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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Stanovov, Vladimir Semenkin, Eugene Institute of Informatics and Telecommunication Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

ISBN: (纸本)9798350308365

One of the most important problems of the Differ-ential Evolution algorithm is the adaptation of scaling factor, due to high sensitivity to this parameter. In this paper the L-SRTDE algorithm is proposed, where the scaling factor is set based on the ratio of improved solutions at each generation, i.e. success rate. The proposed algorithm is used to solve the benchmark problems from the CEC 2024 Bound Constrained Single Objective Numerical optimization competition. Additional experiments are performed using the CEC 2017 and CEC 2022 benchmarks. The analysis of numerical results, considering both accuracy and speed of the algorithms shows that the proposed approach is capable of outperforming many alternative approaches, which use success history-based adaptation. © 2024 IEEE.

关键词： optimization algorithms

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Stability and Generalization for Stochastic Recursive Momentum-based algorithms for (Strongly-)Convex One to K-Level Stochastic optimizations 41

Stability and Generalization for Stochastic Recursive Moment...

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41st International Conference on Machine Learning, ICML 2024

作者： Pan, Xiaokang Li, Xingyu Liu, Jin Sun, Tao Sun, Kai Chen, Lixing Qu, Zhe Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Department of Computer Science Tulane University New Orleans United States College of Computer National University of Defense Technology Changsha China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Institute of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China

STOchastic Recursive Momentum (STORM)based algorithms have been widely developed to solve one to K-level (K ≥ 3) stochastic optimization problems. Specifically, they use estimators to mitigate the biased gradient issue and achieve near-optimal convergence results. However, there is relatively little work on understanding their generalization performance, particularly evident during the transition from one to K-level optimization contexts. This paper provides a comprehensive generalization analysis of three representative STORM-based algorithms: STORM, COVER, and SVMR, for one, two, and K-level stochastic optimizations under both convex and strongly convex settings based on algorithmic stability. Firstly, we define stability for K-level optimizations and link it to generalization. Then, we detail the stability results for three prominent STORM-based algorithms. Finally, we derive their excess risk bounds by balancing stability results with optimization errors. Our theoretical results provide strong evidence to complete STORM-based algorithms: (1) Each estimator may decrease their stability due to variance with its estimation target. (2) Every additional level might escalate the generalization error, influenced by the stability and the variance between its cumulative stochastic gradient and the true gradient. (3) Increasing the batch size for the initial computation of estimators presents a favorable trade-off, enhancing the generalization performance. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Enhancing Dynamic Constrained Multi-Objective optimization With Multi-Centers Based Prediction

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IEEE Transactions on Evolutionary Computation 2025年

作者： Gong, Quan Xia, Yizhang Zou, Juan Hou, Zhanglu Liu, Yuan Xiangtan University Hunan Engineering Research Center of Intelligent System Optimization and Security Hunan Province Xiangtan411105 China

Dynamic constrained multi-objective optimization problems (DCMOPs) involve complex changes in objective functions and constraints over time. These changes challenge most existing algorithms to quickly cross infeasible regions and accurately track the changing Pareto optimal set (POS) and Pareto optimal front (POF). To address this issue, this paper presents a multi-centers based prediction strategy, termed FCP, for solving DCMOPs more effectively. First, we introduce a penalty function to cluster the historical optimal solutions, thereby obtaining multi-centers of these solutions. These centers can roughly represent the distribution of different clusters in POS. Then, we predict cluster centers of the new environment's POS by calculating the distance of centers from the preceding two environments. The prediction strategy can handle the change of POS caused by constraints thereby improving the accuracy of prediction. Finally, a proposed population generator calculates the distances between new centers and utilizes information from these centers to predict a well-distributed initial population. Comprehensive studies on widely used benchmark problems demonstrate that our proposed algorithm is very competitive in dealing with DCMOPs compared with seven state-of-the-art algorithms. Meanwhile, to validate the proposed prediction strategy, it is embedded into the static constraints handling techniques from other DCMOEAs to solving DCMOPs and the experimental results indicate that FCP is superior in generating initial population. © 1997-2012 IEEE.

关键词： optimization algorithms

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Constraint-Pareto Dominance and Diversity Enhancement Strategy based Evolutionary Algorithm for Solving Constrained Multiobjective optimization Problems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Liu, Zhe Han, Fei Ling, Qinghua Han, Henry Jiang, Jing Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Hefei Comprehensive National Science Center Anhui Energy Laboratory Institute of Energy China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang212013 China Jiangsu University of Science and Technology School of Computer Science Zhenjiang212003 China Baylor University School of Engineering and Computer Science Waco76798 United States Anqing Normal University School of Computer and Information Anqing246011 China

