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An intelligent moving object optimization algorithm for design problems with mixed variables, mixed constraints and multiple objectives

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2006年第1期32卷 40-58页

作者： Rahman, M. K. Univ Western Australia Sch Oil & Gas Engn Perth WA 6009 Australia

This paper presents an optimization algorithm for engineering design problems having a mix of continuous, discrete and integer variables;a mix of linear, non-linear, differentiable, non-differential, equality, inequality and even discontinuous design constraints;and conflicting multiple design objectives. The intelligent movement of objects (vertices and compounds) is simulated in the algorithm based on a Nelder-Mead simplex with added features to handle variable types, bound and design constraints, local optima, search initiation from an infeasible region and numerical instability, which are the common requirements for large-scale, complex optimization problems in various engineering and business disciplines. The algorithm is called an INTElligent Moving Object algorithm and tested for a wide range of benchmark problems. Validation results for several examples, which are manageable within the scope of this paper, are presented herein. Satisfactory results have been obtained for all the test problems, hence, highlighting the benefits of the proposed method.

关键词： non-linear constrained optimization direct-search algorithm Nelder-Mead algorithm mixed-variable optimization multi-objective optimization

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Optimal Design of a Thomson-Coil Actuator Utilizing a mixed-Integer-Discrete-Continuous variables Global optimization Algorithm

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IEEE TRANSACTIONS ON MAGNETICS 2011年第10期47卷 4163-4166页

作者： Pham, Minh-Trien Ren, Ziyan Li, Wei Koh, Chang Seop Chungbuk Natl Univ Coll Elect & Comp Engn Cheongju 361763 Chungbuk South Korea

To improve the performance of a Thomson-coil actuator, a new global optimization algorithm is proposed with mixed integer-discrete-continuous variables. In the algorithm, an augmented objective function is constructed by introducing a penalty function and an extended function to treat the discrete quantities as continuous ones. For the discrete objective function, two kinds of continuous function approximation methods, step function and linear function approximations, are suggested. The coupling particles swarm optimization method is then applied to the augmented objective function to find all the global and local optimal points. Finally, the validity of the proposed method is proven by comparing obtained optimization results with those from the enumeration algorithm.

关键词： Augmented objective function coupling particle swarm optimization discrete objective function global optimization mixed-variable optimization penalty function Thomson-coil actuator

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An Adaptive Estimation of Distribution Algorithm for Multipolicy Insurance Investment Planning

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2019年第1期23卷 1-14页

作者： Shi, Wen Chen, Wei-Neng Lin, Ying Gu, Tianlong Kwong, Sam Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cyberspace Informat Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Dept Psychol Guangzhou 510006 Guangdong Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Engn Guilin 541004 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Insurance has been increasingly realized as an important way of investment and risk aversion. Fruitful insurance products are launched by insurers, but there is little research on how to make a proper insurance investment plan for a specific policyholder given different kinds of policies. In this paper, we aim to propose a practical approach to multipolicy insurance investment planning with a data-driven model and an estimation of distribution algorithm (EDA). First, by making use of the insurance data accumulated in the modern financial market, an optimization model about how to choose endowment and hospitalization policies is built to maximize the yearly profit of insurance investment. With the model parameters set according to the real data from insurance market, the resulting plan is practical and individualized. Second, as the optimal solution cannot be achieved by mathematical deduction under this data-driven model, an EDA is introduced. To adapt the EDA for the considered problem, the proposed EDA is mixed with both the continuous and discrete probability distribution models to handle different kinds of variables. In addition, an adaptive scheme for choosing suitable distribution models and an efficient constraint handling strategy are proposed. Experiments under different conditions confirm the effectiveness and efficiency of the proposed model and method.

