Information security is an important and growing need. The most common schemes used for detection systems include pattern-or signature-based and anomaly-based. Anomaly-based schemes use a set of metrics, which outline...
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Information security is an important and growing need. The most common schemes used for detection systems include pattern-or signature-based and anomaly-based. Anomaly-based schemes use a set of metrics, which outline the normal system behavior and any significant deviation from the established profile will be treated as an anomaly. This paper contributes with an anomaly-based scheme that monitors the bandwidth consumption of a subnetwork, at the Universidad Michoacana, in Mexico. A normal behavior model is based on bandwidth consumption of the subnetwork. The presence of an anomaly indicates that something is misusing the network (viruses, worms, denial of service, or any other kind of attack). This work also presents a scheme for an automatic architecture design and parameters optimization of Hidden Markov Models (HMMs), based on evolutionary programming (EP). The variables to be used by the HMMs are: the bandwidth consumption of network (IN and OUT), and the associated time where the network activity occurs. The system was tested with univariate and bivariate observation sequences to analyze and detect anomaly behavior. The HMMs, designed and trained by EP, were compared against semi-random HMMs trained by the Baum-Welch algorithm. On a second experiment, the HMMs, designed and trained by EP, were compared against HMMs created by an expert user. The HMMs outperformed the other methods in all cases. Finally, we made the HMMs time-aware, by including time as another variable. This inclusion made the HMMs capable of detecting activity patterns that are normal during a period of time but anomalous at other times. For instance, a heavy load on the network may be completely normal during working times, but anomalous at nights or weekends.
This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constrain...
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This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constraints. The proposed method does not use a penalty function and it builds surrogates for the objective and constraint functions. Each parent generates a large number of trial offspring in each generation. Then, the surrogate functions are used to identify the trial offspring that are predicted to be feasible with the best predicted objective function values or those with the minimum number of predicted constraint violations. The objective and constraint functions are then evaluated only on the most promising trial offspring from each parent, and the method proceeds in the same way as in a standard EP. In the numerical experiments, the type of surrogate used to model the objective and each of the constraint functions is a cubic radial basis function (RBF) augmented by a linear polynomial. The resulting RBF-assisted EP is applied to 18 benchmark problems and to an automotive problem with 124 decision variables and 68 black-box inequality constraints. The proposed method is much better than a traditional EP, a surrogate-assisted penalty-based EP, stochastic ranking evolution strategy, scatter search, and CMODE, and it is competitive with ConstrLMSRBF on the problems used.
The optimization of bioreactor operations towards swainsonine production was performed using an artificial neural network coupled evolutionary program (EP)-based optimization algorithm fitted with experimental one-fac...
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The optimization of bioreactor operations towards swainsonine production was performed using an artificial neural network coupled evolutionary program (EP)-based optimization algorithm fitted with experimental one-factor-at-a-time (OFAT) results. The effects of varying agitation (300-500 rpm) and aeration (0.5-2.0 vvm) rates for different incubation hours (72-108 h) were evaluated in bench top bioreactor. Prominent scale-up parameters, gassed power per unit volume (P (g)/V (L), W/m(3)) and volumetric oxygen mass transfer coefficient (K (L) a, s(-1)) were correlated with optimized conditions. A maximum of 6.59 +/- A 0.10 mu g/mL of swainsonine production was observed at 400 rpm-1.5 vvm at 84 h in OFAT experiments with corresponding P (g)/V-L and K (L) a values of 91.66 W/m(3) and 341.48 x 10(-4) s(-1), respectively. The EP optimization algorithm predicted a maximum of 10.08 mu g/mL of swainsonine at 325.47 rpm, 1.99 vvm and 80.75 h against the experimental production of 7.93 +/- A 0.52 mu g/mL at constant K (L) a (349.25 x 10(-4) s(-1)) and significantly reduced P (g)/V (L) (33.33 W/m(3)) drawn by the impellers.
In evolutionary programming, each parent has two pieces of information: location and cost. The cost of parent specifies whether its location is suitable for breeding offspring or not. If the parent's cost is an ac...
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In evolutionary programming, each parent has two pieces of information: location and cost. The cost of parent specifies whether its location is suitable for breeding offspring or not. If the parent's cost is an acceptable value, producing offspring (or even steering other offspring) in the parent's area is advisable. This information is used in estimating the region of global minimum;then, using the state feedback controller, the offspring is steered to the optimal region. In the proposed method, the cost and coordination of parents have been used for breeding more elite individuals. Many (sixty-five) well-known cost functions have been selected from different references to reveal the pros and cons of our algorithms. In the first stage, the proposed algorithm has been compared inside the EP family. This stage shows promising results for the proposed algorithm. In the second stage, comparison has been performed out of the EP frontiers in which algorithms are state-of-the-art in the optimization field, and are well known inside and outside of their own families. The statistic test has been performed among the algorithms. CPU time and its sensitivity to variable bounds, population and cost function dimensions have been studied. Finally, the proposed method is used in designing the nonlinear minimum variance controller for CSTR (Continuous Stirred-Tank Reactor) benchmark system.
Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an effective method ba...
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Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an effective method based on evolutionary programming (EP) and Genetic Algorithm (GA) to identify the switching operation plan for feeder reconfiguration and distributed generation size simultaneously. The main objectives of this paper are to gain the lowest reading of real power losses, upgrade the voltage profile in the system as well as satisfying other operating constraints. Their impacts on the network real power losses and voltage profiles are investigated. A comprehensive performance analysis is carried out on IEEE 33-bus radial distribution systems to prove the efficiency of the proposed methodology. The test result on the system showed the power loss reduction, and voltage profile improvement of the EP is superior to the GA method.
