Continuous Genetic Algorithm Research Articles

Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm

Algorithms used for day ahead generation scheduling are crucial for a power system operator to balance conflicting objectives and the network constraints. Practically feasible algorithms using para...

Optimal Design of Transmission Shafts: a Continuous Genetic Algorithm Approach

This paper presents an analysis of the optimal design of transmission shafts by adopting the approach of a novel continuous genetic algorithm. The optimization case study is formulated as a single-objective optimization problem whose objective function is the minimization of the total weight that results from the sum of all the sections in the shaft.Additionally, mechanical stresses and constructive characteristics are considered constraints in this case.The proposed optimization model corresponds to a nonlinear non-convex optimization problem which is numerically solved with a continuous variant of genetic algorithms. SKYCIV®and Autodesk Inventor®were used to verify the quality and robustness of the numerical results in this paper by means of simulation tools and analysis. The results obtained demonstrates that the methodology proposed reduce the complexity and improving the results obtained in comparison to conventional mechanical design.

Optimal power flow solution in direct current grids using Sine-Cosine algorithm

In the next years, Colombian power system will have the connection of distributed generators and constant power loads; Therefore, it is necessary to propose the analysis methods that allow establishing the minimum requirements that have to be satisfied for the power system to guarantee an optimal power flow in order to preserve safe and reliable operation of it. For this purpose, in this paper presents an optimal power flow in direct current resistive grids with constant power loads and distributed generators. The optimal power flow problem is formulated as a master-slave optimization algorithm. The master stage covers the dispatching of all the distributed generators by using the Sine-Cosine algorithm. In the slave stage an efficient power flow method based on successive approximations is employed to determine the voltage variables and evaluate the objective function of the problem, which corresponds to the power loss minimization. A direct current distribution network composed by 21 nodes is used as test case by comparing its numerical performance with nonlinear optimization packages and two metaheuristic approaches named black-hole optimization and continuous genetic algorithm. All the simulations are conducted via MATLAB software.

A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks

This paper proposes a convex approximation approach for solving the optimal power flow (OPF) problem in direct current (DC) networks with constant power loads by using a sequential quadratic programming approach. A linearization method based on the Taylor series is used for the convexification of the power balance equations. For selecting the best candidate nodes for optimal location of distributed generators (DGs) on a DC network, a relaxation of the binary variables that represent the DGs location is proposed. This relaxation allows identifying the most important nodes for reducing power losses as well as the unimportant nodes. The optimal solution obtained by the proposed convex model is the best possible solution and serves for adjusting combinatorial optimization techniques for recovering the binary characteristics of the decision variables. The solution of the non-convex OPF model is achieved via GAMS software in conjunction with the CONOPT solver; in addition the sequential quadratic programming model is solved via quadprog from MATLAB for reducing the estimation errors in terms of calculation of the power losses. To compare the results of the proposed convex model, three metaheuristic approaches were employed using genetic algorithms, particle swarm optimization, continuous genetic algorithms, and black hole optimizers.

Developing an improved approach to solving a new gas lift optimization problem

The increased speed and accuracy in solving optimization problems of gas allocation in the gas lift process are of high importance. Solving gas allocation optimization problems generally involves two steps: (1) The gas lift performance curve (GLPC) fitting (gas lift modeling) and (2) optimizing the allocation of gas between wells. Therefore, in order to increase the speed and accuracy of solving gas allocation optimization problems, both steps need to be improved. In order to increase the accuracy of the first step, a new correlation was proposed in which, in addition to increasing the accuracy of fit, the optimization speed was improved by decreasing the number of constants used in the correlation. Besides, in order to improve the performance of the second step, water cycle optimization algorithm was used and the results obtained from this algorithm were compared with the results obtained from previous studies on teaching–learning-based optimization (TLBO) algorithm, continuous ant colony (CACO) algorithm, genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for solving the five-well Nishikiori index problem. The results suggested that the water cycle optimization algorithm has a very good performance in terms of convergence rate, non-capture at local optimum points and repeatability. Finally, as a new problem, the gas allocation between the wells of one of the heavy oil fields in the southwest of Iran was optimized with predetermined oil production rates. The goal of optimization was to obtain the minimum amount of gas required to produce the predetermined oil rates using the water cycle optimization algorithm. The results showed that optimization is of higher importance in lower oil production targets, resulting in higher additional oil production.

