie equation to estimate oil saturation, and consequently generating a corrected trajectory in this cube based on
—Spatial optimization problems (SOPs) refer to a class of problems where the decision variables require spatial organization. Existing methods based on evolutionary algorithms (EAs) fit conventional evolutionary oper...
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