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Machine-Learning-assisted Swarm Intelligence algorithm for Antenna Optimization With Mixed Continuous and Binary Variables

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第3期73卷 1662-1673页

作者： Fu, Kai Leung, Kwok Wa City Univ Hong Kong State Key Lab Terahertz & Millimeter Wave Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China CityU Shenzhen Res Inst Informat & Commun Technol Ctr Shenzhen 518057 Peoples R China

Many existing surrogate-assisted optimization algorithms are limited to designing antennas with continuous variables only. However, numerous challenges emerge when tackling antenna optimization problems that involve both continuous and binary design variables. This article proposes an efficient surrogate-assisted mixed continuous/binary particle swarm optimization (SAMPSO) algorithm to address these mixed-variable antenna optimization problems. The SAMPSO tightly integrates machine learning (ML) models with PSO in two key aspects: an ML-guided swarm updating method and an ML-assisted prescreening strategy. In addition, a novel local ML model training method is developed to reduce the algorithm time complexity. To verify its effectiveness, the SAMPSO is compared with two existing algorithms in solving benchmark functions and designing antennas. The results demonstrate that SAMPSO can achieve design objectives with a faster convergence speed.

关键词： Optimization Antennas Vectors Computational modeling Particle swarm optimization Heuristic algorithms Optimization methods Accuracy Training Partitioning algorithms Antenna optimization mixed-variable optimization particle swarm optimization (PSO) surrogate-assisted evolutionary algorithm

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Bi-Level Model Management Strategy for Solving Expensive Multi-Objective Optimization Problems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 332-346页

作者： Li, Fei Yang, Yujie Liu, Yuhao Liu, Yuanchao Qian, Muyun Anhui Univ Technol Dept Elect & Informat Engn Anhui Prov Key Lab Met Engn & Resources Recycling Anhui Prov Engn Lab Intelligent D/emolit Equipment Maanshan 243002 Anhui Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Anhui Univ Technol Dept Elect & Informat Engn Maanshan 243002 Anhui Peoples R China

Model management strategy is the main component of surrogate-assisted evolutionary algorithms for solving expensive multi-objective optimization problems(EMOPs). In such problems, evaluating the true fitness function requires significant computational resources, which necessitates effective determination of which individuals should be selected for evaluation. However, existing model management strategies often struggle to effectively balance exploration and exploitation when selecting individuals. To mitigate this issue, a bi-level model management strategy is proposed. The selection procedure not only considers exploring the objective space by balancing predicted values and uncertainty in the lower-level but also considers convergence and diversity in the upper-level selection. Specifically, we first divide the objective space into some sub-spaces through a set of direction vectors. Then, we select the first rank individuals via adopting the non-dominated sorting to balance the predicted objective values and the uncertainty in each subspace. The intersection individuals of the selected candidates can be denoted as the lower-level solutions. In the upper-level selection, we combine the modified inverted generational distance (IGD(+)) and shift-based density estimation (SDE) indicators to select the most promising individuals from the lower-level to update the model. Experimental results on benchmark instances show that the proposed algorithm is competitive compared with some representative algorithms.

关键词： Optimization Uncertainty evolutionary computation Vectors Predictive models Convergence Load modeling surrogate-assisted evolutionary algorithm expensive multi-objective optimization bi-level model management strategy performance indicators Kriging models

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Multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization for expensive optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Li, Chao Zhang, Quanshu Palade, Vasile Lu, Hengyang Sun, Jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China Coventry Univ Ctr Computat Sci & Math Modelling Coventry CV1 2TL England

surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multimodal problems, especially high-dimensional ones. Moreover, for problems with complex landscapes, SAEAs typically require constructing complex global surrogates to model the landscape and performing many iterations to identify the surrogate's optimum, thereby reducing the efficiency of SAEAs. To deal with these issues, this paper proposes a multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization (MHS-QPSO) algorithm for expensive optimization problems. To better balance exploration and exploitation, a search behavior selection strategy is proposed, enabling MHS-QPSO to appropriately switch between global and local searches. For the global search, the search space is divided into multiple regions that can adaptively adjust the size of the areas. A surrogate is constructed in each region, requiring only a small number of QPSO iterations to find the optimum of each surrogate. Furthermore, a novel reliability-based criterion is proposed to screen candidate solutions in different regions for exact evaluations, which can save the number of exact function evaluations and can rapidly improve the fitting accuracy of the surrogates in regions with superior fitness. During local searches, a dynamic boundary adjustment strategy is introduced to guide the QPSO to faster approach the potential optimal region. Experimental results on seven benchmark functions with dimensions from 10 to 100, and on a complex real application, demonstrate that MHS-QPSO significantly outperforms several state-of-the-art algorithms within a limited computational budget. Code for MHS-QPSO is available at https://***/quanshuzhang/***.

