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Journal articles on the topic 'REAL CODED GENETIC ALGORITHM (RCGA)'

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Published: 26 October 2023

Last updated: 25 July 2025

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1

Beklaryan, Gayane L., Andranik S. Akopov, and Nerses K. Khachatryan. "Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control." Cybernetics and Information Technologies 19, no. 2 (2019): 87–103. http://dx.doi.org/10.2478/cait-2019-0017.

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Abstract This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness

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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation." Cybernetics and Information Technologies 21, no. 3 (2021): 127–44. http://dx.doi.org/10.2478/cait-2021-0034.

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Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been e

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Mahmudy, Wayan F., Romeo M. Marian, and Lee H. S. Luong. "Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization." Advanced Materials Research 701 (May 2013): 364–69. http://dx.doi.org/10.4028/www.scientific.net/amr.701.364.

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This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple gen

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Uemura, Kento, and Isao Ono. "AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (2016): 429–37. http://dx.doi.org/10.20965/jaciii.2016.p0429.

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This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adap

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Selvakumari Jeya, I. Jasmine, and S. N. Deepa. "Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7493535.

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A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused o

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Cherif, Imen, and Farhat Fnaiech. "Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)." International Journal of Applied Mathematics and Computer Science 25, no. 4 (2015): 863–75. http://dx.doi.org/10.1515/amcs-2015-0062.

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Abstract This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method

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Shabalin, Denis, and Vladimir Stanovov. "Neural network-based vehicle control in simulated environments using real-coded genetic algorithms." ITM Web of Conferences 72 (2025): 05008. https://doi.org/10.1051/itmconf/20257205008.

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This research explores a method of optimizing neural networks for vehicle control in a simulation environment using a real-coded genetic algorithm (RCGA). The study focuses on applying RCGA in conjunction with multiple genetic operators, including simulated binary crossover (SBX), power mutation (PM), and tournament selection, to evolve neural network weights and biases, enhancing control performance for simulated vehicles. By utilizing RCGA to adjust neural network parameters, the approach enables adaptive and efficient vehicle control. The experiments demonstrate that combining sensor data w

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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm." Cybernetics and Information Technologies 20, no. 3 (2020): 45–63. http://dx.doi.org/10.2478/cait-2020-0027.

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AbstractThis work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were c

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Nakane, Takumi, Xuequan Lu, and Chao Zhang. "A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm." Computational Intelligence and Neuroscience 2020 (September 27, 2020): 1–20. http://dx.doi.org/10.1155/2020/8835852.

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In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, t

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Padmanabhan, S., M. Chandrasekaran, P. Asokan, and V. Srinivasa Raman. "A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization." Advanced Materials Research 622-623 (December 2012): 64–68. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.64.

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he major problem that deals with practical engineers is the mechanical design and creativeness. Mechanical design can be defined as the choice of materials and geometry, which satisfies, specified functional requirements of that design. A good design has to minimize the most significant adverse result and to maximize the most significant desirable result. An evolutionary algorithm offers efficient ways of creating and comparing a new design solution in order to complete an optimal design. In this paper a type of Genetic Algorithm, Real Coded Genetic Algorithm (RCGA) is used to optimize the des

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Lin, W., M. H. Wu, and S. Duan. "Engine Test Data Modelling by Evolutionary Radial Basis Function Networks." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 217, no. 6 (2003): 489–97. http://dx.doi.org/10.1243/095440703766518113.

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The engine test bed is introduced briefly and the importance of modelling for the engine test is discussed. The application of combining radial basis function (RBF) networks and a real-coded genetic algorithm (RCGA) to create the model is described for the engine test. Finally, the experimental results are analysed and it is shown that the proposed approach combining RCGA and RBF models is well suited for the engine test data modelling task.

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Sawyerr, Babatunde A., Aderemi O. Adewumi, and M. Montaz Ali. "Benchmarking RCGAu on the Noiseless BBOB Testbed." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/734957.

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RCGAu is a hybrid real-coded genetic algorithm with “uniform random direction” search mechanism. Theuniform random directionsearch mechanism enhances the local search capability of RCGA. In this paper, RCGAu was tested on the BBOB-2013 noiseless testbed using restarts till a maximum number of function evaluations (#FEs) of 105×Dare reached, whereDis the dimension of the function search space. RCGAu was able to solve several test functions in the low search dimensions of 2 and 3 to the desired accuracy of 108. Although RCGAu found it difficult in getting a solution with the desired accuracy 108

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Li, Ye, and Xiaohu Shi. "Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps." International Journal of Computational Intelligence and Applications 19, no. 03 (2020): 2050023. http://dx.doi.org/10.1142/s1469026820500236.

