Evolutionary Programming Technique Research Articles

Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)

Plain concrete is weak against tension and has low Tensile Strain Capacity (TSC) which significantly affects its long-term performance. To overcome this issue, Engineered Cementitious Composites (ECC) were developed by incorporating polymer fibres in the cement matrix which increases ductility and provides higher TSC than plain concrete and they have emerged as a viable alternative to brittle plain concrete. This study is conducted in an attempt to develop empirical prediction models for TSC prediction of ECC without requiring extensive experimental procedures. For this purpose, two evolutionary programming techniques known as Multi Expression Programming (MEP), Gene Expression Programming (GEP) along with two boosting-based techniques: AdaBoost and Extreme Gradient Boosting (XGB) were developed using data collected from published literature. The gathered dataset had seven input parameters including water-to-binder ratio, sand, fibre content, cement, fly ash, superplasticizer, and age etc. and only one output parameter i.e., TSC. The error assessment of developed models was done using correlation coefficient, Mean Absolute Error (MAE), and Objective Function (OF) etc. and the error comparison showed that XGB has the highest accuracy having the least OF value of 0.081 as compared to 0.11 of AdaBoost, 0.13 of GEP, and 0.16 of MEP. Shapley additive analysis was conducted on the XGB model since it proved to be the most accurate, and the results highlighted that fibre content, age, and water-to-binder ratio are the most important features to predict TSC of ECC.

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

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.

Fixed head hydrothermal scheduling considering uncertainty of load demand and renewable energy sources

AbstractThis manuscript suggests quasi‐oppositional fast convergence evolutionary programming (QOFCEP) technique to determine short‐term generation scheduling of fixed head hydrothermal system comprising pumped storage hydro plants (PSHPs), solar PV plants (SPVPs) and wind turbine generators (WTGs) with demand response program (DRP). PSHPs are used with fixed head hydro power plants (FHPPs), thermal generating units (TGUs), SPVPs and WTGs to operate system conveniently. Uncertainties related to renewable generations and load demands have been considered. Self‐adaptive tent mapped scenarios of uncertain parameters are generated within higher level of confidence space to deal with the system uncertainties. DRP is applied to level the load demand curve and to enhance system flexibility and reduce operational cost. Numerical results of typical test system are matched with those attained by fast convergence evolutionary programming (FCEP) and evolutionary programming (EP).

Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature

The elevated temperature severely influences the mixed properties of concrete, causing a decrease in its strength properties. Accurate proportioning of concrete components for obtaining the required compressive strength (C-S) at elevated temperatures is a complicated and time-taking process. However, using evolutionary programming techniques such as gene expression programming (GEP) and multi-expression programming (MEP) provides the accurate prediction of concrete C-S. This article presents the genetic programming-based models (such as gene expression programming (GEP) and multi-expression programming (MEP)) for forecasting the concrete compressive strength (C-S) at elevated temperatures. In this regard, 207 C-S values at elevated temperatures were obtained from previous studies. In the model’s development, C-S was considered as the output parameter with the nine most influential input parameters, including; Nano silica, cement, fly ash, water, temperature, silica fume, superplasticizer, sand, and gravels. The efficacy and accuracy of the GEP and MEP-based models were assessed by using statistical measures such as mean absolute error (MAE), correlation coefficient (R2), and root mean square error (RMSE). Moreover, models were also evaluated for external validation using different validation criteria recommended by previous studies. In comparing GEP and MEP models, GEP gave higher R2 and lower RMSE and MAE values of 0.854, 5.331 MPa, and 0.018 MPa respectively, indicating a strong correlation between actual and anticipated outputs. Thus, the GEP-based model was used further for sensitivity analysis, which revealed that cement is the most influencing factor. In addition, the proposed GEP model provides simple mathematical expression that can be easily implemented in practice.

A differential evolutionary chromosomal gene expression programming technique for electronic nose applications

The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.

"Found in Translation": An Evolutionary Framework for Auditory-Visual Relationships.

The development of computational artifacts to study cross-modal associations has been a growing research topic, as they allow new degrees of abstraction. In this context, we propose a novel approach to the computational exploration of relationships between music and abstract images, grounded by findings from cognitive sciences (emotion and perception). Due to the problem's high-level nature, we rely on evolutionary programming techniques to evolve this audio-visual dialogue. To articulate the complexity of the problem, we develop a framework with four modules: (i) vocabulary set, (ii) music generator, (iii) image generator, and (iv) evolutionary engine. We test our approach by evolving a given music set to a corresponding set of images, steered by the expression of four emotions (angry, calm, happy, sad). Then, we perform preliminary user tests to evaluate if the user's perception is consistent with the system's expression. Results suggest an agreement between the user's emotional perception of the music-image pairs and the system outcomes, favoring the integration of cognitive science knowledge. We also discuss the benefit of employing evolutionary strategies, such as genetic programming on multi-modal problems of a creative nature. Overall, this research contributes to a better understanding of the foundations of auditory-visual associations mediated by emotions and perception.

