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检索条件"主题词=Multi-objective optimization problem"

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optimization of Contract Distribution Based on multi-objective Estimation of Distribution Algorithm 2018

Optimization of Contract Distribution Based on Multi-objecti...

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International Conference on Computing and Artificial Intelligence (ICCAI)

作者： Hu, Laihong Yang, Xiaogang Fan, Hongdong Xian Res Inst Hi Tech Xian 710025 Shaanxi Peoples R China

ISBN: (纸本)9781450364195

Contract distribution is widely exists in modem commercial society, which mainly depends on qualitative analysis, and there still lack studies of quantitative analysis. Based on multi-objective estimation of distribution algorithm (MOEDA), quantitative research idea on contract distribution is explored in this article. First of all, multi-objective optimization model is built for contract distribution. Then, the algorithm flow base on MOEDA is designed. At last, simulations are carried out and compare with multi-objective genetic algorithm (MOGA). The simulation results show that the MOEDA performs better than MOGA, and verify the effectiveness and robustness of the proposed method in optimization of contract distribution.

关键词： Contract distribution multi-objective optimization problem Estimation of distribution algorithm Genetic algorithm

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Genetic algorithm-based multi-objective optimal absorber system for three-dimensional seismic structures

Genetic algorithm-based multi-objective optimal absorber sys...

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Conference on Active and Passive Smart Structures and Integrated Systems

作者： Ren, Wenjie Li, Hongnan Song, Gangbing Huo, Linsheng Hebei Univ Technol Sch Civil Engn Tianjin 300401 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Univ Houston Dept Mech Engn Houston TX 77204 USA

ISBN: (纸本)9780819475480

The problem of optimizing an absorber system for three-dimensional seismic structures is addressed. The objective is to determine the number and position of absorbers to minimize the coupling effects of translation-torsion of structures at minimum cost. A procedure for a multi-objective optimization problem is developed by integrating a dominance-based selection operator and a dominance-based penalty function method. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. The technique guarantees the better performing individual winning its competition, provides a slight selection pressure toward individuals and maintains diversity in the population. Moreover, due to the evaluation for individuals in each generation being finished in one run, less computational effort is taken. Penalty function methods are generally used to transform a constrained optimization problem into an unconstrained one. The dominance-based penalty function contains necessary information on non-dominated character and infeasible position of an individual, essential for success in seeking a Pareto optimal set. The proposed approach is used to obtain a set of non-dominated designs for a six-storey three-dimensional building with shape memory alloy dampers subjected to earthquake.

关键词： three-dimensional structure multi-objective optimization problem genetic algorithm dominance-based selection operator dominance-based penalty function method pareto-set filter stopping criterion shape memory alloy damper number and position of dampers passive control

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Study on Low-Pressure Casting Technique and Mold Design of the Aluminum Wheel 16

Study on Low-Pressure Casting Technique and Mold Design of t...

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16th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Jiaze Lu, Kaijie Zheng, Qingchun Ma, WenPeng Peng, Pai Chen, Cong Li, Jiehe Li, Peixin Zheng, Dawei Tianjin Univ Technol Tianjin Key Lab Adv Mechatron Syst Design & Intel Sch Mech Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ 391 Binshui West Rd Tianjin Peoples R China

ISBN: (纸本)9781728116976

It is necessary to optimize the die structure for the low-pressure casting of aluminum alloy wheel hub which is prone to shrinkage cavity and porosity. This paper simulated the mold filling and solidification process when the change of the temperature field and stress field, based on the solidification mold defect factors in central composite experiment and response surface analysis to optimize method, finally complete the multi-objective optimization using the algorithm of the NSGA-II reasonably come to the abrasive wall thickness values. In terms of experimental verification, the phase structure of aluminum alloy casting was observed to be uniform and smooth without shrinkage cavity and porosity, which realized the lightweight design of the die and improved the quality of the die casting.

