Evolutionary Programming Approach Research Articles

Evolutionary programming approach for securing medical images using genetic algorithm and standard deviation

Evolutionary programming approach for securing medical images using genetic algorithm and standard deviation

An evolutionary programming technique for evaluating the effect of ambient conditions on the power output of open cycle gas turbine plants - A case study of Afam GT13E2 gas turbine

In this research a novel Evolutionary Programming (EP) approach combined with a Continual Learning Neural Network (EP-CLNN) technique is proposed for the evaluation of Open Cycle Gas Turbines (OCGTs) net power output considering ambient temperature conditions. Studies were performed comparatively considering the proposed EP-CLNN approach, state-of-the-art linear regressors of several Polynomial (Poly) orders (Poly-1, Poly-2, Poly-3, and Poly-4) and three relatively small-to-big datasets. These approaches were applied to two open source datasets reported in the literature and a case study small dataset acquired from the Afam gas power station plant (GT132E2). The results showed that the reported Root-Mean Squared Error (RMSE) and mean net power estimates for the EP-CLNN are indeed promising considering the constraint of sequential continual learning prediction requirement, reduced variable (single input variable), simpler function set, and relatively small datasets. For the case of the big open source dataset (Dataset-1), the proposed EP-CLNN approach gave the best solution with least RMSE of 4.9926 MW over the linear regressors - Poly-1, Poly-2, Poly-3, and Poly-4 with RMSE estimates of 5.4609 MW, 5.2305 MW, 5.0986 MW, and 5.0980 MW respectively. For the case of the small dataset (Dataset-2), the estimated mean net powers were 32,974.0000 MW, 59140.0000 MW, and 96.2143 MW for Poly-1, Poly-2, and the EP techniques respectively; when compared to the base (reference) net mean power of 97.52 MW, the EP technique is the better one. Furthermore, for dataset 3, the EP technique gave an RMSE of 0.3381 MW while the combined solution of EP-CLNN gave a Mean Absolute Percentage Error (MAPE) consensus estimate of 0.1377 from a synthesized dataset of 20 evolved symbolic expressions. Thus, the EP-CLNN represents a promising approach to real-time gas turbine power output modeling with better accuracy and unique consensus expression capability.

Automatic global path generation for large-scale 3D scene exploration

Large-scale virtual scene exploration is still a challenging task. The novice users can easily get distracted and disorientated, which results in being lost in space. Assisted camera control technology is the most effective solution for virtual environment exploration problems which requires viewpoint computation and path planning. In this paper, a novel approach for large-scale virtual scene based on viewpoint scoring is proposed. First, the scene was adaptively divided into several meaningful and easily analyzed subregions according to the optimal view distance criterion. Second, a novel viewpoint scoring method based on visual perception and information entropy fusion was developed for optimal viewpoint determination and greedy N-Bestviewpoint selection algorithm was utilized for visual perceptibility calculation. Then evolutionary programming approach for the Traveling Salesman problem was applied for intra-subregion and inter-subregion exploring path optimization. Finally, the Cubic Hermite Curve was introduced to smoothen the inflection point on the exploration path. The experimental results demonstrate that the proposed method can effectively generate an automatic smooth, informative, aesthetic and non-intersecting path, with the characteristics of good exploring comfort, strong immersion and high scene information perception.

Modified sub-gradient based combined objective technique and evolutionary programming approach for economic dispatch involving valve-point loading, enhanced prohibited zones and ramp rate constraints

A security constrained non-convex power dispatch problem with prohibited operation zones and ramp rates is formulated and solved using an iterative solution method based on the feasible modified sub-gradient algorithm (FMSG). Since the cost function, all equality and inequality constraints in the nonlinear optimization model are written in terms of the bus voltage magnitudes, phase angles, off-nominal tap settings, and the Susceptance values of static VAR (SVAR) systems, they can be taken as independent variables. The actual power system loss is included in the current approach and the load flow equations are inserted into the model as the equality constraints. The proposed modified sub gradient based combined objective technique and evolutionary programming approach (MSGBCAEP) with as decision variable and cost function as fitness function is tested on the IEEE 30-bus 6 generator test case system. The absence of crossover operation and adoption of fast judicious modifications in initialization of parent population, offspring generation and normal distribution curve selection in EP enables the proposed MSGBCAEP approach to ascertain global optimal solution for cost of generation and emission level shown in Table 6 and displayed in Figure 2 and Figure 3 respectively.