Load shedding scheme based metaheuristic technique for power system controlled islanding

<p>Power system-controlled islanding is one of the mitigation techniques taken to prevent blackouts during severe outage. The implementation of controlled islanding will lead to the formation of few islands, that can operate as a stand-alone island. However, some of these islands may not be balanced in terms of generation and load after the islanding execution. Therefore, a good load shedding scheme is required to meet the power balance criterion so that it can operate as a balanced stand-alone island. Thus, this paper developed a load shedding scheme-based metaheuristics technique namely modified discrete evolutionary programming (MDEP) technique to determine the optimal amount of load to be shed in order to produce balanced stand-alone islands. The developed load shedding scheme is evaluated and validated with two other load shedding techniques which are conventional EP and exhaustive search techniques. The IEEE 30-bus and 39-bus test systems were utilized for this purpose. The results proves that the load shedding based MDEP technique produces the optimal amount of loads to be shed with shortest computational time as compared with the conventional EP and exhaustive search techniques.</p>

A Non-Convex Economic Dispatch Problem with Point-Valve Effect Using a Wind-Driven Optimisation Approach.

This study presents the efficiency of the wind-driven optimisation (WDO) approach in solving non-convex economic dispatch problems with point-valve effect. The best economic dispatch for a power system is one wherein the system can generate energy at a low cost. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss. The WDO optimisation technique is developed based on the concept of natural wind movement, which serves as a stabiliser to equalise the inequality of air pressure in the atmosphere. One major advantage of WDO over other techniques is its search accuracy. The proposed algorithm has been implemented in two systems, namely, the 10-generator and 40-generator systems. Both systems were tested in a Matlab environment. To highlight the capabilities of WDO, the results using this proposed technique are compared with the results obtained using flower pollination algorithm, moth flame optimisation, particle swarm optimisation and evolutionary programming techniques to determine the efficiency of the proposed approach in solving economic dispatch. The simulation results show the capability of WDO in determining the optimal power generation value with minimum generation cost and low rate of power loss.

Design of optimal multi-objective-based facts component with proportional-integral-derivative controller using swarm optimization approach

This study proposes a multi-objective-based swarm intelligence method to improve angle stability. An optimization operation with single objective function only improves the performance of one perspective and ignores the other. The combination of two objective functions which derived from real and imaginary components of eigenvalue are able to provide better performance beyond the optimization capabilities of single objective function. Tested using MATLAB, the simulation is performed using a single machine attached to the infinite bus (SMIB) system equipped with static var compensator (SVC) that attached with PID controller (SVC-PID). The objective of this experiment is to explore the excellent parameters in SVC-PID to produce a more stable system. In addition to the comparison of objective functions, this study also compares particle swarm optimization (PSO) capabilities with evolutionary programming (EP) and artificial immune system (AIS) techniques.

Immune-Commensal-Evolutionary Programming for solving non-smooth/non-convex economic dispatch problem

This paper presents the development of a new hybrid optimization technique termed as Immune-Commensal-Evolutionary Programming (ICEP) and its implementation to solve non-smooth/ non-convex Economic Dispatch (ED) problem. ICEP is developed with the objective to overcome the drawbacks of single optimization techniques like evolutionary programming (EP), Artificial Immune System (AIS) and Symbiotic Organisms Search (SOS). The idea of developing this ICEP technique is to gather the strengths from the three single optimization techniques: EP, AIS and SOS to form a new hybrid technique that can solve non-smooth/ non-convex ED problem accurately. This hybrid technique has better performance in finding the global optima of non-smooth/ non-convex ED problem compared to the single optimization techniques. The typical drawback of the single optimization techniques is immature convergence, especially EP and AIS techniques. ICEP can avoid this from happening by thoroughly directing the searching process to its global optima. The proposed ICEP technique has been tested on the IEEE 30-Bus Reliability Test System (RTS) and IEEE 57-Bus Reliability Test System (RTS) with three case studies. It is found that ICEP is superior than EP and AIS in producing better non-smooth/ non-convex ED solution.

Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system

Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and system loss increment. A new optimization technique termed as embedded differential evolutionary programming (EDEP) is proposed, which integrates the traditional differential evolution (DE) and evolutionary programming (EP). Consequently, EDEP was for solving optimizations problem in power system through the tap changer optimizations scheme. Results obtained from this study are significantly superior compared to the traditional EP with implementation on the IEEE 30-bus reliability test system (RTS) for the loss minimization scheme.

Effect of magnetized mixing water on the fresh and hardened state properties of steel fibre reinforced self-compacting concrete

The objective of this research is to investigate the effect of magnetized water on the fresh and hardened properties of self-compacting concrete (SCC) reinforced with different ratios of steel fibres. For this purpose, a total of 12 mixes were produced with different contents of steel fibres (0%, 0.35%, 1%, and 1.65% by volume of concrete) and the mixing water passed through a permanent magnetic field (with a strength of 0.65 T), 1 and 15 times, at a flow speed of 0.75 m/s. Slump flow, V-funnel, compressive strength, splitting tensile strength and flexural strength tests, as well as a statistical analysis, were conducted. The results of the workability, compressive strength, splitting tensile strength and flexural strength tests indicate that the SCC mixes are considerably enhanced by magnetizing the mixing water. Finally, a novel evolutionary programming technique called multi-expression programming was used to generate mathematical models.

