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.
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.
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.
This paper presents a novel algorithm to solve dynamic multiobjective optimization problems. In dynamic multiobjective optimization problems, multiple objective functions and/or constraints may change over time, which...
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Wilting point is an important parameter indicating the inhibition of plant transpiration processes, which is essential for green infrastructures. Generalization of wilting point is very essential for analyzing the hyd...
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Wilting point is an important parameter indicating the inhibition of plant transpiration processes, which is essential for green infrastructures. Generalization of wilting point is very essential for analyzing the hydrological performance of green infrastructures (e.g. green roofs, biofiltration systems) and ecological infrastructures (wetlands). Wilting point of various species is known to be affected by the factors such as soil clay content, soil organic matter, slope of soil water characteristic curve at inflection point (i.e., s index) and fractal dimension. Therefore, its practical usefulness forms the strong basis in developing the model that quantify wilting point with respects to the deterministic factors. This study proposes the wilting point model development task based on optimization approach of Genetic programming (GP) with respect to the input variables (soil clay content, soil organic matter, s-index and fractal dimension) for various type of soils. The GP model developed is further investigated by sensitivity and parametric analysis to discover the relationships between wilting point and input variables and the dominant inputs. Based on newly developed model, it was found that wilting point increases with fractal dimension while behaves highly non-linear with respect to clay and organic content. The combined effect of the clay and organic content was found to greatly influence the wilting point. It implies that wilting point should not be generalized as usually done in literature. (C) 2017 Elsevier Ltd. All rights reserved.
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.
Miniature autonomous sensory agents (MASA) can play a profound role in the exploration of hardly accessible unknown environments, thus, impacting many applications such as monitoring of underground infrastructure or e...
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An Iterative Differential evolutionary Algorithm is proposed for the optimized identification of sets of parameter values for a system whose analysis is very time demanding and for which it is difficult to identify an...
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The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capabi...
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The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive epsilon-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.
Sequential learnable evolutionary algorithm (SLEA) provides an algorithm selection framework for solving the black box continuous design optimization problems. An algorithm pool consists of set of established algorith...
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