Congestion Management Using Optimal Transmission Switching

In this paper, a multiobjective-based congestion management (CM) methodology is proposed using the optimal transmission switching (OTS) strategies considering the minimization of the total operating cost and the maximization of probabilistic reliability as two conflicting objectives. To improve the efficiency of the transmission network, this paper considers the possibility of changing the topology of the transmission system using the transmission switching strategy. The OTS can increase the economic efficiency of power dispatch with the existing infrastructure. The objective of OTS is to find the most influential lines as candidate lines for the disconnection. System operators can change the network topology to increase the transfer capacity, to improve voltage profiles, and also to improve the reliability of the power system. In this paper, the loss of load probability reliability index is used to indicate the probability that an electrical power system is not able to serve the prescribed load within the concerned period of time. Here, the transmission lines that should be switched are determined by using the security-constrained ac optimal power flow. The proposed optimization problem is solved using the hybrid of evolutionary and stochastic programming approaches. The output of the proposed OTS is to find the transmission lines that have to be switched so that the congestion in the system can be relieved. A multiobjective optimization approach is used to generate the Pareto optimal solutions. The suitability and effectiveness of the proposed CM approach have been examined on IEEE 30- and 300-bus test systems.

Assessment of Resource and Forecast Modeling of Wind Speed through An Evolutionary Programming Approach for the North of Tehuantepec Isthmus (Cuauhtemotzin, Mexico)

This work studies the characteristics of the wind resource for a location in the north zone of Tehuantepec isthmus. The study was conducted using climatic data from Cuauhtemotzin, Mexico, measured at different altitudes above the ground level. The measured data allowed establishing the profile of wind speeds as well as the analysis of its availability. Analysis results conclude that the behavior of the wind speed presents a bimodal distribution with dominant northeast wind direction (wind flow of sea–land). In addition, the area was identified as feasible for the use of low speed power wind turbines. On the other hand, the application of a new approach for very short-term wind speed forecast (10 min) applying multi-gene genetic programming and global sensitivity analysis is also presented. Using a computational methodology, an exogenous time series with fast computation time and good accuracy was developed for the forecast of the wind speed. The results presented in this work complement the panorama for the evaluation of the resource in an area recognized worldwide for its vast potential for wind power.

Basic Evolutionary Approach to the Traveling Salesman Problem

Evolutionary programming (EP) is a metaheuristic method developed as an alternative approach to artificial intelligence. The aim of this paper is to bring an introduction to EP algorithms through the implementation of the basic D. B. Fogel’s Evolutionary Programing approach of 1988 and the emulation of his results in order to analyze the performance of the evolutionary programming method on solving a benchmark test case. The EP approach is implemented thru a simple simulation of natural evolution and the allowance of probabilistic survival of individuals. The novelty of this paper relies on testing the algorithm performance in some problems of well-known benchmark instances of the Traveling Salesman Problem, where that 1988 evolutionary approach was not tested. The reproduction of 1988 D. B. Fogel’s approach was possible, the found average error of this method for 200000 offspring applied to the benchmark instances was found to be in the order of the 10%.

