The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have b...
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The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.
The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an ...
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The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods.
Convolutional neural networks (CNN) are highly effective for image classification and computer vision activities. The accuracy of CNN architecture depends on the design and selection of optimal parameters. The number ...
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Convolutional neural networks (CNN) are highly effective for image classification and computer vision activities. The accuracy of CNN architecture depends on the design and selection of optimal parameters. The number of parameters increases exponentially with every connected layer in deep CNN architecture. Therefore, the manual selection of efficient parameters entirely remains ad-hoc. To solve that problem, we must carefully examine the relationship between the depth of architecture, input parameters, and the model's accuracy. The evolutionary algorithms are prominent in solving the challenges in architecture design and parameter selection. However, the adoption of evolutionary algorithms itself is a challenging task as the computation cost increases with its evolution. The performance of evolutionary algorithms depends on the type of encoding technique used to represent a CNN architecture. In this article, we presented a comprehensive study of the recent approaches involved in the design and training of CNN architecture. The advantages and disadvantages of selecting a CNN architecture using evolutionary algorithms are discussed. The manual architecture is compared against automated CNN architecture based on the accuracy and range of parameters in the existing benchmark datasets. Furthermore, we have discussed the ongoing issues and challenges involved in evolutionary algorithms-based CNN architecture design.
Advances in miniaturization have led to the use of microchannels as heat sinks in industry. Studies have established that the thermal performance of a microchannel depends on its geometric parameters and flow conditio...
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Advances in miniaturization have led to the use of microchannels as heat sinks in industry. Studies have established that the thermal performance of a microchannel depends on its geometric parameters and flow conditions. This paper describes two approaches for determining the optimal geometric parameters of the microchannels in micro heat exchangers. One approach combines CFD analysis with an analytical method of calculating the optimal geometric parameters of micro heat exchangers. The second approach involves the usage of multi-objective genetic algorithms in combination with CFD. (c) 2005 Elsevier Ltd. All rights reserved.
evolutionary algorithm- based size optimization of plane and space truss structures is studied for sequential loading scenarios. Size optimization of a truss is a search for the most suitable cross-sectional areas of ...
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evolutionary algorithm- based size optimization of plane and space truss structures is studied for sequential loading scenarios. Size optimization of a truss is a search for the most suitable cross-sectional areas of the truss members from the available design spaces. The novelty of the present work lies in developing an evolution- based algorithm that can consider the sequential loading scenario while designing the planner and space trusses. Fortran- based computer programming has been developed for the proposed optimization method considered here. A standard displacement- based finite element method is implemented to obtain the trusses' nodal displacement and elemental stresses. To assess the performance of the proposed algorithm three plane trusses (3- bar, 18- bar, and 200- bar) and three space trusses (22- bar, 25- bar, and 72- bar) with various displacement and stress constraints under sequential loading have been considered. The optimum weights obtained from all the problems considered here have been analyzed and compared with the same, as obtained from other methods mentioned in the existing literature. It is important to note here that the results are in close agreement and in some cases optimum weights obtained from the present study are even better than the earlier results. Finally, a real- life design problem of an industrial roof truss has been considered to assess the applicability of the proposed algorithm. The convergence study and optimum cross- section of the truss have been reported as a case study.
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.
The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of...
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The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of path planning separately in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speedup. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both the hierarchies make use of an evolutionary algorithm for planning. Both these hierarchies optimize as the robot travels in the map. The static environment path is increasingly optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction. Experimentation was done in a variety of scenarios with static and mobile obstacles. In all cases the robot could optimally reach the goal. Further, the robot was able to escape from the sudden occurrence of obstacles.
In this paper, given a certain number of satellites (N-sat), which is limited due to the sort of mission or economical reasons, the Flower Constellation with N-sat satellites which has the best geometrical configurati...
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In this paper, given a certain number of satellites (N-sat), which is limited due to the sort of mission or economical reasons, the Flower Constellation with N-sat satellites which has the best geometrical configuration for a certain global coverage problem is sought by using evolutionary algorithms. In particular, genetic algorithm and particle swarm optimization algorithm are used. As a measure of optimality, the Geometric Dilution Of Precision (GDOP) value over 30000 points randomly and uniformly distributed over the Earth surface during the propagation time is used. The GDOP function, which depends on the geometry of the satellites with respect to the 30000 points over the Earth surface (as ground stations), corresponds to the fitness function of the evolutionary algorithms used throughout this work. Two different techniques are shown in this paper to reduce the computational cost of the search process: one that reduces the search space and the other that reduces the propagation time. The GDOP-optimal Flower Constellations are obtained when the number of satellites varies between 18 and 40. These configurations are analyzed and compared. Owing to the Flower Constellation theory we find explicit examples where eccentric orbits outperform circular ones for a global positioning system. (C) 2014 Elsevier Masson SAS. All rights reserved.
In this paper a geometric sizing method for a small electric powered flying wing is proposed. The geometric sizing method aims to reduce the effects of variations in the power plant characteristics on endurance. This ...
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In this paper a geometric sizing method for a small electric powered flying wing is proposed. The geometric sizing method aims to reduce the effects of variations in the power plant characteristics on endurance. This results in a single-objective design optimisation problem where the sensitivity to power plant characteristics of the endurance equation is minimised, constrained to Reynolds number, wing load, wing taper ratio, aircraft size and wing sweep angle. As a result, geometric characteristics of the flying wing such as span, tip chord and root chord are obtained. Flying wing aerodynamic characteristics are obtained by means of an inviscid fluid flow analysis program of the type low-order panel methods, known as CMARC. The optimisation problem involves a non convex function so that it is necessary to rely on heuristic programming methods. In particular an evolutionary Algorithm based on differential evolution is considered.
In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffer...
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In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.
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