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The study on the prediction of mine pressure, while exploiting in coal mine, is a critical and technical guarantee for coal mine safety and production. In this paper, primarily due to the actual demand for the prediction of mine pressure, a practical prediction model Mine Pressure Prediction (MPP) was proposed based on fuzzy cognitive maps (FCMs). The Real Coded Genetic Algorithm (RCGA) was proposed to solve the problem by introducing the weight regularization and dropout regularization. A numerical example involving in-situ monitoring data is studied. Mean Square Error (MSE) and fitness funct

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Kita, Hajime. "A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms." Evolutionary Computation 9, no. 2 (2001): 223–41. http://dx.doi.org/10.1162/106365601750190415.

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This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal distribution for mutation into the genetic code and lets them evolve by selection as well as the decision variables. In the RCGA, crossover or recombination is used mainly for search. It utilizes information on several individuals to generate nov

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Mahmudy, Wayan Firdaus, Romeo M. Marian, and Lee H. S. Luong. "Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling." Advanced Materials Research 701 (May 2013): 359–63. http://dx.doi.org/10.4028/www.scientific.net/amr.701.359.

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This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively e

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Nishiba, Ai, Hiroharu Kawanaka, Haruhiko Takase, and Shinji Tsuruoka. "A Proposal of Genetic Operations for BSIM Parameter Extraction Using Real-Coded Genetic Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (2011): 1131–38. http://dx.doi.org/10.20965/jaciii.2011.p1131.

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This paper discusses genetic operations and their effects on evolution of GA in BSIM parameter extraction problems. Generally, Real-Coded Genetic Algorithm (RCGA) using Simplex Crossover (SPX) is often employed to extract BSIM parameter sets. BSIM parameters, however, have recommended operating ranges. There are regarded as constraints, thus all extracted parameters have to be satisfied them. In many cases, when the number of parameters becomes large, the conventional methods generate a lot of infeasible solutions because SPX makes offspring on the simplex plane expanded by ε parameter. This m

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17

Akopov, Andranik. "Modeling and optimization of strategies for making individual decisions in multi-agent socio-economic systems with the use of machine learning." Business Informatics 17, no. 2 (2023): 7–19. http://dx.doi.org/10.17323/2587-814x.2023.2.7.19.

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This article presents a new approach to modeling and optimizing individual decision-making strategies in multi-agent socio-economic systems (MSES). This approach is based on the synthesis of agent-based modeling methods, machine learning and genetic optimization algorithms. A procedure for the synthesis and training of artificial neural networks (ANNs) that simulate the functionality of MSES and provide an approximation of the values of its objective characteristics has been developed. The feature of the two-step procedure is the combined use of particle swarm optimization methods (to determin

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18

Wang, Jiquan, Mingxin Zhang, Okan K. Ersoy, Kexin Sun, and Yusheng Bi. "An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover." Computational Intelligence and Neuroscience 2019 (November 14, 2019): 1–17. http://dx.doi.org/10.1155/2019/4243853.

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A multi-offspring improved real-coded genetic algorithm (MOIRCGA) using the heuristical normal distribution and direction-based crossover (HNDDBX) is proposed to solve constrained optimization problems. Firstly, a HNDDBX operator is proposed. It guarantees the cross-generated offsprings are located near the better individuals in the population. In this way, the HNDDBX operator ensures that there is a great chance of generating better offsprings. Secondly, as iterations increase, the same individuals are likely to appear in the population. Therefore, it is possible that the two parents of parti

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Bhosale, K. C., and P. J. Pawar. "Material Flow Optimisation of Flexible Manufacturing System using Real Coded Genetic Algorithm (RCGA)." Materials Today: Proceedings 5, no. 2 (2018): 7160–67. http://dx.doi.org/10.1016/j.matpr.2017.11.381.

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Kubba, Hassan Abdullah, and Alaa Suheib Rodhan. "A Real-Coded Genetic Algorithm with System Reduction and Restoration for Rapid and Reliable Power Flow Solution of Power Systems." Journal of Engineering 21, no. 5 (2015): 1–19. http://dx.doi.org/10.31026/j.eng.2015.05.01.

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The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted

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Zhang, Pei, and Jian Feng. "Critical Buckling of Prestress-Stable Tensegrity Structures Solved by Real-Coded Genetic Algorithm." International Journal of Structural Stability and Dynamics 18, no. 04 (2018): 1850048. http://dx.doi.org/10.1142/s0219455418500487.

