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Development of a Mutation Operator in a real-coded genetic algorithm for Bridge Model Optimization

KSCE JOURNAL OF CIVIL ENGINEERING 2024年第5期28卷 1822-1835页

作者： Kim, Jaecheon Han, Manseok Shin, Soobong Inha Univ Dept Civil Engn Incheon 22212 South Korea

A mutation operator in a real-coded genetic algorithm is developed and applied for efficient bridge-model optimization. A mutation operator that changes uniformly or dynamically with a crossover operator is proposed to address optimization problems. The performance of the combined genetic operators was verified using a variety of available test problems based on the convergence and search speed of the global optimal solution. It is shown that the genetic algorithm proposed in this study yields relatively better results than the available algorithms and is more effective in constrained optimization problems. The performance of the proposed genetic algorithm is also verified through a sample study using a field load test for the model optimization of an existing bridge.

关键词： real-coded genetic algorithm Mutation operator Model optimization Field load test Crossover operator

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Selection of real-coded genetic algorithm parameters in solving simulation-optimization problems for the design of water distribution networks

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WATER SUPPLY 2023年第12期23卷 5046-5061页

作者： Kumar, Vidavaluru Hemanth Sai Pramada, S. K. Natl Inst Technol Calicut Calicut 673601 Kerala India

The design of the water distribution network (WDN) is very difficult mainly due to the nonlinear relation between head and flow. The distribution network should also be cost-effective. In any simulation-optimization approach, the computational time requirement is very high for very complex problems. The optimization module plays a crucial role in reducing the computational time. This study aims to apply a simulation-optimization approach to designing a WDN. EPANET is selected as the simulation model and real-coded genetic algorithm (RCGA) is selected as the optimization module. To link the simulation module with the optimization module, a program is written in MATLAB. The developed simulation-optimization approach was applied to two benchmark network problems to check the suitability of the method. The parameters of the RCGA were optimized for the two networks. The computational efficiency of the developed simulation-optimization model is checked based on the number of function evaluations. For both networks, the number of function evaluations to get the optimum network design was less than the number of function evaluations required for other methods mentioned in the literature.

关键词： real-coded genetic algorithm simulation-optimization water distribution system

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A New Optimization Model for MLP Hyperparameter Tuning: Modeling and Resolution by real-coded genetic algorithm

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NEURAL PROCESSING LETTERS 2024年第2期56卷 105-105页

作者： El-Hassani, Fatima Zahrae Amri, Meryem Joudar, Nour-Eddine Haddouch, Khalid Sidi Mohamed Ben Abdellah Univ ENSA Engn Syst & Applicat Lab Fes Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol Fez Dept Math Modelling & Math Struct Lab Fes Morocco

This paper introduces an efficient real-coded genetic algorithm (RCGA) evolved for constrained real-parameter optimization. This novel RCGA incorporates three specially crafted evolutionary operators: Tournament Selection (RS) with elitism, Simulated Binary Crossover (SBX), and Polynomial Mutation (PM). The application of this RCGA is directed toward optimizing the MLPRGA+5 model. This model is designed to configure Multilayer Perceptron neural networks by optimizing both their architecture and associated hyperparameters, including learning rates, activation functions, and regularization hyperparameters. The objective function employed is the widely recognized learning loss function, commonly used for training neural networks. The integration of this objective function is supported by the introduction of new variables representing MLP hyperparameter values. Additionally, a set of constraints is thoughtfully designed to align with the structure of the Multilayer Perceptron (MLP) and its corresponding hyperparameters. The practicality and effectiveness of the MLPRGA+5 approach are demonstrated through extensive experimentation applied to four datasets from the UCI machine learning repository. The results highlight the remarkable performance of MLPRGA+5, characterized by both complexity reduction and accuracy improvement.

关键词： real-coded genetic algorithm Hyperparameter Constrained optimization Multi-layer perceptron

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An improved real-coded genetic algorithm with random walk based mutation for solving combined heat and power economic dispatch

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第8期12卷 8561-8584页

作者： Haghrah, A. Nekoui, M. A. Nazari-Heris, M. Mohammadi-ivatloo, B. KN Toosi Univ Technol Fac Elect Engn Tehran Iran Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Combined heat and power economic dispatch (CHPED) is an energy management problem that minimizes the operation cost of power and heat generation while a vast variety of operational constraints of the system should be met. The CHPED is a complicated, non-convex and non-linear problem. In this study, a new real-coded genetic algorithm with random walk-based mutation (RCGA-CRWM) is under study, which is effective in solving large-scale CHPED problem with minimum operation cost. In the presented optimization method, a simple approach is introduced to combine the positive features of different probabilistic distributions for the step size of random walk. Using the presented approach, while the genetic algorithm is speeded up, the premature convergence is also avoided. After verifying the performance of the presented method on the benchmark functions, two large-scale and two medium-scale case studies are used for determining the algorithm strength in solving the CHPED problem. Despite the fact that the complexity of the CHPED rises dramatically by increasing its dimensionality, the algorithm has solved the problems accurately. The application of RCGA-CRWM method improves the results of the CHPED problem in terms of both operation cost and convergence speed in comparison with other optimization methods.