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

This study discusses the evaluation of oscillatory stability based on the synchronizing K s and damping K d torque coefficients for a single-machine system connected to an infinite bus (SMIB). Particle swarm optimization (PSO) technique is used to determine K s and K d values and subsequently identify the oscillatory stability conditions in the SMIB. The ability of PSO is compared with those of evolutionary programming (EP) techniques and artificial immune system (AIS). The least square (LS) method is selected as the benchmark for K s and K d values determined by PSO, EP, and AIS. Simulation results show that PSO successfully estimated K s and K d values closest to LS compared with EP and AIS. PSO also uses lower computational time compared with those of the two other techniques. The proposed technique is suitable for solving oscillatory stability problems based on the determination of eigenvalues and minimum damping ratio.

Multi cases optimal reactive power dispatch using evolutionary programming

<span>Evolutionary Programming (EP) is one of many types in Evolutionary Computation (EC) that used for optimization process. EP technique is used to find the optimal reactive power dispatch (ORPD) since it is one of the accessible options schemes that can be used on the system as a reactive power support. Sometimes, it is not necessary to operate all generators in order to perform ORPD to in achieve the objectives. Also, increment of reactive power load to the system will cause voltage decomposes with the increase in transmission loss in the system. Therefore, the proposed method decides the best grouping of generators that should be operated in system by bearing in mind the transmission loss reduction. ORPD will be used to minimize the transmission loss as well the increasing reactive power loading. This method conducted on IEEE 30-bus Test System with multi cases scenario. The best combination of operating generators determined and the transmission loss after optimization is smaller compared to the transmission loss before optimization resulted.</span>

Computational Intelligence Based Technique for Multi-DG Installation in Transmission System

The increasing electricity demand in transmission system has caused the power transmission system experiencing stress condition. This phenomenon has forced the system to need urgent additional supply to maintain system adequacy, in particular the de-regulated power system environment. Distributed generation (DG) has been identified as one of the possible solutions to address this issue. DG installation has the capability to reduce transmission loss and improve the voltage profile. This paper presents evolutionary programming (EP) technique for optimizing the sizing and locations in DG installation. In this study, several DGs have been installed to address the voltage profile improvement and loss minimization; implemented on the IEEE 30-Bus Reliability Test System (RTS). Results obtained from the study revealed that, installation of multi-DGs in a transmission system has significantly minimized the transmission loss along with voltage profile improvement.

Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;" lang="EN-US">The growth of computational intelligence technology has witnessed its application in numerous fields. Power system study is not left behind as far as computational intelligence trend is concerned. In power system community, optimization process is one of the crucial efforts for most remedial action to maintain the power system security. Basically, power scheduling refers to prior to fact action(such as scheduling generators to generate certain powers for next week). Power scheduling process is one of the most important routines in power systems. Scheduling of generators in a power transmission system is an important scheme; especially its offline studies to identify the security status of the system. This determines the cost effectiveness in power system planning. This paper investigates the performance of multi-verse based evolutionary programming(lowest EP) technique in the application of power system scheduling to ensure loss is gained by the system. Losses in the system can be controlled through this implementation which can be realized through the validation on a chosen reliability test system as the main model. Validation on </span><span style="font-size: 10.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">IEEE 30-Bus Reliability Test System resulted that both techniques are reliable and robust in addressing this issue.</span><p class="MsoTitle"> </p>

Intelligent swarm-based optimization technique for oscillatory stability assessment in power system

<p>This paper discussed the prediction of oscillatory stability condition of the power system using a particle swarm optimization(PSO) technique. Indicators namely synchronizing(<em>K<sub>s</sub></em>)and damping(<em>K<sub>d</sub></em>) torque coefficients is appointed to justify the angle stability condition in a multi-machine system. PSO is proposed and implemented to accelerate the determination of angle stability. The proposed algorithm has been confirmed to be more accurate with lower computation time compared with evolutionary programming(EP) technique. This result also supported with other indicators such as eigenvalues determination, damping ratio and least squares method. As a result, proposed technique is achievable to determine the oscillatory stability problems.</p>

Embedded adaptive mutation evolutionary programming for distributed generation management

<span>Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.</span>

Optimal SVC allocation via symbiotic organisms search for voltage security improvement

It is desirable that a power system operation is in a normal operating condition. However, the increase of load demand in a power system has forced the system to operate near to its stability limit whereby an increase in load poses a threat to the power system security. In solving this issue, optimal reactive power support via SVC allocation in a power system has been proposed. In this paper, Symbiotic Organisms Search (SOS) algorithm is implemented to solve for optimal allocation of SVC in the power system. IEEE 26 Bus Reliability Test System is used as the test system. Comparative studies are also conducted concerning Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) techniques based on several case studies. Based on the result, SOS has proven its superiority by producing higher quality solutions compared to PSO and EP. The results of this study can benefit the power system operators in planning for optimal power system operations.