The majority of problems in aircraft production and operation require decisions made in the presence of uncertainty. For this reason aerodynamic designs obtained with traditional deterministic optimization techniques ...
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evolutionary Game (EG) theory is effective approach to understand and analyze the widespread cooperative behaviors among individuals. Reconstructing EG networks is fundamental to understand and control its collective ...
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Tertiary protein structure prediction in silico is one of the most challenging problems in Structural Bioinformatics. The challenge arises due to the combinatorial explosion of plausible shapes, where a long amino aci...
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In recent years, the design of new selection mechanisms based on quality indicators has become a popular trend in the development of Multi-Objective evolutionary algorithms (MOEAs). This trend has been motivated by th...
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Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicat...
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The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the ...
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The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of cognitive radio networks into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. evolutionary algorithms are suitable techniques to solve the above-mentioned problem. In this paper, we design a CR engine for wireless multicarrier transceivers using real-coded biogeography-based optimization (RCBBO). The CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. RCBBO uses a mutation operator in order to improve the diversity of the population and enhance the exploration ability of the original BBO algorithm. The simulation results show that the RCBBO driven CR engine can obtain better results than the original BBO and outperform results from the literature. Moreover, RCBBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.
The contribution of the present paper is in introducing a numerical method to improve the automatic characterization of thin films by increasing the effectiveness of numerical methods that take into account the macros...
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The contribution of the present paper is in introducing a numerical method to improve the automatic characterization of thin films by increasing the effectiveness of numerical methods that take into account the macroscopic shape of the tip. To achieve this objective, we propose the combination of different feed-forward neural networks architectures adapted to the specific requirements of the physical system under study. First, an Adaline architecture is redefined as a linear combination of Green functions obtained from the Laplace equation. The learning process is also redefined to accurately calculate the electrostatic charges inside the tip. We demonstrate that a complete training set for the characterization of thin films can be easily obtained by this methodology. The characterization of the sample is developed in a second stage where a multilayer perceptron is adapted to work efficiently in experimental conditions where some experimental data can be lost. We demonstrate that a very efficient strategy is to use evolutionary algorithms as training method. By the modulation of the fit function, we can improve the network performance in the characterization of thin films where some information is missing or altered by experimental noise due to the small tip-sample working distances. By doing so, we can discriminate the conductive properties of thin films from force curves that have been altered explicitly to simulate realistic experimental conditions. (C) 2017 Elsevier B.V. All rights reserved.
In the recent literature a popular algorithm namely 'Competitive Swarm Optimizer (CSO)' has been proposed for solving unconstrained optimization problems that updates only half of the population in each iterat...
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In the recent literature a popular algorithm namely 'Competitive Swarm Optimizer (CSO)' has been proposed for solving unconstrained optimization problems that updates only half of the population in each iteration. A modified CSO (MCSO) is being proposed in this paper where two thirds of the population swarms are being updated by a tri-competitive criterion unlike CSO. A small change in CSO makes a huge difference in the solution quality. The basic idea behind the proposition is to maintain a higher rate of exploration to the search space with a faster rate of convergence. The proposed MCSO is applied to solve the standard CEC2008 and CEC2013 large scale unconstrained benchmark optimization problems. The empirical results and statistical analysis confirm the better overall performance of MCSO over many other state-of-the-art meta-heuristics, including CSO. In order to confirm the superiority further, a real life problem namely 'sampling-based image matting problem' is solved. Considering the winners of CEC 2008 and 2013, MCSO attains the second best position in the competition. (C) 2017 Elsevier B.V. All rights reserved.
Different attack and defence techniques have been evolved over time as actions and reactions between black-hat and white-hat communities. Encryption, polymorphism, metamorphism and obfuscation are among the techniques...
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Different attack and defence techniques have been evolved over time as actions and reactions between black-hat and white-hat communities. Encryption, polymorphism, metamorphism and obfuscation are among the techniques used by the attackers to bypass security controls. On the other hand, pattern matching, algorithmic scanning, emulation and heuristic are used by the defence team. The Antivirus (AV) is a vital security control that is used against a variety of threats. The AV mainly scans data against its database of virus signatures. Basically, it claims a virus if a match is found. This paper seeks to find the minimal possible changes that can be made on the virus so that it will appear normal when scanned by the AV. Brute-force search through all possible changes can be a computationally expensive task. Alternatively, this paper tries to apply a Genetic Algorithm in solving such a problem. Our proposed algorithm is tested on seven different malware instances. The results show that in all the tested malware instances only a small change in each instance was good enough to bypass the AV.
Heat treatment is an essential process in many production systems, which is generally carried out in a heat exchanger network (HEN). The major complication arisen in heat treatment is the fouling due to the deposition...
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Heat treatment is an essential process in many production systems, which is generally carried out in a heat exchanger network (HEN). The major complication arisen in heat treatment is the fouling due to the deposition of unwanted particles on heat exchanger surfaces. The difficulties, faced in mitigating the fouling by improving the design of heat exchangers or controlling process parameters, necessitate periodic cleaning of the heat exchangers for reinstating their performances. Accordingly, a HEN is desired to schedule in a way to minimize the cleaning cost satisfying various process conditions. In such an attempt, three mixed-binary evolutionary algorithms (EAs) are investigated here for scheduling a HEN engaged in milk pasteurization, in which the growth rate of fouling is comparatively very high. The experimental results depict that the minimum cleaning cost, however, is accompanied with overheating of milk consuming excess energy and a higher outlet temperature of the heating medium (steam) causing excess requirement of steam. Therefore, the scheduling of the HEN is also handled as a multi-objective optimization problem for simultaneously minimizing the cleaning cost, overheating of milk and flow rate of steam, in which the EAs could maintain a better balance among the three conflicting objectives. (C) 2017 Elsevier B.V. All rights reserved.
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