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作者： He Yan Cheng JiaLin MAO Sen Zhang WeiJing AIRCRAFT INDUSTRIAL (GROUP) CO.LTD. AVIC CHENG'DU

In order to explore the optimization of composite wing structure,this paper reviews the past research works of optimization of composite wing structures worldwide and in *** concludes that most optimization methods and calculations are useful but only applied with specified theoretic *** relevant composite optimization theories and strength constraints with composite lamination failure criteria,stability,aeroelasticity are sorts *** algorithm and applied solution rounding strategy are also *** on weight-reduction analysis on the thickness,sequence,and angle of the composite stacking under multiple constraints,a practical model of a composite wing structure example with a large aspect ratio is completed,and the calculation is based on the finite element analysis tool Nastran and the genetic algorithm Matlab *** optimal solutions are given with detailed differences and the comparison of the composite structure parameters before and after calculation is also *** application example indicates the feasibility of the optimization method and quadratic rounding process,the detail structure performance improvements with skin lamination change under the calculation are illustrated at the same time.

关键词： Composite stacking Wing box structure Genetic algorithm multi-objective constrained optimization

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RESEARCH ON A multi-objective constrained optimization EVOLUTIONARY ALGORITHM 4

RESEARCH ON A MULTI-OBJECTIVE CONSTRAINED OPTIMIZATION EVOLU...

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4th IEEE International Conference on Cloud Computing and Intelligence Systems (IEEE CCIS)

作者： Xiu, Jiapeng He, Qun Yang, Zhengqiu Liu, Chen Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781509012565

To overcome the defects of partial multi-objective constrained optimization evolutionary algorithms especially in getting local optimal solutions, poor diversity and robustness, a hybrid algorithm which is named NCCMOEA (Non-dominated Clonal constrained multi-objective optimization Evolutionary Algorithm) is proposed in this paper. This new algorithm combines the Pareto constrained-dominance, improved stochastic ranking algorithm and clone method in immune multi-objective optimization algorithm. Experiments show that compared with the other effective algorithms, this algorithm NCCMOEA is more excellent in diversity and robustness and avoid getting local optimal solutions obviously.

关键词： multi-objective constrained optimization multi-objective constrained optimization Evolutionary Algorithm NCCMOEA

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RESEARCH ON A multi-objective constrained optimization EVOLUTIONARY ALGORITHM

RESEARCH ON A MULTI-OBJECTIVE CONSTRAINED OPTIMIZATION EVOLU...

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2016 4th IEEE International Conference on Cloud Computing and Intelligence Systems（IEEE CCIS2016）

作者： Jiapeng Xiu Qun He Zhengqiu Yang Chen Liu Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012572

To overcome the defects of partial multi-objective constrained optimization evolutionary algorithms especially in getting local optimal solutions,poor diversity and robustness,a hybrid algorithm which is named NCCMOEA(Non-dominated Clonal constrained multi-objective optimization Evolutionary Algorithm) is proposed in this *** new algorithm combines the Pareto constrained-dominance,improved stochastic ranking algorithm and clone method in immune multi-objective optimization *** show that compared with the other effective algorithms,this algorithm NCCMOEA is more excellent in diversity and robustness and avoid getting local optimal solutions obviously.

关键词： multi-objective constrained optimization multi-objective constrained optimization Evolutionary Algorithm NCCMOEA

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New Dynamic multi-objective constrained optimization Evolutionary Algorithm

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2015年第5期32卷 1550036-1550036页

作者： Liu, Chun-An Wang, Yuping Ren, Aihong Baoji Univ Arts & Sci Dept Math Baoji 721013 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

