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Hypersonic lifting body aerodynamic shape optimization based on the multiobjective evolutionary algorithm based on decomposition

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2015年第7期229卷 1246-1266页

作者： Ma, Yang Yang, Tao Feng, Zhiwei Zhang, Qingbin Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

In this work, a volume and longitudinal stability-constrained multiobjective aerodynamic shape optimization is conducted. The aerodynamic shapes of lifting bodies are parameterized by using class function/shape function transformation parameterization method for maximum design flexibility. Hypersonic aerodynamic objectives and constraints are analyzed by solving the Reynolds-averaged Navier-Stokes equations in conjunction with a two-equation turbulence model. The Kriging technique is adopted to construct surrogate models aiming at reducing the computational cost. A two-stage method of infill sampling combined with multiobjective optimization is proposed to improve the performance of the surrogate models. multiobjective evolutionary algorithm based on decomposition (MOEA/D) combined with penalty function method is employed to handle the multiobjective optimization problem with nonlinear constraints. The optimization results reveal that the two-stage method based on surrogates can reduce the computational cost significantly, and the accuracy of the surrogates around the Pareto front is sufficient. The two objectives are competing such that a set of Pareto optimal solutions are obtained, which are the best tradeoffs among the objectives. The unconstrained multiobjective optimization problem is also investigated with MOEA/D and nondominated sorting genetic algorithm II (NSGA-II) respectively to make a further comparison. The results show that the Pareto sets based on MOEA/D are more excellent and distribute more evenly than that obtained by NSGA-II. The computational efficiency of MOEA/D is about six times faster than that of NSGA-II. Lastly, aerodynamic characters of typical shape of Pareto front are analyzed under different flight conditions, and the results reveal favorable robustness of this shape.

关键词： Hypersonic aerodynamic shape optimization computational fluid dynamics multiobjective evolutionary algorithm based on decomposition Kriging surrogate model expensive optimization

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Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid-liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2019年第5期191卷 287页

作者： Farajvand, Mohammad Kiarostami, Vahid Davallo, Mehran Ghaedi, Abdolmohammad Islamic Azad Univ North Tehran Branch Dept Chem Tehran Iran Islamic Azad Univ Gachsaran Branch Dept Chem Gachsaran Iran

Solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a simple, fast, and low-cost technique was developed for simultaneous extraction of Cd2+ and Cu2+ ions in aqueous solutions. multiobjective evolutionary algorithm based on decomposition with the aid of artificial neural networks (ANN-MOEA/D) was used for the first time in chemistry, environment, and food sciences to optimize several independent variables affecting the extraction efficiency, including disperser volume and extraction solvent volume, pH, and salt addition. To perform the ST-DLLME operations, xylene, methanol, and dithizone were utilized as an extraction solvent, disperser solvent, and chelating agent, respectively. Non-dominated sorting genetic algorithm versions II and III (NSGA II and NSGA III) as multiobjective metaheuristic algorithms and in addition central composite design (CCD) were studied as comparable optimization methods. A comparison of results from these techniques revealed that ANN-MOEA/D model was the best optimization technique owing to its highest efficiency (97.6% for Cd2+ and 98.3% for Cu2+). Under optimal conditions obtained by ANN-MOEAD, the detection limit (S/N=3), the quantitation limit(S/N=10), and the linear range for Cu2+ were 0.05, 0.15, and 0.15-1000gL(-1), respectively, and for Cd2+ were 0.07, 0.21, and 0.21-750gL(-1), respectively. The real sample recoveries at a spiking level of 0.05, 0.1, and 0.3mgL(-1) of Cu2+ and Cd2+ ions under the optimal conditions obtained by ANN-MOEA/D ranged from 94.8 to 105%.