An evolutionary programming approach for securing medical images using watermarking scheme in invariant discrete wavelet transformation

The importance of digital images in the field of health care made major impact in the recent years. There is need for protecting the medical images from unauthorized usage and watermarking serves well in this situations. Digital medical Image watermarking is the procedure of protecting the medical image content by inserting the watermark into it. The major objective of the image watermarking technique is to develop an algorithm with high imperceptibility. To achieve this, this paper proposed the image watermarking algorithm in wavelet transformation (IDWT) using the singular value decomposition (SVD) and particle swarm optimization (PSO). The improved DWT is applied to the medical image to retrieve the invariant wavelet domain. The watermark is inserted in to the selected region by modifying the values of the coefficients in the image using threshold function. The scaling factors are optimized using the PSO algorithm. The performance of the proposed model is evaluated using the existing schemes similar to the properties of the proposed model. The normalized coefficient (NC) and Peak Signal to Noise Ratio (PSNR) is considered to evaluate the similarity between the medical image and watermarked medical image. The proposed algorithm showed improved performance in terms of imperceptibility and robustness.

Efficient fractal-based mutation in evolutionary algorithms from iterated function systems

In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs). The new mutation procedure proposed consists of considering a set of IFS which are able to generate fractal structures in a two-dimensional phase space, and use them to modify a current individual of the EP algorithm, instead of using random numbers from different probability density functions. We test this new proposal in a set of benchmark functions for continuous optimization problems. In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps. We also include a discussion on the IFS-based mutation in a real application of Tuned Mass Dumper (TMD) location and optimization for vibration cancellation in buildings. In both practical cases, the proposed EP with the IFS-based mutation obtained extremely competitive results compared to alternative classical mutation operators.

Evolutionary Programming Approach for Deregulated Power Systems to Optimal Positioning of FACTS Devices

Evolutionary Programming Approach for Deregulated Power Systems to Optimal Positioning of FACTS Devices

A Computational Intelligence Solution for Power Flow Automatic Adjustment

With power system growing more enormous and complicated, adjusting operating point to change the power of cross-section to the given value becomes unhandy and tedious for the system operators. In this paper, a power flow automatic adjustment strategy based on the computational intelligence technique is proposed. With sensitivity analysis based on direct current power flow (DCPF) model, the generators are grouped and filtered to decrease calculating amount. An improved evolutionary programming approach is applied to search the combination of active power and voltage magnitude of generators to achieve the automatic adjustment of the given cross-section power flow. The simulations are performed on the IEEE 3-machine-9-bus test case to illustrate the rationality and validity of the proposed strategy.

An Effective Economic Load Dispatch Methodology for Power System using Improved TANAN's Algorithm

Exponentially increasing pollution due to power industries is the major problem in the power industrial sector. The solution to the problem can be solved by optimal fuel usage in power industries. Though the renewable energy substitutes the pollution, it cannot be taken as the alternate for full power generation. More than 80% of the power production is served through non-renewable energy resource. Waste management in case of nuclear power sector is a major issue. The fuel usage is minimized by effectively considering the power demand as the factor. The proposed methodology is considered as multi objective problem and tested in IEEE 30 bus system. The pollution gases emitted by the fuel is considered as the emission constraint in order to make the proposed system a more eco-friendly one. The proposed algorithm is compared with λ-iteration, evolutionary programming approach & Genetic algorithms. The proposed algorithm effectively reduces the loss and fuel cost by 0.02 times and the powerloss is reduced by 2%. The gas emission by the proposed algorithm is reduced by 1% when compared to the RGAalgorithm.

Evolutionary-based return forecasting with nonlinear STAR models: evidence from the Eurozone peripheral stock markets

Traditional linear regression and time-series models often fail to produce accurate forecasts due to inherent nonlinearities and structural instabilities, which characterize financial markets and challenge the Efficient Market Hypothesis. Machine learning techniques are becoming widespread tools for return forecasting as they are capable of dealing efficiently with nonlinear modeling. An evolutionary programming approach based on genetic algorithms is introduced in order to estimate and fine-tune the parameters of the STAR-class models, as opposed to conventional techniques. Using a hybrid method we employ trading rules that generate excess returns for the Eurozone southern periphery stock markets, over a long out-of-sample period after the introduction of the Euro common currency. Our results may have important implications for market efficiency and predictability. Investment or trading strategies based on the proposed approach may allow market agents to earn higher returns.