关键词： Combined heat and power (CHP) economic dispatch real-coded genetic algorithm random walk complex optimization problem

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Solving engineering optimization problems using an improved real-coded genetic algorithm (IRGA) with directional mutation and crossover

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SOFT COMPUTING 2021年第7期25卷 5455-5481页

作者： Das, Amit Kumar Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 W Bengal India

genetic algorithm (GA) is used to solve a variety of optimization problems. Mutation operator also is responsible in GA for maintaining a desired level of diversity in the population. Here, a directional mutation operator is proposed for real-coded genetic algorithm (RGA) along with a directional crossover (DX) operator to improve its performance. These evolutionary operators use directional information to guide the search process in the most promising area of the variable space. The performance of an RGA with the proposed mutation operator and directional crossover (DX) is tested on six benchmark optimization problems of different complexities, and the results are compared to that of the RGAs with five other mutation schemes. The proposed IRGA is found to outperform other RGAs in terms of accuracy in the solutions, convergence rate, and computational time, which is established firmly through statistical analysis. Furthermore, the performance of the proposed IRGA is compared to that of a few recently proposed optimization algorithms. The proposed IRGA is seen to yield the superior results compared to that of the said techniques. It is also applied to solve five constrained engineering optimization problems, where again, it has proved its supremacy. The proposed mutation scheme using directional information leads to an efficient search, and consequently, a superior performance is obtained.

关键词： real-coded genetic algorithm Directional mutation Directional crossover Computational time

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real coded genetic algorithm in operational optimization of a district cooling system: An inceptive applicability assessment and power saving evaluation

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BUILDING SERVICES ENGINEERING RESEARCH & TECHNOLOGY 2024年第6期45卷 755-773页

作者： Reshi, Mubashir A. Mursaleen, M. Natl Inst Technol Dept Mech Engn Srinagar 190006 India

District Cooling Systems are progressively becoming a standard feature of smart cities. This is attributed to their inherent feature of low operating cost and high energy efficiency. Given the constantly increasing energy prices worldwide and the target of the Conference of the Parties-28th Session for reducing emissions, the District Cooling System technology is quite promising in this direction. Various studies are available that have particularly focused on the design phase optimization of the systems, while in-process operational optimization is still in its miniature phase. This paper presents a model-based metaheuristic optimization approach to cooling water system towards an inceptive control strategy to explore and exploit the energy-saving potential using a real coded genetic algorithm. The algorithm is implemented in MATLAB to search for high-performance settings in real-time scenarios. The results showed that an energy saving from 9.66% to 26.54% can be obtained across 6 cases in the study, compared to the supervisory *** application District cooling technology is expected to gain more credibility as the most sustainable alternative to air conditioning in the upcoming decades due to the world's rapidly expanding need for cooling combined with the need to reduce carbon dioxide emissions. The current research and development efforts are yielding promising results for the fifth generation of this technology. Meanwhile, the study validates the enormous potential of operational optimization with contemporary artificial intelligence tools. This paper paves the way for future research by showing how the operation of a large-scale district cooling plant can be solved for energy saving.

关键词： District cooling system energy modelling real-coded genetic algorithm optimization, energy saving

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Multiobjective geometry optimization of microchannel heat exchanger using real-coded genetic algorithm

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APPLIED THERMAL ENGINEERING 2022年 202卷 1页

作者： Garcia, John Carlo S. Tanaka, Hiroki Giannetti, Niccolo Sei, Yuichi Saito, Kiyoshi Houfuku, Mamoru Takafuji, Ryoichi Waseda Univ Dept Appl Mech & Aerosp Engn Tokyo 1698555 Japan Univ Philippines Dept Mech Engn Quezon City 1101 Philippines Waseda Univ Waseda Inst Adv Study Tokyo 1698050 Japan Waseda Univ Interdisciplinary Inst Thermal Convers Engn & Mat Tokyo 1698555 Japan Univ Electrocommun Dept Informat Tokyo 1828585 Japan Hitachi Johnson Controls Air Conditioning Inc 500 TomidaOhira Machi Tochigi City Tochigi 3294404 Japan

