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Chaotically Enhanced meta-heuristic algorithms for Optimal Design of Truss Structures with Frequency Constraints

PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2022年第3期66卷 900-921页

作者： Kaveh, Ali Yosefpoor, Hosein Iran Univ Sci & Technol Dept Civil Engn Tehran *** Iran Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran *** Iran

The natural frequencies of any structure contain useful information about the dynamic behavior of that structure, and by controlling these frequencies, the destructive effects of dynamic loads, including the resonance phenomenon, can be minimized. Truss optimization by applying dynamic constraints has been widely welcomed by researchers in recent decades and has been presented as a challenging topic. The main reason for this choice is quick access to dynamic information by examining natural frequencies. Also, frequency constraint relations are highly nonlinear and non-convex and have implicit variables, so using mathematical and derivative methods will be very difficult and time consuming. In this regard, the use of meta-heuristic algorithms in truss weight optimization with frequency constraints has good results, but with the introduction of form variables, these algorithms trap at local optima. In this research, by applying chaos map in meta-heuristic algorithms, suitable conditions have been provided to escape from local optima and access to global optimums. These algorithms include Chaotic Cyclical Parthenogenesis algorithms (CCPA), Chaotic Biogeography-Based Optimization (CBBO), Chaotic Teaching-Learning-Based Optimization (CTLBO) and Chaotic Particle Swarm Optimization (CPSO), respectively. Also, by using different scenarios, a good balance has been achieved between the exploration and exploitation of the algorithms.

关键词： frequency constraints shape and cross section optimization meta-heuristic algorithms chaos map

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Competition of Three Chaotic meta-heuristic algorithms with Physical Inspiration for Optimal Design of Truss Structures

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2024年第4期68卷 1211-1228页

作者： Kaveh, Ali Yosefpoor, Hosein Iran Univ Sci & Technol Sch Civil Engn POB16765-163 Tehran *** Iran Islamic Azad Univ Dept Tech & Engn Maragheh Branch POB 345 Maragheh *** Iran

Chaos maps create a significant improvement in the optimization results of meta-heuristic algorithms by creating a balance between the stages of exploration and exploitation. The optimization algorithms of structures are strongly non-linear and non-convex, having several local optima. Chaotic functions, while creating chaotic jumps, provide the conditions for escaping from local optima to global optima. Most of the meta-heuristic algorithms fall into the trap of local optima and suffer some kind of premature convergence. In this paper, by forming three scenarios, chaos functions can be embedded into the exploration, exploitation or both stages at the same time, and improve the results of meta-heuristic algorithms. The considered algorithms are inspired by physical phenomena, with the possibility of accessing classical and regular relations, the effectiveness of chaos functions in meta-heuristic algorithms are increased. Nowadays, chaotic algorithms are widely utilized by researchers and are considered as a challenging topic. In the present research, the effects of logistic and Gaussian chaos functions on the optimization results of three physically inspired meta-heuristic algorithms are investigated. These algorithms include Chaotic Thermal Exchange Optimization (CTEO), Chaotic Big Bang-Big Crunch (CBB-BC), and Chaotic Tug-of-War Optimization (CTWO).

关键词： chaos maps meta-heuristic algorithms exploration exploitation premature convergence local optima global optima

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Interval valued demand and prepayment-based inventory model for perishable items via parametric approach of interval and meta-heuristic algorithms

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KNOWLEDGE-BASED SYSTEMS 2022年 242卷 108343-108343页

作者： Manna, Amalesh Kumar Khan, Md. Al-Amin Rahman, Md Sadikur Shaikh, Ali Akbar Bhunia, Asoke Kumar Univ Burdwan Dept Math Burdwan 713104 India C V Raman Global Univ Dept Math Bhubaneswar 752054 India Jahangirnagar Univ Dept Math Savar Dhaka 1342 Bangladesh

This work mainly focuses on the mathematical formulation of an imprecise inventory model with partial prepayment policy in interval environment. In the proposed model, two different cases (without shortage and with partially backlogged shortages) are taken into consideration. Here, the demand and deterioration rates are considered as interval valued. Also, all the inventory costs, viz. ordering cost, purchase cost, holding cost, shortage cost and lost sale cost are considered as interval valued. Then, the continuous change of inventory level with time of the proposed model is defined mathematically by the interval differential equations. For both cases of the proposed model, the interval valued average profits are obtained by using parametric approach of interval. Here, the selling price and business period are taken as decision variables of the corresponding interval maximization problem. Finally, two numerical examples are considered and solved by using different meta-heuristic algorithms for checking the validity of the proposed model.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Inventory Interval differential equation Prepayment Parametric approach meta-heuristic algorithms

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Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges

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APPLIED ENERGY 2021年 301卷 117446-117446页

作者： Lu, Peng Ye, Lin Zhao, Yongning Dai, Binhua Pei, Ming Tang, Yong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China China Elect Power Res Inst State Key Lab Power Gird Safety & Energy Conserva Beijing 100192 Peoples R China