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Tensegrity structures are classified as kinematically determinate ones with two subcases and kinematically indeterminate ones with three subcases in view of their respective stability properties. How the stiffness of a tensegrity structure changes as the level of prestress changes is explored for different scenarios using six carefully chosen samples. For a tensegrity structure merely satisfying the prestress-stability condition, a new optimization model is presented to determine its critical buckling state corresponding to zero stiffness. A real-coded genetic algorithm (RCGA) is then develope

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Akopov, Andranik S., Armen L. Beklaryan, and Aleksandra A. Zhukova. "Optimization of Characteristics for a Stochastic Agent-Based Model of Goods Exchange with the Use of Parallel Hybrid Genetic Algorithm." Cybernetics and Information Technologies 23, no. 2 (2023): 87–104. http://dx.doi.org/10.2478/cait-2023-0015.

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Abstract A novel approach to modeling stochastic processes of goods exchange between multiple agents is presented, considering the possibility of optimizing the environment's characteristics and individual decision-making strategies. The proposed model makes it possible to form optimal states when choosing the moments of concluding barter and monetary transactions at the individual level of each agent maximizing the utility function. A new parallel hybrid Real-Coded Genetic Algorithm and Particle Swarm Optimization (RCGA-PSO) has been developed, combining methods of evolutionary selection base

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Noureddine, Aloui, Mohamed Boussif, and Cherif Adnane. "A Modified Ultraspherical Window and Its Application for Speech Enhancement." Traitement du Signal 39, no. 1 (2022): 79–86. http://dx.doi.org/10.18280/ts.390108.

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In this paper an improved ultraspherical window is developed for designing quadrature mirror filters banks (QMF) with the help of a real coded genetic algorithm (RCGA). In fact, the ultraspherical window is modified by adding a parameter (α) which to improve the spectral parameters. Then, RCGA is used to find optimal values of the adjustment parameters, the side-lobes ratio of ultraspherical window and the cut-off frequency of the low-pass prototype filter. This latter, is used to derive all the filters of QMF banks. When the developed QMF banks are exploited for speech enhancement algorithm b

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Sachan, Ruchi, Zahid Muhammad, Jaehoon (Paul) Jeong, Chang Wook Ahn, and Hee Yong Youn. "MABC: Power-Based Location Planning with a Modified ABC Algorithm for 5G Networks." Discrete Dynamics in Nature and Society 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/4353612.

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The modernization of smart devices has emerged in exponential growth in data traffic for a high-capacity wireless network. 5G networks must be capable of handling the excessive stress associated with resource allocation methods for its successful deployment. We also need to take care of the problem of causing energy consumption during the dense deployment process. The dense deployment results in severe power consumption because of fulfilling the demands of the increasing traffic load accommodated by base stations. This paper proposes an improved Artificial Bee Colony (ABC) algorithm which uses

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Rao, R. V., and R. B. Pawar. "Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks." Journal of Computational Design and Engineering 7, no. 6 (2020): 830–63. http://dx.doi.org/10.1093/jcde/qwaa060.

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Abstract In this paper, an endeavor is made to enhance the convergence speed of the recently proposed Rao algorithms. The new upgraded versions of Rao algorithms named as “quasi-oppositional-based Rao algorithms” are proposed in this paper. The quasi-oppositional-based learning is incorporated in the basic Rao algorithms to diversify the searching process of the algorithms. The performance of the proposed algorithms is tested on 51 unconstrained benchmark functions. Also, three multi-objective optimization case studies of different heat sinks such as a single-layered microchannel heat sink (SL

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Ly, Hai-Bang, Tien-Thinh Le, Huong-Lan Thi Vu, Van Quan Tran, Lu Minh Le, and Binh Thai Pham. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams." Sustainability 12, no. 7 (2020): 2709. http://dx.doi.org/10.3390/su12072709.

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genet

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Le, Tien-Thinh. "Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading." Advances in Civil Engineering 2020 (October 28, 2020): 1–19. http://dx.doi.org/10.1155/2020/8832522.

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In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluation of the model was performed using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the RCGA-ANFIS (R2 = 0.974) was more reliable and effective than conventional gradient descent (GD) technique (R2 = 0.952). The accuracy of the present work was found

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Y, Ainul, H. M., Salleh, S. M, Halib, N, Taib, H., and Fathi, M. S. "Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)." International Journal of Engineering & Technology 7, no. 4.30 (2018): 443. http://dx.doi.org/10.14419/ijet.v7i4.30.22363.