关键词： Data-driven endowment insurance estimation of distribution algorithm (EDA) hospitalization insurances mixed-variable optimization

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Evolution of Controllers Under a Generalized Structure Encoding/Decoding Scheme With Application to Magnetic Levitation System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第9期69卷 9655-9666页

作者： Xin, Bin Wang, Yipeng Xue, Wenchao Cai, Tao Fan, Zhun Zhan, Jiaoyang Chen, Jie Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Shantou Univ Minist Educ Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China

Evolutionary search has been widely implemented for the adjustment of controllers' parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimize both structures and parameters simultaneously in this article. A controller is made up of a combination of some basic controller components and relevant parameters. The design of controllers can be transformed into an optimization problem involving the structure (represented by discrete vectors) and parameters (represented by real numbers). A generalized structure encoding/decoding scheme is developed. Guided by the performance indicators, intelligent algorithms for both combinatorial and numerical optimization are employed to iteratively and cooperatively evolve the controller structure and parameters, respectively. In order to effectively reduce some redundant or infeasible solutions, a set of generation rules for the controller structure are put forward, which also ensures the feasibility of the structure. Furthermore, this method is applied to a magnetic levitation ball system with nonlinear dynamics and external disturbance. Both simulation and experiment results demonstrate the effectiveness and practicability of the proposed method.

关键词： optimization Design methodology Control systems Tuning Magnetic levitation Signal processing Periodic structures Automatic design of controllers disturbance-rejection intelligent optimization algorithms magnetic levitation system (MLS) mixed-variable optimization

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Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring

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IEEE ACCESS 2021年 9卷 109847-109864页

作者： Zhang, Hao Dou, Lihua Xin, Bin Chen, Jie Gan, Minggang Ding, Yulong Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Peng Cheng Lab Shenzhen 518052 Peoples R China

This article studies the data collection task planning for a fixed-wing unmanned aerial vehicle (UAV) in forest fire monitoring. Multiple wireless-based detection nodes (DNs) are distributed in high-risk areas of the forest to monitor the surrounding environment. The task of UAV is to circularly fly to them and collect the environmental data. Because of the kinematic constraints of UAV and the effective communication range between UAV and DN, this problem can be generally regarded as a Dubins traveling salesman problem with neighborhood (DTSPN). A bi-level hybridization-based metaheuristic algorithm (BLHMA) is proposed for solving this problem. At the first level, differential evolution (DE) optimizes the continuous-valued communication positions and UAV headings by the population-based search. For the asymmetric traveling salesman problem (ATSP) corresponding to the combination of the positions and headings generated by DE, a constructive heuristic based on self-organized multi-agent competition (SOMAC) is proposed to determine the discrete collection sequence. By competitive iterations in such a cooperative way in DE, a high-quality data collection tour can be generated. At the second level, a local search based on multistage approximate gradient is proposed to further refine the positions and headings, which accelerates the convergence of the BLHMA. Referring to a real-world scene of forest fire monitoring, the simulation experiments are designed, and comparative results show that BLHMA can find significantly shorter data collection tours in most cases over three state-of-the-art algorithms. The proposed UAV data collection planning algorithm is conducive to the efficient execution of the forest fire monitoring data collection mission and the energy saving of UAV.

关键词： Forestry Monitoring Data collection Unmanned aerial vehicles Task analysis Planning Traveling salesman problems Unmanned aerial vehicle forest fire monitoring data collection task planning mixed-variable optimization differential evolution constructive heuristic

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mixed Radial Basis Function Neural Network Training Using Genetic Algorithm

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NEURAL PROCESSING LETTERS 2023年第8期55卷 10569-10587页

作者： Elansari, Taoufyq Ouanan, Mohammed Bourray, Hamid Moulay Ismail Univ Fac Sci Dept Comp Sci BP 11201 Meknes 50070 Morocco

The Radial Basis Function Neural Network (RBFNN) is a feedforward artificial neural network employing radial basis functions as activation functions in the hidden layer. The output of the RBFNN is a linear combination of the outputs from the hidden layer. This paper present a mixed Radial Basis Function Neural Network (MRBFNN) training using Genetic Algorithm (GA). The choice of the type of Radial Basis Functions (RBFs) utilized in each hidden layer neuron has a significant impact on convergence, interpolation and performance. In this work, the authors are introducing a new approach to optimizing the choice of radial basis functions, centers, radius and weights of the output layer. We model in terms of mixed-variable optimization problems with linear constraints. To solve this model we will use an approach based on the genetic algorithm, allows us to determine the types of RBF to use in the hidden layer and the optimal weight of the output layer which gives us a good generalization. The results numerically demonstrate the performance of the theoretic results presented in this paper, as well as the benefits of the new modeling.