关键词： surrogate-assisted evolutionary algorithm (SAEA) Quantum-behaved particle swarm optimization (QPSO) Multi-region search Hierarchical optimization algorithm Reliability-based criterion

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Two-layer surrogate-assisted collaborative framework for expensive constrained optimization problems involving mixed integer variables

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INFORMATION SCIENCES 2025年 690卷

作者： Liu, Jiansheng Chen, Jin Yang, Zan Liu, Yuanhao Qiu, Haobo Gao, Liang Nanchang Univ Sch Adv Mfg Nanchang 330031 Peoples R China Jiangxi Tellhow Scitech Co Ltd Nanchang 330031 Peoples R China Nanchang Univ Jiangxi Prov Key Lab Light Alloy Nanchang 330031 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Natl Ctr Technol Innovat Intelligent Design & Nume Wuhan 430074 Peoples R China

Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown exceptional abilities in solving expensive constrained optimization problems (ECOPs). However, few of them are designed for ECOPs involving mixed integer variables (ECOPs-MI). Therefore, a two-layer surrogate-assisted collaborative framework called TLSACF is designed, in which the radial basis function (RBF) and Gaussian Process (GP)-based mixed-integer collaborative frameworks are combined effectively. In the RBF-based collaborative framework, the combination between RBFassisted prescreening and RBF-based local search achieves effective population update by balancing the exploration and exploitation on high potential parental information. For the GPbased collaborative framework, a variable type-based cooperative mutation is utilized to obtain potential candidate individuals by assigning targeted mutation for different variables, and a hypervolume-based complete expected improvement matrix function is derived to prescreen these candidate solutions from the perspective of balancing the optimization on constraints and the objective for the effective search on disconnected sub-feasible regions caused by integer variables. Experimental results on ten classical problems show that TLSACF performs excellent in solving ECOPs-MI.

关键词： Mixed integer variables surrogate-assisted evolutionary algorithm Variable type-based cooperative mutation Hypervolume-based complete expected improvement matrix function

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Lightweight design optimization of two-layer corrugated cored sandwich panel under blast loading using surrogate-assisted different evolution for mixed-integer variables

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ENGINEERING STRUCTURES 2024年 321卷

作者： Liu, Yuanhao Yang, Zan Jiang, Chen Xu, Danyang Qiu, Haobo Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Jiangxi Tellhow Sci Tech Co Ltd Nanchang 330031 Peoples R China Nanchang Univ Jiangxi Prov Key Lab Light Alloy Nanchang 330031 Peoples R China Natl Ctr Technol Innovat Intelligent Design & Nume Wuhan 430074 Peoples R China

The sandwich structure stands out as an ideal candidate for blast-resistant armor due to its excellent performance, of which the optimization typically involves time-consuming finite element simulations. Recently, surrogate models have emerged as a promising alternative for time-consuming simulations to accelerate the optimization process of blast-resistant sandwich panels. However, existing methods are limited to optimizing a handful of continuous variables, and often focus on single-layer core sandwich panels. This article introduces a novel constrained optimization model for Two-layer composite Corrugated cored Sandwich Panels (TCSP). The design variables include not only continuous variables such as structural component heights and thicknesses, but also integer variables such as material selection and the number of core unit-cells. To tackle this complex optimization problem with mixed-integer variables, a surrogate-assisted differential evolution (SADE-TCSP) is proposed. This algorithm utilizes the predictive capabilities of surrogate models to guide the optimization process towards a high-quality feasible solution within a very limited computational budget. In order to validate our approach, an optimization framework integrating MATLAB and ABAQUS is developed to conduct numerical experiments. Experimental results show that the proposed method can achieve a 51.04 % reduction in total structural mass while satisfying performance constraints, which is significantly better than existing methods.