Demonstration of Broadband Reflectarray Using Unit Cells With Spline-Shaped Geometry

In this paper, we propose an accurate and fully automatic method to design a unit cell with optimized shape metal patch. The method determines the outer boundaries of the metal patch based on the spline technique, resulting in a smooth and continuous shape for the patch. In order to find the best position of the points of the element boundaries and achieve the best phase response for the element, we employ a continuous genetic algorithm as an optimization tool. An in-house code is developed to link MATLAB and CST softwares for numerically calculating the phase response of the investigated unit cell in each iteration of the optimization process. As a typical case, we achieve a single-layer wideband unit cell that has a nearly ideal phase response from 8 to 12.5 GHz. In order to verify the validity of the designed unit cell, a 210 mm $\times210$ mm reflectarray is designed, fabricated, and tested by the proposed unit cell. Experimental results show that the antenna has a 1 dB gain of 37% (8.85–12.8 GHz) and the maximum efficiency of 64.7%, which are regarded as highly acceptable achievements.

Multi-objective energy and daylight optimization of amorphous shading devices in buildings

As integrated components of the building envelopes, shading devices are the elements designed for stopping excessive amount of direct and indirect sunlight passing through and for avoiding undesirable admission of light into glazed buildings. Shading devices also reduce the operational cost of active systems, mostly heating and cooling, by providing considerable energy saving without completely blocking the daylight. However, the conventional shading device types in real world applications and even the ones presented in the literature stick to non-amorphous shapes providing limited improvement of the energy performance with negligible rates. Considering the lack of amorphous solutions in the literature, we propose novel design alternatives of energy-efficient shading device with panels in amorphous forms generated by parametric modeling and performance evaluation-based optimization in contrast with the conventionally designed structures. Initially, a performance evaluation-based optimization model was developed by employing evolutionary multi-objective optimization algorithms to overcome the complexity of the design process. Moreover, minimization of TEC (Total Energy Consumption) and maximization of the UDI (Useful Daylight Illuminance) are defined as the main objective functions to be optimized by non-dominated sorting genetic algorithm (NSGA II) and self-adaptive continuous genetic algorithm with differential evolution (JcGA-DE) in the shading model. According to the numerical results of the annual energy consumption, we managed to reach considerable energy saving up to 14%, while keeping the daylight availability above 50%.

Adaptive Fuzzy Control Method for a Single Tilt Tricopter

This article proposes an adaptive fuzzy gain scheduling (FSG) design of the traditional proportional integral derivative (PID) control method by using fuzzy logic rules to schedule controlled gains at different phases. Owing to minimization of the tracking error of the controller design using three parameters and the integral of time weighted-squared error (ITSE) minima criterion of the controller design process, the fuzzy rules of the triangular membership functions are exploited online to verify the PID controller gains in different operated scheduling modes. For that reason, the controller designs can be used to tune the system models during the whole operation time period to enable efficient error tracking. The continuous genetic algorithm (GA) is considered an innovation because in it, the decode chromosome step is totally neglected. Owing to this improvement, it is superior to the standard GA because it requires less storage and enables naturally faster convergence. In this research, the controlled parameters were optimized using the continuous GA to enhance the efficiency of the proposed method. Thereafter, it was implemented to a single tilt Tricopter model to test whether the control performance is better when compared with the conventional PID control method.

Curriculum change for graduate-level control engineering education at the Universidad Pontificia Bolivariana

Abstract This paper addresses the graduate-level control engineering curriculum change performed at the Universidad Pontificia Bolivariana (UPB), Medellin, Colombia. New proposed methodologies include active learning activities using a new multipurpose experimental test bed that was developed with industrial components. The renovated graduate-level control engineering related courses include: Continuous Processes, Discrete Processes, Fuzzy Logic, Neural Networks and Genetic Algorithms, Linear Control, Nonlinear Control, and Optimal Estimation. The new experimental station was developed for teaching, research, and industrial training activities for the School of Engineering at the UPB. In this work, we report the use of the station in an Optimal Estimation course to replace a traditional homework/exams evaluation approach with an applied work that required independent study, the implementation of different observers in a real lab-scale industrial plant, and a paper-style written report. Increasing independent study activities resulted in academic discussions that are valuable for the learning process of the student. The use of the experimental station and the real comparison of estimation algorithms, implemented by using industrial controllers and high-level programming environments, provided the student skills that cannot be acquired by using only simulations in which real implementation restrictions/challenges do not appear. This work represents one of the first approaches for the implementation of the new curriculum model at the UPB for graduate education. The methodology used in the Optimal Estimation class promoted independent learning, critical thinking and writing skills through significant learning activities.

Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods

In this study, the application of four evolutionary algorithms, continuous genetic algorithm (CGA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were considered for training and optimization of adaptive neuro-fuzzy inference system (ANFIS) to model groundwater quality variables. At first, using correlation and sensitivity analysis, the best inputs were selected to estimate electrical conductivity (EC), sodium adsorption ratio (SAR) and total hardness (TH). After that, the quality variables were modeled by simple ANFIS and the ANFIS trained by evolutionary algorithms. Finally, the models’ performances were evaluated using determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) and sensitivity analysis. Results indicated that: 1) All the suggested algorithms improved the ANFIS performance in the modeling of EC and TH. Also, in SAR, CGA and PSO had a better performance than existing algorithms of ANFIS. 2) CGA with the most appropriate results, was the best algorithm in improving ANFIS performance for modeling the groundwater quality variables such that the amounts of R2, RMSE, and MAPE were improved by 0.14, 35.4, and 0.59 for TH, by 0.13, 226 (μmho Cm−1), 2.16 for EC, and by 0.15, 690, and 19.04 for SAR, respectively. 3) Sensitivity analysis showed that the results obtained by correlation analysis was dependable and could be used as a primary step in choosing the best input data for prediction of groundwater quality variables.

Continuous Genetic Algorithms as Intelligent Assistance for Resource Distribution in Logistic Systems

This paper addresses the problem of resource distribution control in logistic systems influenced by uncertain demand. The considered class of logistic topologies comprises two types of actors—controlled nodes and external sources—interconnected without any structural restrictions. In this paper, the application of continuous-domain genetic algorithms (GAs) is proposed in order to support the optimization process of resource reflow in the network channels. GAs allow one to perform simulation-based optimization and provide desirable operating conditions in the face of a priori unknown, time-varying demand. The effectiveness of inventory management process governed under an order-up-to policy involves two different objectives—holding costs and service level. Using the network analytical model with the inventory management policy implemented in a centralized way, GAs search a space of candidate solutions to find optimal policy parameters for a given topology. Numerical experiments confirm the analytical assumptions.

Calibration of a sedimentation model through a continuous genetic algorithm

ABSTRACTIn this contribution we consider the problem of flux identification in a scalar conservation law modelling the phenomenon of sedimentation. The experimental observation data used for the calibration consist of a solid concentration profile at a fixed time. The identification problem is formulated as an optimization one, where the distance between the profiles of the model simulation and observation data is minimized by a least squares cost function. The direct problem is approximated by a monotone finite volume scheme. The numerical solution of the calibration problem is obtained by a continuous genetic algorithm. Numerical results are presented in order to validate the efficiency of the proposed algorithm.

Molecular dynamics, grand canonical Monte Carlo and expert simulations and modeling of water–acetic acid pervaporation using polyvinyl alcohol/tetraethyl orthosilicates membrane

In this study, molecular dynamics (MD) and Monte Carlo (MC) simulations techniques were employed as well as artificial intelligence knowledge of ANFIS and GP to investigate water–acetic acid pervaporation (PV) separation through poly vinylalcohol (PVA)‑silicone based membranes under a wide range of experimental conditions. For the first time, three new optimization algorithms, namely ant colony optimization for continuous domains (ACOR), differential evolution (DE) and genetic algorithm (GA) were employed for improving ANFIS modeling. The GP creates a mathematical function or model for the estimation of pervaporation separation index (PSI) as a function of the input variables. ACOR-ANFIS and GA-ANFIS and GP had high accuracy (R2 = 0.9831, 0.9792 and 0.9722, respectively) but DE-ANFIS had a lower accuracy (R2 = 0.9610) as compared to other models. On the other hand, molecular simulation methods were used and the results of all simulation models were compared fairly to each other and to the experimental results of the literature. Also, some characterizations were taking place to investigate the characteristics of the simulated membranes with MS such as WAXD, and FFV and glass transition temperature was used to estimate the thermal properties of the simulated membranes.

