Evolutionary Programming Research Articles

Thermal unit commitment solution using an improved Lagrangian Relaxation

An improved Lagrangian relaxation (LR) solution to the thermal unit commitment problem (UCP) is proposed in this paper. The algorithm is characterized by: (1) a new Matlab function to determine the optimal path of the dual problem, (2) new initialization procedure of Lagrangian multipliers, based on both unit and time interval classification, (3) a flexible adjustment of Lagrangian multipliers, and (4) a dynamic search for uncertain stage scheduling, using a Lagrangian relaxation - dynamic programming method (LR-DP). After the LR best feasible solution is reached, and when identical or similar units exist, a unit decommitment is used to adjust the solution. The proposed algorithm is tested and compared to conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA), and genetic algorithm based on unit characteristic classification (GAUC) on systems with the number of generating units in the range of 10 to 100. The total system production cost of the proposed algorithm is less than the others especially for the larger number of generating units. Computational time was found to increase almost linearly with system size, which is favorable for large-scale implementation.

Оптимизация мероприятий технической эксплуатации оросительных систем методами искусственного интеллекта

The purpose of the study is to develop a constructive solution for choosing priority objects of repair and restoration works using artificial intelligence methods in conditions of limited budget financing of operational measures of irrigation systems. The paper uses methods of system analysis and mathematical modeling, including binary variables for discrete optimization using evolutionary genetic programming methods. Based on the analysis of management decision support by means of mathematical support, the use of optimization methods for predicted impacts is justified, including the choice of discrete options to increase the functionality and effectiveness of planned technical operation measures. The allocation of limited financial resources is carried out on the model of multi-criteria optimization, including minimizing irrigation water losses, while increasing the area of irrigated land and increasing the financial indicators of the water management organization, which improves the quality of management impacts. The practical significance of research is determined by the development of innovative tools to solve the problem of allocating limited resources for carrying out repair and restoration work of the municipal water management complex using artificial intelligence methods. The approbation of the proposed solutions, carried out on the materials of the operation service of the State Budgetary Institution of the Republic of Crimea “Crimean Department of Water Management and Melioration”, proved the expediency of large-scale implementation of methods for quantitative assessment of management decisions. The technical and economic indicators of the planned activities correspond to the expected values and ensure the fulfillment of the following requirements: completeness, ensuring that they provide the necessary and sufficient information for decision-making; availability of reliable sources of reliable and accessible data for the information content of indicators and criteria.

Optimum dynamic dispatch of clean water, methane, electricity and heat considering carbon capture

This paper suggests a novel clean water, natural gas, heat and power generation system using wind turbine generators, PVT panels, bioenergy power plants, micro-hydro power plants, micro-cogeneration units (MCUs), battery energy storage system, thermal energy storage system and plug-in electric vehicles (PEVs). In this system, a power to gas technology consisting of a carbon capture unit, an electrolyzer, a hydrogen storage unit, and a methanation process, is used to supply the natural gas demand using CO2 captured from the MCUs and the hydrogen generated by the clean water electrolyzer. Furthermore, the electricity is consumed by a desalination unit to procure the clean water demand, carbon captured unit (CCU), electrolyzer water inflow, and fill up the water storage tank. At peak clean water demand periods, the excess water is pumped from the storage tank to the consumption side. Demand response program (DRP) is used for levelling the demand. Here, total cost for water-gas-heat-power nexus model throughout the day with and without DRP for three different scenarios is minimized by using quasi-oppositional fast convergence evolutionary programming, fast convergence evolutionary programming, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and differential evolution. It is seen from numerical results that cost attained with DRP is less than the cost attained without DRP.

Enhancing Robot Path Planning through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Enhancing Robot Path Planning through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Immune Plasma Programming: A new evolutionary computation-based automatic programming method

Immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation-based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems.

Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria

At present, synthetic biology applications are based on the programming of synthetic bacteria with custom-designed genetic circuits through the application of a top-down strategy. These genetic circuits are the programs that implement a certain algorithm, the bacterium being the agent or shell responsible for the execution of the program in a given environment. In this work, we study the possibility that instead of programming synthesized bacteria through a custom-designed genetic circuit, it is the circuit itself which emerges as a result of the evolution simulated through an evolutionary algorithm. This study is conducted by performing in silico experiments in a community composed of synthetic bacteria in which one species or strain behaves as pathogenic bacteria against the rest of the non-pathogenic bacteria that are also part of the bacterial consortium. The goal is the eradication of the pathogenic strain through the evolutionary programming of the agents or synthetic bacteria. The results obtained suggest the plausibility of the evolutionary design of the appropriate genetic circuit resulting from the application of a bottom-up strategy and therefore the experimental feasibility of the evolutionary programming of synthetic bacteria.

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

Symbolic regression with feature selection of dye biosorption from an aqueous solution using pumpkin seed husk using evolutionary computation-based automatic programming methods

Symbolic regression with feature selection of dye biosorption from an aqueous solution using pumpkin seed husk using evolutionary computation-based automatic programming methods

Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups.

An ANN-based Hong-Lagrange algorithm (ANN-based HLA) for auto design-based building application (ABBA) with prestressed precast piperack frame

ABSTRACT Building optimization using traditional approaches based on explicit objective functions is difficult because of large numbers of variables and constraints. Many studies, thus, developed derivative-free optimizations using evolutionary programming, particle swarm methods, swarm intelligence, etc. Applications of artificial neural networks (ANNs) in frame designs are not common, although ANN is an evolutionary technology in many areas. This study presents novel holistic designs and optimizations of prestressed multi-story frames in general and piperack frames in particular, using the ANN-based Hong-Lagrange Algorithm. The proposed method provides optimized designs in terms of multiple objective functions such as costs, CO2 emissions, weights, and energy consumptions. A software is developed based on MATLAB codes for generating big datasets following American standards. A big data of 120,000 samples is used to train ANNs, formulating objective and constraint functions of 38 input and 69 output parameters. Designs minimizing multiple objective functions are performed by applying sequential quadratic programming to ANN-based functions constrained by equalities and inequalities imposed by architectural and code requirements. Examples show reductions up to 31.81%, 38.63%, 5.06%, and 37.36% in costs, CO2 emissions, weights, and energy consumption, respectively, compared with the minima of those identified from 340,000 samples.

Multicounty fuel constraint‐based dynamic economic dispatch problem using horse herd optimization

AbstractDue to the diminution of fossil fuels, the profitable usage of fuel for the generation of power is reducing. It is an extremely vital concern for power generation companies. The fossil fuel‐based power plant must operate within its fuel limits and contractual restrictions. Therefore, economic optimization is required to optimize the fuel cost for generation under fuel constraints. To address this issue, in this work, a horse herd optimization algorithm (HOA) is developed to solve multicounty fuel‐constrained dynamic economic dispatch with demand‐side management incorporating wind turbine generators, solar photovoltaic plants, and pumped hydro storage plants. The efficiency of the suggested HOA algorithm has been revealed on a selected test system. The HOA technique shows better convergence as compared with the other methods. Moreover, the computation time requirement of the HOA technique is also lower than that of other compared methods. From numerical results, it is observed that the consumption of fuel is sufficiently high if constraints are incorporated into the problem. Simulation results are matched up to those obtained from hierarchical particle swarm optimization with time‐varying acceleration coefficients, fast convergence evolutionary programming, and differential evolution. After comparison, it is observed that the recommended HOA offers a better‐quality solution.

Research on Synthesis of Multi-Layer Intelligent System for Optimal and Safe Control of Marine Autonomous Object

The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms—were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control.

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.