关键词： Sandwich structure Expensive constrained optimization Blast resistance surrogate-assisted evolutionary algorithm Finite element Mixed-integer

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Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第1-2期130卷 253-266页

作者： Wang, Xiaohan Zhang, Lin Laili, Yuanjun Liu, Yongkui Li, Feng Chen, Zhen Zhao, Chun Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China KTH Royal Inst Technol Dept Prod Engn S-10044 Stockholm Sweden Xidian Univ Sch Mechanoelect Engn Xian 710071 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Beijing Informat Sci & Technol Univ Comp Sch Beijing 100101 Peoples R China

Manufacturing systems develop toward cloud-edge collaboration where manufacturing and computation are tightly coupled. Under this circumstance, large-scale hybrid tasks that include manufacturing and computational tasks need to be collaboratively scheduled among heterogeneous resources. This paper solves the hybrid task scheduling problem in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution (F-RDE). First, we establish a system model for hybrid task scheduling with the objective of minimizing the total makespan and energy consumption. This model includes four types of time constraints between the hybrid tasks. The scheduling of such hybrid tasks has received limited attention in existing research. Next, large-scale decision variables are encoded into the evolutionary chromosomes. To generate offspring chromosomes, we construct four differential evolution operators that are randomly selected during the search process. Furthermore, we propose the fully convolutional regression network (FCRN) as a novel surrogate model to accelerate fitness evaluation. To enhance the integration of FCRN and the differential evolution procedure, we employ three strategies: chromosome folding, top-K re-evaluation, and three training modes. The FCRN surrogate can effectively represent chromosomes with up to 12000 dimensions and achieve generalization across diverse scheduling cases. This leads to reduced solving time and enhanced fitness estimation accuracy. Numerical experiments on three hybrid task scheduling cases validate the superiority compared to the other twelve scheduling algorithms, and the proposed FCRN surrogate can save at most 43% of solving time.

关键词： Scheduling surrogate-assisted evolutionary algorithm Cloud manufacturing Intelligent manufacturing systems Cloud-edge collaboration

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Transfer Learning Based Co-surrogate assisted evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times

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evolutionary COMPUTATION 2022年第2期30卷 221-251页

作者： Wang, Xilu Jin, Yaochu Schmitt, Sebastian Olhofer, Markus Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Bielefeld Univ Fac Technol D-33615 Bielefeld Germany Honda Res Inst Europe GmbH Carl Legien Str 30 D-63073 Offenbach Germany

Most existing multiobjective evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is untenable in many real-world optimization scenarios where evaluation of different objectives involves different computer simulations or physical experiments with distinct time complexity. To address this issue, a transfer learning scheme based on surrogate-assisted evolutionary algorithms (SAEAs) is proposed, in which a co-surrogate is adopted to model the functional relationship between the fast and slow objective functions and a transferable instance selection method is introduced to acquire useful knowledge from the search process of the fast objective. Our experimental results on DTLZ and OF test suites demonstrate that the proposed algorithm is competitive for solving bi-objective optimization where objectives have non-uniform evaluation times.

关键词： Multiobjective optimization non-uniform evaluation times transfer learning co-surrogate Gaussian process surrogate-assisted evolutionary algorithm Bayesian optimization

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surrogate-assisted global and distributed local collaborative optimization algorithm for expensive constrained optimization problems

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SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Liu, Xiangyong Yang, Zan Liu, Jiansheng Xiong, Junxing Huang, Jihui Huang, Shuiyuan Fu, Xuedong Nanchang Univ Sch Adv Mfg Nanchang 330031 Peoples R China Jiangxi Tellhow Scitech Co Ltd Nanchang 330031 Peoples R China Engn Technol Res Ctr Mfg Informatizat Jiangxi Prov Nanchang 330031 Peoples R China Nanchang Univ Sch Math & Comp Sci Nanchang 330031 Peoples R China Jiangxi Tellhow Power Technol Co Ltd Nanchang 330031 Peoples R China

This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed collaboratively at each generation. As the complexity of optimization problems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions. For global surrogate-assisted collaborative evolution phase, the global candidate set is generated through classification collaborative mutation operations to alleviate the pre-screening pressure of the surrogate model. For local surrogate-assisted phase, a distributed central region local exploration is designed to achieve intensively search for promising distributed local areas which are located by affinity propagation clustering and mathematical modeling. More importantly, a three-layer adaptive selection strategy where the feasibility, diversity and convergence are balanced effectively is designed to identify promising solutions in global and local candidate sets. Therefore, the SGDLCO efficiently balances global and local search during the whole optimization process. Experimental studies on five classical test suites demonstrate that the SGDLCO provides excellent performance in solving expensive constrained optimization problems.