In this paper, an evolutionary programming (EP) based technique has been presented for the optimal placement of distributed generation (DG) units energized by renewable energy resources (wind and solar) in a radial di...
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In this paper, an evolutionary programming (EP) based technique has been presented for the optimal placement of distributed generation (DG) units energized by renewable energy resources (wind and solar) in a radial distribution system. The correlation between load and renewable resources has been nullified by dividing the study period into several segments and treating each segment independently. To handle the uncertainties associated with load and renewable resources, probabilistic techniques have been used. Two operation strategies, namely "turning off wind turbine generator" and "clipping wind turbine generator output", have also been adopted to restrict the wind power dispatch to a specified fraction of system load for system stability consideration. To reduce the search space and thereby to minimize the computational burden, a sensitivity analysis technique has been employed which gives a set of locations suitable for DG placement. For the proposed EP based approach, an index based scheme has also been developed to generate the population ensuring the feasibility of each individual and thus considerably reducing the computational time. The developed technique has been applied to a 12.66-kV, 69-bus distribution test system. The solutions result in significant loss reduction and voltage profile improvement.
Optimum path planning is a fundamental necessity for the successful functioning of a mobile robot in industrial applications. This research work investigates the application of the artificial bee colony (ABC) approach...
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Optimum path planning is a fundamental necessity for the successful functioning of a mobile robot in industrial applications. This research work investigates the application of the artificial bee colony (ABC) approach, probabilistic roadmap (PRM) method, and evolutionary programming (EP) algorithm to tackle the issue of single and multi-robot path planning in partially known or unknown industrial complex environments. Conventional techniques depend on external factors such as delay of information from one bee's stage to another for selecting neighbour food points. Due to this, its efficiency is comparatively low and might result in longer runtimes. To address these challenges, a novel hybrid framework based on ABC-PRM-EP has been introduced. Firstly, a suboptimal initial feasible path is attained by a new framework (ABC-PRM) within the mobile robot sensor detection range. Then, EP performs refinement of that attained suboptimal path to provide a short and optimum path. Also, a multi-robot collaboration strategy has been introduced based on the concept of hold-up. A number of comparative studies have been conducted in three different test scenarios with different complexity to validate the proposed framework efficiency and performance. Different performance indices such as path length (m), smoothness (rad), collision safety value, success rate, processing time (s), and convergence speed have been measured to validate the effectiveness of the proposed framework. The comparative analysis obtained from these test scenarios indicates that the proposed framework outperforms conventional ABC, ABC-EP and HPSO-GWO-EA, while performing path planning.
Heavy equipment is a crucial resource in the construction industry. Recent studies have shown that analyzing the sound patterns generated by construction machinery can be an effective way to monitor their performance ...
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Heavy equipment is a crucial resource in the construction industry. Recent studies have shown that analyzing the sound patterns generated by construction machinery can be an effective way to monitor their performance and detect potential operational issues. However, construction jobsites are complex environments that require consideration of multiple factors when creating an audio-based model. To perform efficient audio-based modeling of jobsites, it is essential to optimize the number and placement of microphones to capture the sound emitted by all the operating machines and achieve optimal sound quality. To address this challenge, we developed two optimization methods: (a) an integer programming model that guarantees finding the optimal placement of microphones, and (b) an evolutionary programming model, a heuristic approach more suited to larger problem instances. We evaluated the effectiveness of these models in five different case studies from construction jobsites. Our results showed that the developed models require a reasonable number of microphones to achieve the desired sound quality, demonstrating their satisfactory performance. Notably, both approaches exhibited similar performance in terms of the required number of microphones needed to cover all the machinery.
Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments i...
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Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments is crucial. However, existing particle swarm optimization (PSO) algorithms struggle with these problems as they fail to consider UAV dynamics, resulting in many infeasible solutions and poor convergence to optimal solutions. To address these challenges, we propose a spherical vector-based adaptive evolutionary particle swarm optimization (SAEPSO) algorithm. This algorithm, based on spherical vectors, directly incorporates UAV dynamic constraints and introduces improved tent map and reverse learning to enhance the diversity and distribution of initial solutions. Additionally, dynamic nonlinear and adaptive factors are integrated to balance exploration and exploitation capabilities. To avoid local optima in highly complex environments, we propose an adaptive acceleration strategy for poor particles, and an evolutionary programming strategy is incorporated to further improve the optimization capability. Finally, we conducted comparative studies and in six benchmark scenarios with varying threat levels, and the results demonstrated that the proposed algorithm outperforms others in the initial solution effectiveness, the final solution accuracy, convergence stability, and scalability.
This paper presents a novel proximal planning and control (PPC) formulation for an unmanned ground vehicle (UGV) which is affected by skidding and slip disturbances. The control approach also considers the presence of...
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This paper presents a novel proximal planning and control (PPC) formulation for an unmanned ground vehicle (UGV) which is affected by skidding and slip disturbances. The control approach also considers the presence of moving and static obstacles in the context of operation. The PPC technique is divided into three parts;first, a nonlinear model predictive control (NMPC) based path-planner is designed to periodically generate an updated feasible trajectory for reaching the goal pose, under the constraint of avoiding collisions with dynamic and static objects which are present in the context. In particular, a proximal averaged Newton-type method for optimal control (PANOC) is used to implement NMPC. Second, the velocity commands are produced via evolutionary programming (EP) based on kinematic control (KC). Third, a dynamic control process with an extended state observer (ESO) is introduced to estimate disturbances whose magnitudes are unknown but bounded. Finally, to verify the performance of the proposed scheme, simulations are performed for the platform operating in the presence of static obstacles (SO) and moving obstacles (MO), whose trajectories may be nonlinear and difficult to be accurately predicted. Additionally, we have investigated and confirmed that the proposed PPC is able to operate in real time under limited CPU processing resources.
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