The utilization of both constrained and unconstrained-based optimization for solving constrained multi-objective optimization problems (CMOPs) has become prevalent among recently proposed constrained multiobjective evolutionary algorithms (CMOEAs). However, the constrained-based optimization which adopted by many CMOEAs typically gives priority to feasible solutions over infeasible ones regardless of their objective values, potentially leading to degraded performance due to the elimination of promising infeasible solutions with strong convergence and diversity. Furthermore, many existing CMOEAs have difficulty in maintaining diversity while focusing on feasibility, thereby hindering their ability to effectively address CMOPs characterized by complex feasible regions. To tackle these challenges, a constraint-Pareto dominance relationship is proposed in this paper to evaluate solutions based on both objectives and feasibility, to improve the optimization potential by reduce the elimination probability of promising infeasible solutions. A diversity enhancement strategy is also designed to enable simultaneously focus on both diversity and feasibility, thus effectively ensuring the diversity of the feasible solutions obtained. Empirical results from benchmark suites and real-world problems demonstrate that our proposed algorithm surpasses state-of-the-art CMOEAs. © 1997-2012 IEEE.

关键词： optimization algorithms

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Load and Mutual Inductance Recognition Method for LCC-S MCRWPT Systems Based on Improved Dung Beetle optimization Algorithm

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IEEE Transactions on Transportation Electrification 2025年

作者： Guo, Zhen Nai, Jiqiu Zhang, Hailong Chen, Shuang Ye, Minghao Yu, Haitao Li, Zhaodong Tian, Yanbing Zhang, Min Qingdao University of Technology School of Information and Control Engineering Qingdao266520 China

One of the key issues in improving the performance of magnetically coupled wireless power transfer (WPT) systems is the identification of load and mutual inductance parameters. This study proposes an improved dung beetle optimization algorithm for identifying load and mutual inductance in an LCC-S type magnetically coupled WPT system. By neglecting the influence of higher-order harmonics in the system, a steady-state circuit model is established, providing a mathematical description of the system's circuit parameters and state variables. On this basis, the parameter identification problem of the system is transformed into an optimization problem, and an improved dung beetle optimization algorithm is employed to identify the system's load and mutual inductance parameters. Through parameter identification, further control can be achieved, such as efficiency maximization and adjustment of the DC-DC circuit duty cycle for maximum efficiency tracking based on the identified mutual inductance and load. This identification method does not require additional circuits or related control, thereby reducing circuit complexity and system size. Simulation and experimental results verify the feasibility and effectiveness of this identification method. © 2015 IEEE.

关键词： optimization algorithms

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An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第44期29卷 67180-67213页

作者： Jalil-Masir, Hamed Fattahi, Rohollah Ghanbari-Adivi, Elham Aghbolaghi, Mahdi Asadi Ehteram, Mohammad Ahmed, Ali Najah El-Shafie, Ahmed Shahrekord Univ Dept Water Sci Engn Shahrekord Iran Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Univ Tenaga Nasl UNITEN Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates

Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR-vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D-50/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D-50/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.

关键词： Sediment transport rate Flexible vegetation cover Coast optimization algorithms Kernel extreme learning model

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Multi-layer controller with state-constraint: Vehicle lateral stability control based on fuzzy logic

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2022年第1期236卷 155-167页

作者： Wan, Neng Zeng, Guangping Zhang, Chunguang Pan, Dingqi Cai, Songtao Univ Sci & Technol Beijing Sch Comp & Commun Engn 30 Xueyuan Rd Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Knowledge Engn Mat Sci 30 Xueyuan Rd Beijing 100083 Peoples R China

This paper deals with a new state-constrained control (SCC) system of vehicle, which includes a multi-layer controller, in order to ensure the vehicle's lateral stability and steering performance under complex environment. In this system, a new constraint control strategy with input and state constraints is applied to calculate the steady-state yaw moment. It ensures the vehicle lateral stability by tracking the desired yaw rate value and limiting the allowable range of the side slip. Through the linkage of the three-layer controller, the tire load is optimized and achieve minimal vehicle velocity reduction. The seven-degree-of-freedom (7-DOF) simulation model was established and simulated in MATLAB to evaluate the effect of the proposed controller. Through the analysis of the simulation results, compared with the traditional ESC and integrated control, it not only solves the problem of obvious velocity reduction, but also solves the problem of high cost and high hardware requirements in integrated control. The simulation results show that designed control system has better performance of path tracking and driving state, which is closer to the desired value. Through hardware-in-the-loop (HIL) practical experiments in two typical driving conditions, the effectiveness of the above proposed control system is further verified, which can improve the lateral stability and maneuverability of the vehicle.