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System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different m

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Ashish, Saini, and Saraswat Amit. "SOLVING A MULTI-OBJECTIVE REACTIVE POWER MARKET CLEARING MODEL USING NSGA-II." International Journal of Advanced Information Technology (IJAIT) 2, no. 3 (2012): 49–62. https://doi.org/10.5281/zenodo.3559557.

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This paper presents an application of elitist non-dominated sorting genetic algorithm (NSGA-II) for solving a multi-objective reactive power market clearing (MO-RPMC) model. In this MO-RPMC model, two objective functions such as total payment function (TPF) for reactive power support from generators/synchronous condensers and voltage stability enhancement index (VSEI) are optimized simultaneously while satisfying various system equality and inequality constraints in competitive electricity markets which forms a complex mixed integer nonlinear optimization problem with binary variables. The pro

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Daoudi, Jamal, and Chakir Tajani. "Genetic Algorithm-Based Optimization Approach for Solving a Class of Inverse Problems with Tikhonov Regularization." WSEAS TRANSACTIONS ON MATHEMATICS 22 (November 14, 2023): 842–53. http://dx.doi.org/10.37394/23206.2023.22.92.

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In this paper, we are interested in solving the data completion problem for the Laplace equation. It consists to determine the missing data on the inaccessible part of the boundary from overspecified conditions in the accessible part. Knowing that this problem is severely ill-posed, we consider its formulation as an optimization problem using Tikhonov regularization. Then, we consider an optimization approach based on adapted Real Coded Genetic Algorithm (RCGA) to minimize the cost function and recover the missing data. The performed numerical simulations, with different domains, illustrate th

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Ameerudden, Mohammad Riyad, and Harry C. S. Rughooputh. "Hybridized Genetic Algorithms in the Optimization of a PIFA Antenna Using Fitness Characterization and Clustering." Advanced Materials Research 622-623 (December 2012): 40–44. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.40.

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With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide both larger bandwidth and small dimensions. The aim of this project is to design and optimize the bandwidth of a Planar Inverted-F Antenna (PIFA) in order to achieve a larger bandwidth in the 2 GHz band. This paper presents an intelligent optimization technique using a hybridized Genetic Algorithms (GA) coupled with the intelligence of the

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Li, Xue, Xueliang Fu, and Honghui Li. "A CARS-SPA-GA Feature Wavelength Selection Method Based on Hyperspectral Imaging with Potato Leaf Disease Classification." Sensors 24, no. 20 (2024): 6566. http://dx.doi.org/10.3390/s24206566.

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Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (

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So, GunBaek. "Design of an Intelligent NPID Controller Based on Genetic Algorithm for Disturbance Rejection in Single Integrating Process with Time Delay." Journal of Marine Science and Engineering 9, no. 1 (2020): 25. http://dx.doi.org/10.3390/jmse9010025.

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The integrating process with time delay (IPTD) is a fundamentally unstable open-loop system due to poles at the origin of the transfer function, and designing controllers with satisfactory control performance is very difficult because of the associated time delay, which is a nonlinear element. Therefore, this study focuses on the design of an intelligent proportional-integral-derivative (PID) controller to improve the regulatory response performance to disturbance in an IPTD, and addresses problems related to optimally tuning each parameter of the controller with a real coded genetic algorithm

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Ahila, M. Jeraldin, S. Joseph Jawhar, and N. Albert Singh. "Generation Unit Capacity Expansion Planning Analysis: Approach Using Real Coded Improved Genetic Algorithm." Applied Mechanics and Materials 626 (August 2014): 190–96. http://dx.doi.org/10.4028/www.scientific.net/amm.626.190.

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This paper provides information about the development of an algorithm called Real Coded Improved Genetic Algorithm (RCIGA). And it leads to a plan for generating units of power with minimum cost and that plan is called as Generation Unit Expansion Planning (GUEP) problem. GUEP is a fully forced non linear system. And this can be solved by technique called genetic algorithm. RCIGA helps in providing faster speed and the space which helps in searching also is increased. RCIGA helps in calculating the combination of units through which the minimum cost can be obtained and units of power should me

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Kubba, Hassan Abdullah, and Mounir Thamer Esmieel. "Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems." Journal of Engineering 24, no. 3 (2018): 84. http://dx.doi.org/10.31026/j.eng.2018.03.07.