Application and development of artificial intelligence in environmental health

In recent years, major industries have been hit hard by the COVID-19 pandemic, and people have become more aware of issues such as environmental health. The development of computer programs, artificial intelligence (AI), has gradually entered the vision of researchers. The product of this technology can help people better manage and improve the current environmental conditions. This paper, through a method of literature review, aims to explain the development history of AI and its application in different fields such as the environment. Through the research, the paper concludes that artificial intelligence can observe in real time for long periods of time without getting tired like humans do. Based on the application of big data analysis and intelligent programs, artificial intelligence can make reasonable plans in a much shorter time than human beings. However, the solution is only based on the operation of the system. In the actual situation, there will be many obstacles. Therefore, it is still necessary for humans to make judgments based on the results of an operation rather than relying entirely on artificial intelligence. Complicated weather conditions can also make the results of the calculation inaccurate. So there will still be a need to evolve computer programs or for artificial intelligence to be able to calibrate the data it is monitoring from the past.

A Hybrid Mode Membrane Computing Based Algorithm with Applications for Proton Exchange Membrane Fuel Cells

Membrane computing is a branch of natural computing which has been extended to solve various optimization problems. A hybrid mode membrane-computing-based algorithm (HMMCA) is proposed in this paper to solve complex unconstrained optimization problems with continuous variables. The algorithmic framework of HMMCA translates from its distributed cell-like membrane structure and communication rule. A non-deterministic evolutionary programming method and two computational rules are applied to enhance the computational performance. In a numerical simulation, 12 benchmark test functions with different variables are used to verify the algorithmic performance. The test results and comparison with three other algorithms illustrate its effectiveness and superiority. Moreover, a case study on a proton exchange membrane fuel cell (PEMFC) system parameter optimization problem is applied to validate its practicability. The results of the simulation and comparison with seven other algorithms demonstrate its practicability.

Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate

AbstractSelf-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) and gene expression programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the linear regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R2) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.

Evaluation and control of data relevance in information systems of the transport industry management

Purpose. Analysis of data coming to the information systems (IS) of the transport area (TA) and development of proposals for verification of their relevance to ensure reliability and efficiency of management. Development of the evaluation and control method of data relevance in information systems of the TA. Methodology. Special and general methods of scientific knowledge are used: critical analysis to establish scenarios of the impact of data relevance on the level of effective interaction of the elements of the system and its management; content analysis for stratification of requirements for data from IS and management decisions at the stage of analysis; mathematical formalization for the development of the data evaluation method and control of their relevance, which is proposed as the mathematical base of the algorithm; method of sequential selection for automatic detection of functional dependence, which describes data with a smaller value of standard error. Findings. Proposals to ensure data relevance have been developed. Data and management decision stratification has been proposed which will allow avoiding common errors due to non-normalization of data presentation. The scenarios of the impact of data relevance on the level of effective interaction of the elements of the transport system and its management have been established. For permanent data control, an algorithm for detecting deviations in consecutive data sets, automatic analysis of specified deviations, and detection of univariate and multivariate nonlinear patterns that cause deviations has been proposed. In case of inconsistencies of data with the identified non-linear patterns, the information is checked by the method of evolutionary programming. The effectiveness of the proposed methods is demonstrated using the example of analysis of the cargo turnover of seaports whose trends are characterized by significant changes over time and across ports. These trends may even have opposite characteristics. Originality. The method of data evaluation and control of their relevance, which is based on the methods of evolutionary (“genetic”) programming, is developed. Practical value. Proposals for evaluation and control of data relevance in information systems of the TA will increase the efficiency of management activities.

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.

Sexual harassment disproportionately affects ecology and evolution graduate students with multiple marginalized identities in the United States

Abstract Sexual harassment within academic institutions has profound impacts that may lead to the attrition of groups historically excluded from the biological sciences and related disciplines. To understand sexual harassment's effects on vulnerable communities within academia, we examined graduate student experiences with sexual harassment. In a survey of ecology and evolutionary biology programs across the United States, we found that 38% of the graduate student respondents were sexually harassed during their time in these programs. Sexual harassment disproportionately affected graduate students with multiple intersecting marginalized identities, and these experiences led to delays in completing graduate programs and shifts away from their desired careers. Our research highlights the need for academic institutions, and science more broadly, to make widespread changes to sexual harassment policies, including treating sexual harassment as scientific misconduct and creating resources for individuals within students’ informal support networks, in tandem with efforts to dismantle barriers to advancing diversity, equity, and inclusion.