For dynamic multi-objective constrained optimization problem (DMCOP), it is important to find a sufficient number of uniformly distributed and representative dynamic Pareto optimal solutions. In this paper, the time period of the DMCOP is first divided into several random subperiods. In each random subperiod, the DMCOP is approximately regarded as a static optimization problem by taking the time subperiod fixed. Then, in order to decrease the amount of computation and improve the effectiveness of the algorithm, the dynamic multi-objective constrained optimization problem is further transformed into a dynamic bi-objective constrained optimization problem based on the dynamic mean rank variance and dynamic mean density variance of the evolution population. The evolution operators and a self-check operator which can automatically checkout the change of time parameter are introduced to solve the optimization problem efficiently. And finally, a dynamic multi-objective constrained optimization evolutionary algorithm is proposed. Also, the convergence analysis for the proposed algorithm is given. The computer simulations are made on four dynamic multi-objective optimization test functions and the results demonstrate that the proposed algorithm can effectively track and find the varying Pareto optimal solutions or the varying Pareto fronts with the change of time.

关键词： Dynamic optimization multi-objective constrained optimization Pareto optimal solution Pareto front time parameter

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Two-stage coevolutionary constrained multi-objective optimization algorithm for solving optimal power flow problems with wind power and FACTS devices

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RENEWABLE ENERGY 2024年 232卷

作者： Zhu, Jun-Hua Wang, Jie-Sheng Zheng, Yue Zhang, Xing-Yue Liu, Xun Wang, Xiao-Tian Zhang, Song-Bo Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

As a large amount of wind energy is integrated into the grid, the randomness it brings poses a challenge to modern power systems. The application of Flexible AC Transmission Systems (FACTS) in the grid is becoming more and more common, and it is necessary to consider how to choose suitable equipment in the appropriate locations. In this paper, a multi-objective optimal power flow (MOOPF) model with wind farms and FACTS devices is established. The Weibull probability density function is used to establish the wind speed model, and the cost problem brought by wind power is considered. The locations and ratings of thyristor-controlled series compensators, thyristor-controlled phase shifters, and static VAR compensators are added to the system as control variables. In addition, the constraints on the prohibited operating areas of thermal power generators and the valve point effect are also considered. Coevolutionary constrained multi-objective optimization algorithm (CCMO) is an advanced technology, and this paper improves it and names it two-stage coevolutionary constrained multi-objective optimization algorithm (TSCCMO). The proposed algorithm uses the constraint violation value as an additional objective function in the sub-population environmental selection process, and integrates a neighborhood selection strategy into the mating selection process. The population evolution process is divided into two stages, in the first stage the two populations cooperate weakly, and in the second stage the two populations will have strong cooperation. TSCCMO is used to solve this complex constrained MOOPF problem, and its results are compared and analyzed with CCMO, NSGA-II-CDP, C3M, and PPS. The comprehensive performance of TSCCMO is the best among the 6 cases.

关键词： Optimal power flow Wind energy FACTS multi-objective constrained optimization Coevolution

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Relative assessment of constrained multiple objective optimization techniques for optimal pumping strategy design to mitigate saline water intrusion in coastal groundwater systems

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DESALINATION 2025年 606卷

作者： Roy, Dilip Kumar Datta, Bithin Bangladesh Agr Res Inst Irrigat & Water Management Div Gazipur 1701 Bangladesh James Cook Univ Coll Sci & Engn 1 James Cook Dr Douglas Qld 4814 Australia