关键词： surrogate-assisted evolutionary algorithm Expensive constrained optimization problems Global search Local search

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A surrogate-assisted differential evolution algorithm with a dual-space-driven selection strategy for expensive optimization problems

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COMPLEX & INTELLIGENT SYSTEMS 2025年第5期11卷 1-29页

作者： Liu, Hanqing Ren, Zhigang He, Chenlong Du, Wenhao Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian Peoples R China

surrogate-assisted evolutionary algorithms (SAEAs) have shown great potential to solve computationally expensive optimization problems (EOPs). Their two key components, i.e., the optimizer and the surrogate model, both need to select solutions to promote further evolution and to update the model, respectively. However, the corresponding selection strategies are designed independently and mainly consider the predicted fitness of candidate solutions. Consequently, they can hardly complement each other well and are much likely to mislead the population evolution since the surrogate model necessarily has some prediction errors. Directing against this issue, this study proposes a unified dual-space-driven selection (DSDS) strategy and develops a new SAEA by taking differential evolution and radial basis function as the optimizer and the surrogate model, respectively. Besides the predicted fitness in objective space of candidate solutions, DSDS also considers their distribution in decision space by measuring how close they are to the geometric centers of their respective neighborhoods with a new indicator called neighborhood centrality (NC). It tries to select the solutions with good fitness and small NC values under a multi-objective sample selection framework and thus helps the resulting SAEA steadily search in multiple neighborhoods of excellent solutions. The performance of the new SAEA was extensively tested on six commonly used benchmark functions with five different dimensions from 20 to 200 as well as two typical real-word traffic signal optimization cases. Experimental results demonstrate that it possesses more competitive performance and stronger robustness than state-of-the-art SAEAs.

关键词： Expensive optimization problem surrogate-assisted evolutionary algorithm Differential evolution Radial basis function Selection strategy

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Design optimization of composite wind turbine blade using complete constrained expected improvement-subset simulation optimization

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RENEWABLE ENERGY 2025年 249卷

作者： Ma, Yuan-Zhuo Wei, Jia Liu, Wei-Dong Zhi, Peng-Peng Zhao, Zhen-Zhou Xu, Chang Li, Hong-Shuang Hohai Univ Coll Renewable Energy Nanjing Peoples R China Hohai Univ Coll Elect & Power Engn Nanjing Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou Peoples R China Inner Mongolia Univ Technol Key Lab Wind & Solar Energy Utilizat Technol Minist Educ Hohhot Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing Peoples R China

Design: optimization of the large-scale composite wind turbine blade during conceptual design is one of the key processes of the cost decreasing and benefit increasing for current wind power industry. It however, still remains several key issues, such as lack of a fully parametric process, suffering from a huge computational burden and being easily trapped into local optimum. To remedy these issues, this paper proposes a design optimization method for composite wind turbine blade using Complete Constrained Expected Improvement-Subset Simulation Optimization (CCEI-SSO). A fully parametric Finite Element Analysis (FEA) coded by ANSYS parametric design language of the composite wind turbine blade is firstly proposed, which can be linked to an arbitrary optimization method to form a unified joint simulation framework. To enhance the performance of the optimization process, CCEI-SSO is further proposed, where an adaptive Kriging model leveraging CCEI infill strategy is deeply coupled into each simulation level of SSO to keep balance of optimality and feasibility within very limited number of real FEAs. Inheriting from the random nature within SSO, local optimum is well avoided as well. A case of the design optimization of a 10 MW wind turbine blade is considered to demonstrate the performance of the proposed method.

关键词： Wind turbine blades Composite materials Fully parametric modeling Expensive constrained optimization surrogate-assisted evolutionary algorithm

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