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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 th

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Liu, An, Erwie Zahara, and Ming-Ta Yang. "A Modified NM-PSO Method for Parameter Estimation Problems of Models." Journal of Applied Mathematics 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/530139.

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Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies a

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Sudhagar, S., and V. Srinivasa Raman. "Design Optimization of Spur and Helical Gear Pairs." Applied Mechanics and Materials 766-767 (June 2015): 1034–43. http://dx.doi.org/10.4028/www.scientific.net/amm.766-767.1034.

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Gears are the most common of machine elements and due to that many studies have been conducted on optimum gear design. Gear optimization can be divided into two categories, namely, single gear pair or Gear train optimization. The problem of gear pairs design optimization is difficult to solve because it involves multiple objectives and large number of variables. Hence a trustworthy and resilient optimization technique will be more useful in obtaining an optimal solution for the problems. In the proposed work an effort has been made to optimize spur and helical gear pair design using LINGO and

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Arumugam, M. Senthil, and M. V. C. Rao. "On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems." Discrete Dynamics in Nature and Society 2006 (2006): 1–17. http://dx.doi.org/10.1155/ddns/2006/79295.

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This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights sep

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LU, WEI, LIYONG ZHANG, JIANHUA YANG, and XIAODONG LIU. "THE LINGUISTIC FORECASTING OF TIME SERIES USING IMPROVED FUZZY COGNITIVE MAP." International Journal of Computational Intelligence and Applications 12, no. 03 (2013): 1350014. http://dx.doi.org/10.1142/s1469026813500144.

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Most researchers of time series forecasting devote to design and develop quantitative models for pursuing high accuracy of forecasting on the numerical level. However, in real world, the numerical accuracy is sometimes not necessary for human cognition and decision-making and the numerical results of forecasting based on quantitative model are deficient in interpretability, thus the development of qualitative forecasting model of time series becomes an evident challenge. In this paper, the improved fuzzy cognitive map (IFCM) are proposed first, and then it is applied to develop qualitative mod

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Moqbel, Mohammed Ali Mohammed, Talal Ahmed Ali Ali, Zhu Xiao, and Amani Ali Ahmed Ali. "Design of efficient generalized digital fractional order differentiators using an improved whale optimization algorithm." PeerJ Computer Science 11 (July 1, 2025): e2971. https://doi.org/10.7717/peerj-cs.2971.

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This article proposes a new design and realization method for generalized digital fractional-order differentiator (GFOD) based on a composite structure of infinite impulse response (IIR) subfilters. The proposed method utilizes an improved whale optimization algorithm (IWOA) to compute the optimal coefficients of IIR subfilters of the realization structure. IWOA is developed by incorporating a piecewise linear chaotic mapping (PWLCM) and an adaptive inertia weight based on the hyperbolic tangent function (AIWHT) into the framework of original whale optimization algorithm (WOA). Simulation expe

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Lasisi, H.O., T.O. Ajewole, O. Oladepo, and O.E. Olabode. "Optimization of Reactive Power Injection on Radial Distribution Network for Improved System Performance." Nigerian Research Journal of Engineering and Environmental Sciences 7, no. 1 (2022): 69–81. https://doi.org/10.5281/zenodo.6721014.

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<em>Distribution systems occupied a core position in the hierarchical structure of conventional power systems. However, several factors limit the expected efficiency of many practical distribution systems. It is, therefore, a thing of concern to power distribution engineers to seek better ways to manage both the amount of real power loss and deviation on bus voltage profile. On this premise, this paper presents the use of a shunt capacitor as a mitigating device. The initial state of the test case system was determined using the backward forward sweep (BFS) power flow technique. Cuckoo search

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Mondal, Milan K., Nirmalendu Biswas, Aparesh Datta, Bikash K. Sarkar, and Nirmal K. Manna. "Positional impacts of partial wall translations on hybrid nanofluid flow in porous media: Real Coded Genetic Algorithm (RCGA)." International Journal of Mechanical Sciences 217 (March 2022): 107030. http://dx.doi.org/10.1016/j.ijmecsci.2021.107030.

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Santosh Naidana, Krishna, Yaswanth Yarra, and Lakshmi Prasanna Divvela. "Facial micro-expression classification through an optimized convolutional neural network using genetic algorithm." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 307–15. http://dx.doi.org/10.11591/eei.v14i1.8048.