Constrained multi-objective wind farm layout optimization: Novel constraint handling approach based on constraint programming

Abstract Wind farms are frequently located in proximity to human dwellings, natural habitats, and infrastructure making land use constraints and noise matters of increasing concern for all stakeholders. In this study, we perform a constrained multi-objective wind farm layout optimization considering energy and noise as objective functions, and considering land use constraints arising from landowner participation, environmental setbacks and proximity to existing infrastructure. A multi-objective, continuous variable Genetic Algorithm (NSGA-II) is combined with a novel constraint handling approach to solve the optimization problem. This constraint handling approach uses a combination of penalty functions and Constraint Programming to balance local and global exploration to find feasible solutions. The proposed approach is used to solve the wind farm layout optimization problem with different numbers of turbines and under different levels of land availability (constraint severity). Our results show increasing land availability and/or number of turbines, increases energy generation, noise production, and computational cost. Results also illustrate the potential of the proposed constraint handling approach to outperform existing methods in the context of evolutionary optimization, yielding better solutions at a lower computational cost.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches. 

Continuous Genetic Algorithm : A Robust Method to Solve Higher Order Non-Linear Boundary Value Problem

Continuous Genetic Algorithm : A Robust Method to Solve Higher Order Non-Linear Boundary Value Problem

Optimization of a multi-link steering mechanism using a continuous genetic algorithm

A steering mechanism is one of the main components of a vehicle that is responsible of controlling the directional wheels. The optimal design of a steering mechanism is not a trivial problem because the equations involved in the modeling are highly nonlinear. Therefore, this paper proposed a method for global optimization using a genetic algorithm for a multi-link steering mechanism, which can also be applied to other problems of optimal synthesis of mechanisms.

Calibración numérica de leyes de conservación escalar vía un algoritmo genético continuo

Our work deals with the flux identication problem for scalar conservation laws. The problem is formulated as an optimization problem, where the objective function compares the solution of the direct problem with observed proles at a fixed time. A finite volume scheme solves the direct problem and a continuous genetic algorithm solves the inverse problem. The numerical method is tested with synthetic experimental data. Simulation parameters are recovered approximately. The tested heuristic optimization technique turns out to be more robust than classical optimization techniques.

Parameter estimation of aquifer transmissivity variogram using WLS, GA and PSO algorithms

This study focuses on the parameters estimation of aquifer transmissivity (T) variogram; sill, nugget effect and range using the weighted least square (WLS), continuous genetic algorithm (CGA) and particle swarm optimisation (PSO) algorithms. Three types of semivariograms including the spherical, Gaussian and exponential have been used to select the best-fitted model for corresponded empirical values. The objective function for this optimisation procedure is minimising the sum of deviation squares between theoretical and the empirical semivariogram values. These models have been applied with the transmissivity data of Qazvin plane aquifer which is located in north of Iran. The kriged values of T over the aquifer indicate that the simulated results based on PSO have the lowest estimation variance and most reliability in modelling of spatial structure of transmissivity compared to other optimisation algorithms (GA and WLS). Results show that if the kriging technique combined with the PSO algorithm, it would promote to a promising tool in estimation of spatial structures of aquifer transmissivity and be effective in groundwater quality and quantity management.

Optimization Design of IGV Profile in Centrifugal Compressor

Variable inlet guide vane (IGV) is used to control the mass flow and generate prewhirl in centrifugal compressors. The efficient operation of IGV is limited to the range of aerodynamic characteristics of their vane profiles. In order to find out the best vane profile for IGV regulation, the modern optimization method was adopted to optimize the inlet guide vane profile. The main methodology idea was to use artificial neural network for continuous fitness evaluation and use genetic algorithm for global optimization. After optimization, the regulating performance of IGV has improved significantly, the prewhirl ability has been enhanced greatly, and the pressure loss has been reduced. The mass flow and power of compressor reduced by using the optimized guide vane at large setting angles, and the efficiency increased significantly; the flow field distribution has been improved obviously, since the nonuniform distribution of flow and flow separation phenomenon greatly weakened or even completely disappeared. The achievement of this research can effectively improve the regulation ability of IGV and the performance of compressor.