关键词： Solvent-terminated dispersive liquid-liquid microextraction Cadmium ions Copper ions multiobjective evolutionary algorithm based on decomposition Artificial neural networks

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A novel decomposition-based multiobjective evolutionary algorithm using improved multiple adaptive dynamic selection strategies

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INFORMATION SCIENCES 2021年 556卷 472-494页

作者： Xie, Yingbo Qiao, Junfei Wang, Ding Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116620 Peoples R China

In the last decade, the decomposition-based multiobjective optimization evolutionary algorithm (MOEA/D) has displayed promising performance when dealing with multiobjective optimization problems (MOPs). However, for some complex MOPs, the conventional MOEA/D often leads to the loss of population diversity in the iterative process, and the convergence performance of the population is weakened in the mean time. In this paper, a novel MOEA/D, based on improved multiple adaptive dynamic selection strategies and elite archive strategy (MOEA/D-IMA), is proposed to improve the population diversity and convergence. First, a novel differential evolution (DE) operator is constructed, which constitutes an operator pool with other DE operators. According to the search information of the current population, an adaptive dynamic selection strategy is proposed, which is used by MOEA/D-IMA to select a suitable DE operator to replace the simulated binary crossover (SBX) operator. Second, a parameter adaptive dynamic selection strategy is proposed to enhance the robustness of MOEA/D-IMA by using the information of population evolution state. Third, an elite archive strategy is introduced to improve the convergence and diversity of the population where mutual dominance of individuals and their aggregation distance is employed. Finally, the proposed MOEA/D-IMA is compared with several state-ofthe-art algorithms on three suits of 18 test problems. Experimental results indicate that the proposed MOEA/D-IMA can significantly improve the optimization performance when coping with MOPs. (C) 2020 Elsevier Inc. All rights reserved.

关键词： multiobjective optimization multiobjective evolutionary algorithm based on decomposition Adaptive dynamic selection Differential evolution Elite archive

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Design of optimal blind watermarking technique based on MOEA/D

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IET IMAGE PROCESSING 2020年第16期14卷 4368-4375页

作者： Mun, Kwanghyok Son, Cholmin Kim Chaek Univ Technol Fac Appl Math Pyongyang North Korea

In image watermarking, the quality of the watermarked image and the watermark's robustness are the main indicators for evaluating the performance of the watermarking scheme, but they contradict each other, making it difficult to design a rational watermarking method. In this study, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is employed to solve the multi-objective optimisation problem to ensure the balance between contradictory objectives. The watermark is embedded by selecting the regions with the lowest human visual characteristics in the host image and modifying the frequency coefficients of the discrete cosine transform. At this time, the determination of the positions of embedding coefficients and parameters related to the modifications results in the multi-objective optimisation problem on the robustness and imperceptibility, and the optimal solution is determined by finding the approximate Pareto set. Simulation results for various attacks on watermarked images demonstrate the effectiveness of the proposed watermarking technique.

关键词： image watermarking image coding Pareto optimisation discrete cosine transforms evolutionary computation performance evaluation optimal blind watermarking technique image watermarking watermarked image quality rational watermarking method multiobjective evolutionary algorithm based on decomposition multiobjective optimisation problem human visual characteristics host image MOEA D performance evaluation frequency coefficients discrete cosine transform approximate Pareto set

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Diversity Improvement in decomposition-based Multi-Objective evolutionary algorithm for Many-Objective Optimization Problems

Diversity Improvement in Decomposition-Based Multi-Objective...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Zhenan He Gary G. Yen School of Electrical and Computer Engineering Oklahoma State University

ISBN: (纸本)9781479938414

decomposition-based Multi-Objective evolutionary algorithms (DBMOEA), such as Multiple Single Objective Pareto Sampling (MSOPS) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), have been successfully applied in finding Pareto-optimal fronts in multiobjective Optimization Problems (MOPs), two or three-objective in general. DBMOEA decomposes one MOP into multiple Single-objective Optimization Problems (SOPs) where the convergence of approximated front is facilitated by finding the optimal solution of each SOP and its diversity is preserved by a group of well distributed SOPs. However, when solving problems with many objectives, one single solution can be the optimal solution of multiple SOPs which inadvertently leads to a severe loss of population diversity. In this paper, we propose a new diversity improvement method incorporated into a modified DBMOEA to directly handle this challenge. The design includes two steps. First, a few number of weight vectors guide the whole population towards a small number of solutions nearby the true Pareto front. Afterwards, initialize a subpopulation around each solution and diversify them toward well distribution. As a case study, a new algorithm based on this design is compared with three state-of-the-art DBMOEAs, MOEA/D, MSOPS, and MO-NSGA-II. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the chosen competitors for solving many-objective optimization problems.