Saltwater intrusion (SWI) degrades water quality in coastal groundwater systems. Over-abstraction from these aquifers accelerates SWI, necessitating practical measures to prevent it and enable sustainable extraction of groundwater resources. Simulation-optimization (S/O) methods have been widely adopted for developing optimized groundwater extraction strategies, with optimization algorithms playing a critical role. This study compared several constrained multi-objective optimization algorithms (CMOOAs) for designing pumping schemes to mitigate SWI in coastal aquifers. The evaluated algorithms included the Controlled Elitist multi- objective Genetic Algorithm (CEMOGA), multi-objective Feasibility Enhanced Particle Swarm optimization (MOFEPSO), multi-objective Lichtenberg Algorithm (MOLA), and multi-objective Pareto Search (MOPS). A biobjective SWI management model was developed using an S/O approach, with the FEMWATER code replaced by a surrogate model based on multivariate Adaptive Regression Spline (MARS) to improve computational efficiency. The MARS model effectively captured aquifer processes, demonstrating strong correlation metrics (R = 0.986-0.999) and low error indicators (NRMSE = 0.001-0.019, MAE = 0.522-6.291 mg/l). This surrogate model was integrated with each CMOOA to design optimal pumping strategies that maximize beneficial abstraction while minimizing SWI. multiple independent MARS-assisted optimization runs were conducted for a robust comparison of solution accuracy and convergence time. Results showed that MARS-CEMOGA outperformed other algorithms, delivering the highest solution quality and computational efficiency, requiring just 323.86 s compared to 388.12 s for MOPS, 999.18 s for MOFEPSO, and 2316.55 s for MOLA. The study demonstrates that MARS-CEMOGA is a highly efficient tool for developing SWI management models in coastal aquifers, although other CMOOAs also produced satisfactory results.

关键词： Coastal aquifer Saltwater intrusion Coupled simulation-optimization Surrogate model multi-objective constrained optimization

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A Benchmark-Suite of real-World constrained multi-objective optimization problems and some baseline results

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 67卷 100961-100961页

作者： Kumar, Abhishek Wu, Guohua Ali, Mostafa Z. Luo, Qizhang Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam Das, Swagatam Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Jordan Univ Sci & Technol Sch Comp Informat Syst Irbid 22110 Jordan Kyungpook Natl Univ Sch Elect Engn Dept Artificial Intelligence Daegu 41566 South Korea Nanyang Technol Univ Sch Elect Elect Engn Singapore 639798 Singapore Indian Stat Inst Elect & Commun Sci Unit Kolkata India

Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the performance of metaheuristics. However, these SBPs may include various unrealistic properties. As a consequence, performance assessment may lead to underestimation or overestimation. To address this issue, few benchmark suites containing real-world problems have been proposed for all kinds of metaheuristics except for constrained multi-objective Metaheuristics (CMOMs). To fill this gap, we develop a benchmark suite of Real-world constrained multi-objective optimization Problems (RWCMOPs) for performance assessment of CMOMs. This benchmark suite includes 50 problems collected from various streams of research. We also present the baseline results of this benchmark suite by using state-of-the-art algorithms. Besides, for comparative analysis, a ranking scheme is also proposed.

关键词： Metaheuristics Performance assessment Real-World problems multi-objective constrained optimization Benchmark-Suite Ranking scheme

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Vibration Suppression of multi-Stage-Blade AMB-Rotor Using Parallel Adaptive and Cascaded multi-Frequency Notch Filters

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APPLIED SCIENCES-BASEL 2024年第14期14卷 6255页

作者： Zhang, Min Tang, Jiqiang Zhou, Jinxiang Han, Xue Wang, Kun Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Natl Inst Extremely Weak Magnet Field Infrastruct Hangzhou 310051 Peoples R China

The application of active magnetic bearings (AMBs) in high-speed rotating machinery faces the challenge of micro-vibration. This research addresses the vibration control of a high-speed magnetically suspended turbo molecular pump (MSTMP) with rotor mass imbalance vibration and multi-stage-blade modal vibration. A novel integrated AMB controller consisting of parallel co-frequency adaptive notch filter (ANF) and cascaded multi-frequency improved double-T notch filters (DTNFs) is proposed. To suppress rotor mass imbalance vibration, a bandwidth factor rectification method of the ANF based on displacement stiffness perturbation is designed. To suppress multi-stage-blade modal vibration, a multi-objective constrained optimization method of cascaded improved DTNFs based on linear normalization is designed. Simulation and experimental results validate that the proposed structure improvement of the addition of an AMB controller and multi-parameter optimization of the algorithm can effectively improve not only the phase stability margin and the notch vibration performance of the magnetically suspended rotor (MSR) system but also the efficiency and practicability of the algorithm. At rotational speeds of 12,000 rpm, 15,000 rpm, 18,000 rpm, and 21,000 rpm, the suppression of co-frequency synchronous vibration is approximately maintained between -30.94 dB and -30.56 dB. At the rated speed of 24,000 rpm, compared with other algorithms, the value of the rotor displacement converges from 0.08 mm to 0.03 mm, a reduction of 62.50%. The convergence time decreases from 3.67 s to 2.85 s, a reduction of 22.34%.