Comparison of EPSO, PSO and EP Approaches in Transmission Loss Minimization in Power System Network

The paper presents a comparison of Computational Intelligence techniques are Evolutionary Programming Swarm Optimization (EPSO), Particle Swarm Optimization (PSO), Evolutionary Programming (EP) to optimal placement and sizing of Static Var Compensator. The technique has been implemented to minimize the transmission loss and improve the voltage profile of the system. Simulation performed on standard IEEE 118-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

Network anomaly detection by continuous hidden markov models: An evolutionary programming approach

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.

Performance comparison of evolutionary programming and differential evolution approaches for social welfare maximization by placement of multi type FACTS devices in pool electricity market

In restructuring power markets, the money matters of demand and supply governs the prices at each bus with rigid constraints imposed in transmission line. The market operators can misuse the “market power” and make additional profit. It eventually hinders the growth of restructured power market and minimizes the social welfare. Hence Flexible AC Transmission System (FACTS) devices are considered one such technology that helps in improving the social welfare. In this paper, the new methodology for proper placement of FACTS devices in the restructured electricity market is proposed. An efficient and reliable evolutionary programming (EP) and differential evolution (DE) techniques based economic dispatch for pool electricity market is used to improve the social welfare. These techniques are developed for both the cases with and without FACTS devices and the results are compared. Utilization of these FACTS devices helps in “maximizing the benefit” and “minimizing the cost” and also helps in reducing the total number of overloads, excess power flow and severity of overloading. The standard data of sample 5 bus, three generators and two customers system has been taken into account and simulated with aid of MAT-lab software and comparative results are obtained.

Multi Population Evolutionary Programming Approach for Distributed Generation Installation

This paper describes the impact of the distribution development in order to identify optimum location and sizing for distribution generation (DG) in power system network. Highly demand on the load will lead to in control power distributed by introducing power losses via transmitting the power. Therefore, small-scale electricity generation is required to ensure the large power generated can be used for particular location to minimize losses. In addition, the implementation of distribution generation will slightly reduce the capital cost compared to existing power plant due to space, speed and source required. Thus, proper DG location will significantly enhance the impact on the power flow analysis by considering the source of energy easily obtained. This study will be conducted by using Matlab and the proposed algorithm (MPEP)will be applied on IEEE 30 buses radial distribution system network. As results, the DG is located at optimal locations and sizes rely on the losses consume in various type of DGs technology system used in the network. On the others hand, the condition and location DG itself will generate the optimal power contribution depends on the design strategies that have been implemented.

Multi Population Evolutionary Programming Approach for Distributed Generation Installation

Multi Population Evolutionary Programming Approach for Distributed Generation Installation

Evolutionary Programming based Optimal Wind and Thermal Generation Dispatch with valve point effect

This paper discusses an Evolutionary Programming Approach to obtain optimal wind and thermal generation dispatch. As wind power penetrations increase in current power systems, its impact to conventional thermal unit should be investigated due to the intermittency and unpredictability of wind power generation. Development of better wind thermal coordination economic dispatch is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. In this paper Evolutionary Programming (EP) is utilized to coordinate the wind and thermal generation dispatch and to minimize the total production cost in the economic dispatch considering wind power generation and valve effect of thermal units. Different test system incorporating one wind power plant is utilized for numerical simulation. Different simulations with and without wind power production are simulated. Simulation result shows the effect of wind power generation in reducing total fuel cost.

Hybrid Evolutionary Particle Swarm Optimization for the Coordination of Wind and Thermal Generation Dispatch

Development of better wind and thermal coordination dispatch is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. In this paper hybrid Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) approach is utilized to coordinate the wind and thermal generation dispatch and to minimize the total production cost considering wind power generation and valve effect of thermal units. Numerical studies have been performed for three different test systems, i.e., six, thirteen and forty generating unit systems. The simulation results demonstrate the effectiveness of the proposed approach and shows the effect of wind power generation in reducing the total fuel.