关键词： Pareto optimisation evolutionary computation sampling methods DBMOEA MO-NSGA-II algorithm MOEA/D algorithm MSOPS algorithm Pareto-optimal front decomposition-based multiobjective evolutionary algorithm diversity improvement method many-objective optimization problems multiobjective evolutionary algorithm based on decomposition multiple single objective Pareto sampling algorithm single-objective optimization problem Diversity methods evolutionary computation Measurement Optimization Sociology Statistics Vectors

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Biparty multiobjective optimal power flow: The problem definition and an evolutionary approach

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APPLIED SOFT COMPUTING 2023年 146卷

作者： Chang, Yatong Luo, Wenjian Lin, Xin Song, Zhen Coello, Carlos A. Coello Harbin Inst Technol Sch Comp Sci & Technol Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518055 Guangdong Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Jiangsu Peoples R China CINVESTAV IPN Dept Toxicol Mexico City DF Mexico Tecnol Monterrey Sch Engn & Sci Monterrey Nuevo Lean Mexico

The multiobjective optimal power flow (MOOPF) problem consists of adjusting the generator power and the voltage state of each node within the feasible range in the process of power transmission and, finally of achieving the objectives of optimizing the cost, loss and stability, etc. In the MOOPF, two key decision makers are usually involved, which are the power generation sector and the transmission sector. Thus, it is more suitable to model an MOOPF as a biparty multiobjective optimal power flow (BPMOOPF) problem. However, so far, there is no work on treating and solving the MOOPF problem from the perspective of biparty multiobjective optimization. In this paper, we propose the definition of the BPMOOPF problem as well as a novel evolutionary biparty multiobjective optimization algorithm for solving the BPMOOPF problem, which we call BPMOOPF-EA. Our experimental results show that, compared two state-of-the-art algorithms (C-MOEA/D and A-NSGA-III), our proposed BPMOOPF-EA has a better performance when solving the BPMOOPF problem. (c) 2023 Elsevier B.V. All rights reserved.

关键词： multiobjective optimization Multiparty multiobjective optimization multiobjective evolutionary algorithm based on decomposition Optimal power flow

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Modelling of preparation parameters of polymer and oily waste sludge modified bitumen using neural network coupled with multiobjective evolutionary algorithm

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2024年第1期25卷

作者： Iravanchi, Ashkan Kiarostami, Vahid Hojjati, Mohammad Manafi, Mohammadreza Davallo, Mehran Islamic Azad Univ Fac Sci Chem Dept South Tehran Branch Tehran Iran Islamic Azad Univ Chem Fac North Tehran Branch Tehran Iran

In this study, artificial neural networks coupled multi-objective evolutionary algorithm based on decomposition (ANN-MOEA/D) and non-dominated sorting genetic algorithm versionIII (ANN-NSGAIII) as the chemometric approaches were used for the first time in the pavement field to model the modification conditions of the base bitumen (PG58-22). The novel ternary system of styrene-butadiene-styrene and two inexpensive waste materials as independent factors and five responses were considered in this work. Under the ANN-MOEA/D conditions, as the better optimisation method, the amounts of polyethylene, styrene-butadiene-styrene, and oily waste sludge were 3.56%, 4%, and 4.34%, respectively. The response values of penetration and change of mass after short-term ageing were at their minimum values of 50 dmm and 0.06%, respectively. The response values of softening point, ductility at 10oC, and retained penetration after short-term ageing, were at their maximum values of 65oC, 30 cm, and 89%, respectively. The analysis of the results of BBR and DSR tests in the optimal conditions obtained from two metaheuristic algorithms indicated improvement in the rutting resistance (G*/sin delta), m-value, and stiffness parameters of the modified bitumen compared to the base bitumen. The optimal conditions of ANN-MOEA/D and ANN-NSGAIII of the modified bitumen reached the characteristics of PG70-28 and PG-64-22 bitumen, respectively.

关键词： Bitumen polyethylene styrene-butadiene-styrene oily waste sludge multiobjective evolutionary algorithm based on decomposition artificial neural network

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