In this paper, a multiobjective optimization of the structure of a flat-tubed microchannel heat exchanger is performed to reduce its volume and fan power at a specified capacity. Design variables include tube height, tube width, tube length, fin height, and fin pitch. A weight-based, real-coded genetic algorithm is implemented to optimize the design variables within their specified range of dimensions. To further improve the numerical simulations of the microchannel heat exchanger performance, correlations for the air-side Nusselt number, friction factor, and fin efficiency are developed and validated. In the optimization, the Pareto optimal fronts are obtained by varying weights of the two conflicting objectives. A reference microchannel heat exchanger operating at different capacities is optimized. Results show that the volume and fan power of the reference microchannel heat exchanger can be reduced by up to 45% and 51% respectively, depending on the weighting factor selected. The optimization approach of this study provides the optimal solutions at the given domain of geometric parameter dimensions.

关键词： Microchannel heat exchanger Optimization real-coded genetic algorithm

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Learning of Boosting Fuzzy Cognitive Maps Using a real-coded genetic algorithm

Learning of Boosting Fuzzy Cognitive Maps Using a Real-coded...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Yang, Ze Liu, Jing Wu, Kai Xidian Univ Sch Artificial Intelligence Xian Shaanxi Peoples R China

ISBN: (纸本)9781728121536

Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. However, the accuracy of population-based FCM learning algorithms is relatively low. Boosting is an effective method to improve the accuracy of any learning algorithm. To this end, we combine FCMs with boosting, termed as boosting fuzzy cognitive maps (BFCMs). The BFCM is an extension of FCMs and has a better performance on fast numerical reasoning than FCMs. In this paper, a real-coded genetic algorithm, which is a popular population-based learning algorithm, is improved on mutation operator and applied to learn the BFCM models, termed as RCGA-BFCM. In the experiments, RCGA-BFCM is applied to learn the BFCM from synthetic data with varying sizes and densities. The experimental results show that RCGA-BFCM can learn BFCMs with high accuracy from synthetic data. In addition, the performance of RCGA-BFCM is validated on the benchmark datasets DREAM3 and DREAM4. The experimental results show that RCGA-BFCM outperforms other learning algorithms obviously, which illustrates that RCGA-BFCM can reconstruct gene regulatory networks (GRNs) effectively.

关键词： fuzzy cognitive maps boosting real-coded genetic algorithm

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Fuel consumption minimization of transport aircraft using real-coded genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2018年第10期232卷 1925-1943页

作者： Singh, Vedant Abhilashi Univ Fac Engn Technol Mandi Himachal Prades 175028 India

The issue of rising carbon dioxide emissions from aviation fuel consumption is increasingly crucial in terms of the Paris Agreement on climate change, which was adopted in 2015. The rapid growth of the global transport network has been affecting the environment adversely owing to the emission of greenhouse gases. Therefore, researchers are attempting to minimize aviation fuel consumption. This study presents a fuel consumption minimization problem in transport aircraft design. A real-coded genetic algorithm with direction-based crossover is employed to determine the optimum design for minimum fuel consumption. To mimic the aircraft fuel consumption issues, the proposed real-coded genetic algorithm employs the following three operators: ranking selection, direction-based crossover, and dynamic random mutation. Compared with earlier studies that used uniform crossover, the present study reduces the fuel consumption by applying real-coded genetic algorithm with a direction-based crossover operator. Comprehensive results show that the proposed real-coded genetic algorithm achieves remarkably faster convergence and improved search performance than the compared method. Direction-based crossover significantly enhances the fitness by guiding the crossover along a certain direction. In addition, it ensures a higher probability of locating the global optimum. Therefore, adopting such a technique can reduce the aircraft development cost.

关键词： Fuel consumption optimization preliminary aircraft design real-coded genetic algorithm direction-based crossover

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A real-coded genetic algorithm for optimizing the damping response of composite laminates

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COMPUTERS & STRUCTURES 2018年 198卷 51-60页

作者： Pathan, M. V. Patsias, S. Tagarielli, V. L. Imperial Coll London Dept Aeronaut London SW7 2AZ England Rolls Royce Plc POB 31 Derby DE24 8BJ England

We develop a real-coded constrained genetic algorithm (GA) and assess its performance for the case of selected classical optimisation problems. The proposed GA uses a roulette selection method, BLX-alpha cross-over operation, non-uniform mutation along with single elitist selection at every generation. The GA is then applied, in conjunction with the finite element (FE) method, to optimise the damping response of a laminate comprising unidirectional composite laminae and viscoelastic damping layers. Modal loss factors are maximised against the constraints of given structural stiffness and mass. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： real-coded genetic algorithm Composite laminates Optimization Damping

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