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Computer vision facilitates machines to interpret the visual world using various computer aided detection (CAD)-based techniques. It plays a crucial role in micro-expression auto classification. A micro-expression is a brief facial movement which reveals a genuine emotion that a person tries to conceal, it usually lasts for a short duration and is imperceptible with normal vision. To reveal people’s genuine emotions, an automatic micro-expression screening using convolutional neural network (CNN) is in great need. Traditional methods for micro-expression recognition (MER) suffer from low class

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Mohammed, Ali H., and Suad I. Shahl. "Impact of Distributed Generation on a Distribution Network Voltage Sags in Baghdad City." Engineering and Technology Journal 39, no. 4A (2021): 528–42. http://dx.doi.org/10.30684/etj.v39i4a.1828.

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Voltage sags are considered as one of the most detrimental power quality (PQ) disturbance due to their costly influence on sensitive loads. This paper investigates the voltage sag mitigation in distribution network following the occurrence of a fault. Two software are used in this work; the 1st is MATLAB R2017a for implementation of the Differential Evaluation (DE) algorithm to find the optimal location and size DG and while the 2nd software is CYME 7.1 for the distribution system modelling and analysis. The effectiveness of the proposed method is tested by implementing it on IEEE 33-bus syste

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Lee, Won Jin, and Eui Hoon Lee. "Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization." Water 14, no. 1 (2022): 99. http://dx.doi.org/10.3390/w14010099.

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Runoff in urban streams is the most important factor influencing urban inundation. It also affects inundation in other areas as various urban streams and rivers are connected. Current runoff predictions obtained using a multi-layer perceptron (MLP) exhibit limited accuracy. In this study, the runoff of urban streams was predicted by applying an MLP using a harmony search (MLPHS) to overcome the shortcomings of MLPs using existing optimizers and compared with the observed runoff and the runoff predicted by an MLP using a real-coded genetic algorithm (RCGA). Furthermore, the results of the MLPHS

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Rafique, Muhammad, Zunaib Haider, Khawaja Mehmood, et al. "Optimal Scheduling of Hybrid Energy Resources for a Smart Home." Energies 11, no. 11 (2018): 3201. http://dx.doi.org/10.3390/en11113201.

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The present environmental and economic conditions call for the increased use of hybrid energy resources and, concurrently, recent developments in combined heat and power (CHP) systems enable their use at a domestic level. In this work, the optimal scheduling of electric and gas energy resources is achieved for a smart home (SH) which is equipped with a fuel cell-based micro-CHP system. The SH energy system has thermal and electrical loops that contain an auxiliary boiler, a battery energy storage system, and an electrical vehicle besides other typical loads. The optimal operational cost of the

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Bhosale, K. C., and P. J. Pawar. "Material flow optimisation of production planning and scheduling problem in flexible manufacturing system by real coded genetic algorithm (RCGA)." Flexible Services and Manufacturing Journal 31, no. 2 (2018): 381–423. http://dx.doi.org/10.1007/s10696-018-9310-5.

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Guewouo, Thomas, Lingai Luo, Dominique Tarlet, and Mohand Tazerout. "Identification of Optimal Parameters for a Small-Scale Compressed-Air Energy Storage System Using Real Coded Genetic Algorithm." Energies 12, no. 3 (2019): 377. http://dx.doi.org/10.3390/en12030377.

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Compressed-Air energy storage (CAES) is a well-established technology for storing the excess of electricity produced by and available on the power grid during off-peak hours. A drawback of the existing technique relates to the need to burn some fuel in the discharge phase. Sometimes, the design parameters used for the simulation of the new technique are randomly chosen, making their actual construction difficult or impossible. That is why, in this paper, a small-scale CAES without fossil fuel is proposed, analyzed, and optimized to identify the set of its optimal design parameters maximizing i

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Dasgupta, Koustav, and Provas Kumar Roy. "Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4 (2020): 38–63. http://dx.doi.org/10.4018/ijamc.2020100103.

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In this article, a new optimization technique, the backtracking search algorithm (BSA), is proposed to solve the hydrothermal scheduling problem. The BSA has mainly unique five steps: (i) Initialization; (ii) Selection – I; (iii) Mutation; (iv) Crossover; and (v) Selection – II; which have been applied to minimize fuel cost of the hydro-thermal scheduling problem. The BSA is very fast, robust, reliable optimization technique and gives an accurate, optimized result. Mutation and crossover are very effective steps of the BSA, which help to determine the better optimum value of the objective func

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Papageorgiou, Poczeta, Papageorgiou, Gerogiannis, and Stamoulis. "Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece." Algorithms 12, no. 11 (2019): 235. http://dx.doi.org/10.3390/a12110235.

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This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimizatio

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