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 an...
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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.
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 NCCMO...
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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 constrainedmulti-objectiveoptimization 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-objectiveoptimization 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.
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...
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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 constrainedmulti-objectiveoptimization Evolutionary Algorithm) is proposed in this *** new algorithm combines the Pareto constrained-dominance,improved stochastic ranking algorithm and clone method in immune multi-objectiveoptimization *** 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.
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 p...
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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-objectiveconstrainedoptimization 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-objectiveoptimization 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.
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 mor...
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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 constrainedmulti-objectiveoptimization algorithm (CCMO) is an advanced technology, and this paper improves it and names it two-stage coevolutionary constrainedmulti-objectiveoptimization 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.
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 o...
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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 constrainedmulti-objectiveoptimization 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.
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 lea...
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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 constrainedmulti-objective Metaheuristics (CMOMs). To fill this gap, we develop a benchmark suite of Real-world constrainedmulti-objectiveoptimization 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.
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 mo...
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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%.
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 sc...
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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.
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. H...
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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.
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