The integration of large-scale wind power introduces issues in modern power systems operations due to its strong randomness and volatility. These issues can be resolved via wind power forecasting that can provide comprehensive future information about wind power uncertainties. This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting. The framework is based on the auxiliary layer, forecasting base layer, and core layer. The auxiliary layer, such as the data-decomposition layer, decomposes the wind power time series into many relatively stationary subseries, and uses prediction models, including artificial neural networks (ANNs) and machine learning (ML). The core layer is based on meta-heuristic algorithms, which include evolutionary-based algorithms, physics-based algorithms, human-based algorithms, swarm-based algorithms, hybrid algorithms, and multi-objective optimization algorithms. These algorithms aim to search for the optimal solutions under constraints, which is highly significant for optimizing the key parameters of the prediction models. Besides, multiple error evaluation metrics, e.g., deterministic, uncertainty, and testing methods used in the field of wind power prediction are described. A quantitative analysis focusing on their advantages, disadvantages, forecasting accuracy, and computational costs are also provided. Finally, a few open research issues and trends related to the topic are discussed, which can contribute to improving the understanding of each wind power forecasting method. In general, this review paper provides valuable insights to wind power engineers.

关键词： meta-heuristic algorithms Wind power forecasting Combined approach Multiple time horizons Multiple error evaluation metrics

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Comparative study of meta-heuristic algorithms for reactor fuel reloading optimization based on the developed BP-ANN calculation method

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ANNALS OF NUCLEAR ENERGY 2022年 165卷

作者： Li, Zhan Huang, Jie Wang, Jincheng Ding, Ming Harbin Engn Univ Heilongiiang Prov Key Lab Nucl Power Syst & Equi Harbin 150001 Peoples R China China Nucl Power Technol Res Inst CO LTD Shenzhen 518026 Peoples R China

In order to obtain optimal core configurations, the problems of fuel reloading optimization must be solved in nuclear engineering field. This problem is a combinational optimization problem with an enormous solution space. In the traditional calculation method, the meta-heuristic algorithms (MHAs) are used to search and update optimization solutions continuously, while the core physics calculation programs are used to calculate the optimization parameters. In this research, we developed a new BP ANN calculation method to solve the problems of fuel reloading optimization for thorium-based block type high temperature gas-cooled reactors (HTGRs). In this method, the function of core physics calculation programs are replaced by BP-ANNs in order to reduce calculation time. Besides, five different MHAs, which are genetic algorithm (GA), particle swarm optimization (PSO), Teaching-learning based optimization algorithm (TLBO), harmony search (HS) and Rank-based ant system (RAS), are used under this new method to solve the problems of fuel reloading optimization, respectively. The results shows that PSO is the best MHA with a real feasible solution ratio of 0.30 and the best fitness function value of 0.455904. Compared to the traditional calculation method, the calculation time of BP-ANN calculation method is reduced to 0.7% of the former. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Fuel reloading optimization Traditional calculation method BP-ANN calculation method meta-heuristic algorithms

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Predicting the carbon dioxide emission caused by road transport using a Random Forest (RF) model combined by meta-heuristic algorithms

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SUSTAINABLE CITIES AND SOCIETY 2023年 93卷

作者： Khajavi, Hamed Rastgoo, Amir KN Toosi Univ Technol Dept Civil Engn Tehran Iran

Carbon dioxide is one of the most important pollutants in urban areas. Since the relationship between the factors of road transport and CO2 emission is often complex, using methods based on computational intelligence can be useful. In this way, a hybrid Random Forest, support vector regression, and response surface methodology are implemented to predict CO2 emission in 30 major cities in China. Also, seven optimizers are applied to the Random Forest, and two optimizers are applied to the support vector regression methods to tune their hyper -parameters. The mentioned methods' accuracy is compared through the Standard Error (SE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), and the coefficient of determination (R2) statistical indexes. The obtained results reveal that the support vector regression with Harris Hawk optimizer has the best accuracy in the training process with an R2 value of 0.9999, and the Random Forest with the Slime Mould Algorithm with an R2 value of 0.9641 has the best accuracy in the testing process. Hence, the Random Forest with Slime Mould Algorithm (RF-SMA) is the best method to predict CO2 emission.

关键词： Carbon dioxide emission Road transport Random Forest meta-heuristic algorithms Slime Mould Algorithm

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Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms

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ENERGY 2023年第1期272卷

作者： Khajavi, Hamed Rastgoo, Amir KN Toosi Univ Technol Dept Civil Engn Tehran Iran

The growing population has caused to increase in energy demand worldwide. Since significant energy con-sumption in the residential building sector is assigned to the heating. Thus, forecasting consumed energy is effective for planning and providing future demand. However, the prediction of the consumed energy of heating systems in residential buildings due to various variables is complex. This study presents a model based on ma-chine learning to improve the prediction of heating energy consumption in residential buildings. In this regard, Support Vector Regression (SVR) was selected as the central core of problem-solving. Then, using the hybridi-zation technique, SVR was combined with six meta-heuristic algorithms to tune and optimize the hyper -parameters of the SVR algorithm. Finally, the proposed method's performance and accuracy compared to hybrid models by conducting a case study were investigated. The results show that the proposed method can accurately predict the heating energy consumed in residential buildings. Also, it is shown that the hybrid SVR-Battle Royale Optimization model had the best performance among all investigated hybrid models. For example, the R2 values of this model are equal to 0.999386 and 0.998898 for the train and test datasets, which are the highest values among all models.