关键词： active magnetic bearing magnetically suspended turbo molecular pump rotor mass imbalance vibration multi-stage-blade modes notch filter multi-objective constrained optimization

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multi-objective optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds

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IEEE ACCESS 2021年 9卷 89891-89905页

作者： Calzarossa, Maria Carla Della Vedova, Marco L. Massari, Luisa Nebbione, Giuseppe Tessera, Daniele Univ Pavia Dept Elect Comp & Biomed Engn I-27100 Pavia Italy Univ Cattolica Sacro Cuore Dept Math & Phys I-25121 Brescia Italy

Cloud technologies are being used nowadays to cope with the increased computing and storage requirements of services and applications. Nevertheless, decisions about resources to be provisioned and the corresponding scheduling plans are far from being easily made especially because of the variability and uncertainty affecting workload demands as well as technological infrastructure performance. In this paper we address these issues by formulating a multi-objective constrained optimization problem aimed at identifying the optimal scheduling plans for scientific workflows to be deployed in uncertain cloud environments. In particular, we focus on minimizing the expected workflow execution time and monetary cost under probabilistic constraints on deadline and budget. According to the proposed approach, this problem is solved offline, that is, prior to workflow execution, with the intention of allowing cloud users to choose the plan of the Pareto optimal set satisfying their requirements and preferences. The analysis of the combined effects of cloud uncertainty and probabilistic constraints has shown that the solutions of the optimization problem are strongly affected by uncertainty. Hence, to properly provision cloud resources, it is compelling to precisely quantify uncertainty and take explicitly into account its effects in the decision process.

关键词： Uncertainty Task analysis Cloud computing Probabilistic logic Scheduling Processor scheduling Optimal scheduling Cloud computing uncertainty multi-objective constrained optimization Genetic Algorithm Monte Carlo method scientific workflows

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Quantum alternating operator ansatz for solving the minimum exact cover problem

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2023年 626卷

作者： Wang, Sha-Sha Liu, Hai-Ling Song, Yan-Qi Gao, Fei Qin, Su-Juan Wen, Qiao-Yan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The Quantum Alternating Operator Ansatz (QAOA+) is an extension of the Quantum Approximate optimization Algorithm (QAOA), where the search space is smaller in solving constrained combinatorial optimization problems. However, QAOA+ requires a trivial feasible solution as the initial state, so it cannot be applied directly for problems that are difficult to find a trivial feasible solution. For simplicity, we call them as Non-Trivial-Feasible-Solution Problems (NTFSP). In this paper, we take the Minimum Exact Cover (MEC) problem as an example, studying how to apply QAOA+ to NTFSP. As we know, Exact Cover (EC) is the feasible space of MEC problem, which has no trivial solutions. To overcome the above problem, the EC problem is divided into two steps to solve. First, disjoint sets are obtained, which is equivalent to solving independent sets. Second, on this basis, the sets covering all elements (i.e., EC) are solved. In other words, we transform MEC into a multi-objective constrained optimization problem, where feasible space consists of independent sets that are easy to find. Finally, we also verify the feasibility of the algorithm from numerical experiments. Furthermore, we compare QAOA+ with QAOA, and the results demonstrated that QAOA+ has a higher probability of finding a solution with the same rounds of both algorithms. Our method provides a feasible way for applying QAOA+ to NTFSP, and is expected to expand its application significantly.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Quantum alternating operator ansatz Minimum exact cover Trivial feasible solutions multi-objective constrained optimization problem

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