关键词： Radial basis function neural networks mixed-variable optimization Genetic algorithm Universal approximation

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Design of induction motors using a mixed-variable approach

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COMPUTATIONAL MANAGEMENT SCIENCE 2005年第3期2卷 213-228页

作者： Liuzzi, Giampaolo Lucidi, Stefano Piccialli, Veronica Villani, Marco Univ Roma La Sapienza Dipartimento Informat & Sistemist Via Buonarroti 12 I-00185 Rome Italy Univ Aquila Dipartimento Ingn Elettr I-67040 Laquila Italy

In this paper we are concerned with the problem of optimally designing three-phase induction motors. This problem can be formulated as a mixed variable programming problem. Two different solution strategies have been used to solve this problem. The first one consists in solving the continuous nonlinear optimization problem obtained by suitably relaxing the discrete variables. On the opposite, the second strategy tries to manage directly the discrete variables by alternating a continuous search phase and a discrete search phase. The comparison between the numerical results obtained with the above two strategies clearly shows the fruitfulness of taking directly into account the presence of both continuous and discrete variables.

关键词： Optimal design Induction Motors mixed-variable optimization Derivative-free methods

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Active optimization adjustment for the surface accuracy of spaceborne SAR antennas

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年第1期138卷

作者： Yu, Dewen Hu, Guobiao Cai, Saijie Yang, Yaowen Hong, Jun Xi An Jiao Tong Univ Sch Mech Engn Key Lab Modern Design & Rotor Bearing Syst Minist Educ Xian 710049 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Nanyang Ave Singapore 639798 Singapore Hong Kong Univ Sci & Technol Guangzhou Thrust Internet Things Guangzhou 511400 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Key Lab Thermo Fluid Sci & Engn Minist Educ Xian 710049 Peoples R China

Inevitable disturbances in the spatial thermal environment will seriously degrade the surface accuracy of satellite antennas. Unfortunately, the ground pre-adjustment cannot adaptively guarantee the antenna performance under alternating thermal loadings. To tackle the challenge, this study proposes an active optimization adjustment method to achieve the required surface accuracy for spaceborne antennas. Starting from the comprehensive analysis of external thermal fluxes in outer space, the heat transfer model is firstly established to acquire the temperature field of the antenna system. Subsequently, considering the thermoelastic effect and the geometrical nonlinearity, the antenna surface accuracy is predicted. In particular, the thermoelastic forces induced from temperature changes and dimensional deviations are precisely determined by the absolute nodal coordinate formulation. Moreover, an efficient computational method with invariant matrices is developed to accelerate the prediction. On this basis, we construct the on-orbit active adjustment model to compensate for the effect of thermally induced deformation on the surface accuracy. A mixed-variable optimization algorithm is further put forward to find the optimal strategy of dimensional adjustment. Finally, a case study with simulation analysis and experiment verification demonstrates the feasibility and superiority of the proposed surface adjustment method. (c) 2023 Elsevier Masson SAS. All rights reserved.

关键词： Surface accuracy Active adjustment Thermoelastic deformation mixed-variable optimization Spaceborne antenna

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Latent map Gaussian processes for mixed variable metamodeling

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 387卷 114128-114128页

作者： Oune, Nicholas Bostanabad, Ramin Univ Calif Irvine Mech & Aerosp Engn Irvine CA 92697 USA

Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. In this paper, we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation. Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Gaussian processes Emulation Metamodeling mixed-variable optimization Computer experiments Manifold learning

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