关键词： Consumed heating energy Residential buildings Support vector regression (SVR) meta-heuristic algorithms Battle royale optimization (BRO)

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Meshless Search SR-STAP for Airborne Radar Based on meta-heuristic algorithms

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SENSORS 2023年第23期23卷 9444-9444页

作者： Hou, Yunfei Zhang, Yingnan Gui, Wenzhu Wang, Di Dong, Wei Jilin Univ State Key Lab Integrated Optoelect Changchun 130012 Peoples R China

The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples. However, when the cluttering is nonlinear in a spatial-Doppler profile, it will cause an off-grid effect and reduce the sparse recovery performance. A meshless search using a meta-heuristic algorithm (MH) can completely eliminate the off-grid effect in theory. Therefore, genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods are applied to SR-STAP for selecting exact clutter atoms in this paper. The simulation results show that MH-STAP can estimate the clutter subspace more accurately than the traditional algorithm;PSO-STAP and GWO-STAP showed better clutter suppression performance in four MH-STAP methods. To search for more accurate clutter atoms, PSO and GWO are combined to improve the method's capacity for global optimization. Meanwhile, the fitness function is improved by using prior knowledge of the clutter distribution. The simulation results show that the improved PSO-GWO-STAP algorithm provides excellent clutter suppression performance, which solves the off-grid problem better than does single MH-STAP.

关键词： space-time adaptive processing (STAP) off-grid effect clutter subspace meta-heuristic algorithms

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Performance Comparison of Hybrid Neuro-Fuzzy Models using meta-heuristic algorithms for Short-Term Wind Speed Forecasting

ELECTRICA

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ELECTRICA 2021年第3期21卷 305-321页

作者： Dokur, Emrah Yuzgec, Ugur Kurban, Mehmet Bilecik Seyh Fdebali Univ Dept Elect & Elect Engn Energy Technol Applicat & Res Ctr Bilecik Turkey Bilecik Seyh Fdebali Univ Dept Comp Engn Bilecik Turkey

In this paper, short-term wind speed forecasting models have been developed using neuro-fuzzy systems. The optimal neuro-fuzzy model has been investigated in detail. In addition, meta-heuristic algorithms, such as artificial bee colony differential evolution genetic algorithm and particle swarm optimization for training adaptive neuro-fuzzy Inference systems parameters have been used in this study. This is a novel study, as four different meta-heuristic approaches are used to determine the appropriate adaptive neuro-fuzzy inference systems model parameters for short-term wind speed estimation, and analyzed comparatively. To validate the effectiveness of the proposed approach, wind speed series collected from a wind observation station located in Turkey are used in the short-term wind speed forecasting. In the first step, the results of analysis for finding the accurate model revealed that the optimal model that is proposed is adaptive neuro-fuzzy inference systems,.. architecture. The meta-heuristic algorithms used in the optimization of adaptive neuro-fuzzy inference systems model parameters are then independently run 10 times, and the performance results are calculated statistically for the training and test phases of the adaptive neuro-fuzzy inference systems model. The results of the study clearly show that the adaptive neuro-fuzzy inference systems-particle swarm optimization hybrid model has the test performance in the training aspect, but it is observed that the ANFIS-differential evolution hybrid model gives better results than the others in the test step.

关键词： Artificial intelligence forecasting neuro-fuzzy meta-heuristic algorithms wind energy

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New design of robust PID controller based on meta-heuristic algorithms for wind energy conversion system

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WIND ENERGY 2020年第2期23卷 391-403页

作者： Elsisi, Mahmoud Natl Taiwan Univ Sci & Technol Ind Implementat Ctr 4 0 Taipei Taiwan Benha Univ Fac Engn Shoubra Elect Engn Dept 108 Shoubra StPOB 11241 Cairo Egypt

This paper proposes a new robust control method for a wind energy conversion system. The suggested method can damp the deviations in the generator speed because of the penetration of wind speed and load demand fluctuations in the electrical grid. Furthermore, it can overcome the uncertainties of the plant parameters because of load demand fluctuations and the errors of the implementation. The new method has been built based on new simple frequency-domain conditions and the whale optimization algorithm (WOA). This method is utilized to design a robust proportional-integral-derivative (PID) controller based on the WOA in order to enhance the damping characteristics of the wind energy conversion system. Simulation results confirm the superiority and robustness of the proposed technique against the wind speed fluctuations and the plant parameters uncertainties compared with other meta-heuristic algorithms.

关键词： frequency-domain constraints meta-heuristic algorithms robust PID controller wind energy conversion system

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