关键词： Vehicle dynamics control state constraints fuzzy logic lateral stability optimization algorithms

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Compressive strength estimation of eco-friendly geopolymer concrete: Application of hybrid machine learning techniques

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STEEL AND COMPOSITE STRUCTURES 2022年第6期45卷 877-894页

作者： Yang, Xiang Daibo, Jiang Gou, Hateo Chongqing Vocat Inst Engn Sch Civil Engn Chongqing 402260 Peoples R China Chongqing Technol & Business Inst Logist Base Chongqing 401520 Peoples R China Bldg Dept Shandong Univ Jinan 250000 Peoples R China

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO2 emissions in the construction industry. The compressive strength (f(c)) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f(c) of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the f(c) of GPC forecasting process.

关键词： artificial intelligence compressive strength eco-friendly concrete geopolymer concrete optimization algorithms prediction

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Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第7期36卷 1835-1860页

作者： Achite, Mohammed Banadkooki, Fatemeh Barzegari Ehteram, Mohammad Bouharira, Abdelhak Ahmed, Ali Najah Elshafie, Ahmed Univ Chlef Water & Environm Lab Hassiba Benbouali BP 78C Ouled Fares Chlef 02180 Algeria Natl Higher Sch Agron ENSA Hassan Badi Algiers 16200 Algeria Payame Noor Univ Dept Agr Tehran Iran Semnan Univ Dept Water Engn Semnan Iran Univ Hassiba Benbouali Chlef Lab Water & Environm Chlef 02180 Algeria Univ Tenaga Nasional UNITEN Dept Civil Engn Coll Engn Klang 43000 Selangor Malaysia Univ Malaya Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates

Forecasting drought is essential for water resource management when policymakers encounter a water shortage and high demand. This research utilizes the Bayesian averaging model (BMA) based on multiple hybrid artificial neural network models including ANN- water strider algorithm (WSA), ANN-particle swarm optimization (ANN-PSO), ANN-salp swarm algorithm (ANN-SSA), and ANN-sine cosine algorithm (ANN-SCA) to forecast standardized precipitation index as one of the most important indices of drought. The models were used to forecast Standardized Precipitation Index (SPI) SPI (1), SPI (3), SPI (6), and SPI (12) in the Wadi Ouahrane basin of Algeria. The WSA, SSA, SCA, and PSO were applied to set model parameters of the ANN model. The inputs were lagged El Nino-Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), North Atlantic oscillation index (NAO), and southern oscillation index (SOI). The gamma test was integrated with WSA to identify the best input scenario for forecasting drought. The BMA for forecasting SPI (1) improved the MAE attained by the ANN-WSA, ANN-SSA, ANN-SCA, ANN-PSO, and ANN models 26, 33, 38, 42, and 46%, respectively in the testing level. The MAE of BMA for forecasting SPI (6) was 40, 42, 46, 48, and 62% lower than those of ANN-WSA, ANN-SSA, ANN-SCA, ANN-PSO, and ANN-PSO. Also, the BMA and ANN-WSA had the best accuracy among other models for forecasting SPI (6) and SPI (12). This study indicated that the WSA, SSA, SCA, and PSO improved the accuracy of the ANN models for forecasting drought.

关键词： ANN Forecasting drought optimization algorithms SPI

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Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第11期36卷 3875-3910页

作者： Ehteram, Mohammad Graf, Renata Ahmed, Ali Najah El-Shafie, Ahmed Semnan Univ Dept Water Engn Semnan Iran Adam Mickiewicz Univ Inst Phys Geog & Environm Planning Dept Hydrol & Water Management PL-61680 Poznan Poland Univ Tenaga Nasional UNITEN Dept Civil Engn Coll Engn Kajang 43000 Selangor Malaysia Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia

Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water resources and planning management. The present study uses Bayesian Model Averaging (BMA) based on developed and optimized Kernel Extreme Learning Machine (KELM) models for predicting daily evaporation in different provinces of Iran with different climates. The Water Strider Algorithm, Salp Swarm Algorithm, Shark Algorithm, and Particle Swarm optimization were combined with the KELM to predict daily evaporation in the Hormozgan, Mazandaran, Fars, Yazd, and Isfahan provinces. The models' inputs were average temperature, rainfall, number of sunny hours, wind speed, and relative humidity. The introducing a new hybrid gamma test for determining the adequate inputs, using hybrid and optimized KELM based on developing ELM for predicting evaporation, integrating individual models for predicting evaporation, and quantifying the uncertainty of outputs are the main innovations of the current study. Multiple error indices were used to evaluate the ability of models for predicting evaporation. The standalone and optimized KELM models were used to predict daily evaporation in the first level. In the next level, the BMA based on outputs of standalone and optimized KELM models predicted daily pan evaporation. The general results indicated that the BMA provided the best accuracy among other models in all stations. This study also introduced the new hybrid gamma test (GT-WSA) for choosing the best input combinations. The hybrid GT-WSA gave the best input combination without computing all input combinations (2(5) - 1). The uncertainty analysis of models also indicated that the uncertainty of BMA and optimized KELM models was lower than that of the KELM model.

关键词： Kernel Extreme Learning Machine model Evaporation optimization algorithms Bayesian Model Averaging

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