关键词： Low-pressure casting Response surface methodology optimization design Center composite design multi-objective optimization problem

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Tasks Scheduling with Load Balancing in Fog Computing: a Bi-level multi-objective optimization Approach

Tasks Scheduling with Load Balancing in Fog Computing: a Bi-...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Kouka, Najwa Piuri, Vincenzo Samarati, Pierangela Univ Milan Dipartimento Informat Milan Italy

ISBN: (纸本)9798400704949

Fog computing is characterized by its proximity to edge devices, allowing it to handle data near the source. This capability alleviates the computational burden on data centers and minimizes latency. Ensuring high throughput and reliability of services in Fog environments depends on the critical roles of load balancing of resources and task scheduling. A significant challenge in task scheduling is allocating tasks to optimal nodes. In this paper, we tackle the challenge posed by the dependency between optimally scheduled tasks and the optimal nodes for task scheduling and propose a novel bi-level multi-objective task scheduling approach. At the upper level, which pertains to task scheduling optimization, the objective functions include the minimization of makespan, cost, and energy. At the lower level, corresponding to load balancing optimization, the objective functions include the minimization of response time and maximization of resource utilization. Our approach is based on an Improved multi-objective Ant Colony algorithm (IMOACO). Simulation experiments using iFogSim confirm the performance of our approach and its advantage over existing algorithms, including heuristic and meta-heuristic approaches.

关键词： Task scheduling Load-balancing fog computing multi-objective optimization problem and ant colony optimization

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An Evolutionary Algorithm with Variable-Length Chromosome for multi-objective Minimalistic Attack 2

An Evolutionary Algorithm with Variable-Length Chromosome fo...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Zhou, Chengyu Hou, Yaqing Ma, Wenqiang Yu, Hua Ge, Hongwei Dalian Univ Technol Dept Comp Sci Dalian Peoples R China

ISBN: (纸本)9798350354102;9798350354096

Evolutionary Algorithms (EAs) have acquired significant achievements in multi-objective optimization. Canonical EAs are mainly based on fixed-length chromosome. However, certain optimization problems require variable-length chromosome based EAs to solve. In this paper, our interest lies in solving the minimalistic attack problem which is formulated as a multi-objective optimization problem aiming to apply perturbations on the input (pictures) of well-trained deep reinforcement learning (DRL) policies. The objective is to mislead the DRL agent to alter its predictions. To achieve this, we propose a novel evolutionary algorithm with variable-length chromosome that dynamically adapts the chromosome length. Experiments show that the proposed algorithm converges more quickly and achieves better results than the baseline algorithm.

关键词： multi-objective optimization problem evolutionary algorithm variable length chromosome multi-objective minimalistic attack

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multi-objective Layout optimization of Aircraft multi-branch Cable Harness Based on MOPSO/D 12

Multi-objective Layout Optimization of Aircraft Multi-branch...

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12th International Conference on Mechanical and Aerospace Engineering (ICMAE)

作者： Zhang, Dan Liu, Zhaochao Zhou, Chen Zuo, Dunwen Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China

ISBN: (纸本)9781665433211

The routing of multi-branch cable harness in aircraft relies heavily on manual work and experience, which is highly time-consuming. hi order to realize the automatic 3D layout of multi-branch cable harness, this paper presents a novel approach by solving the layout as a multi-objective optimization problem that is NP-hard. The optimization objectives are to minimize the weight of multi-branch cables, to maximize the proportion of main roads, and to maximize the openness of the wiring path. Various regulatory and functional design rules are considered to be constraints. And the number and spatial position of branch points are taken as decision variables. As a result, a multi-objective layout optimization problem model of multi-branch cable based on Steiner tree is established. Then an improved multi-objective particle swarm optimization based on decomposition (MOPSO/D) is proposed to obtain the Pareto solution set of multi-branch cable layout. Finally, the feasibility of the proposed method is verified by the cable layout of the aircraft cabin.

关键词： cable harness layout Steiner minimum tree multi-objective optimization problem MOPSO/D

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optimization Methods to Improve Efficiency and Fairness in Peer-to-Peer Energy Trading 37

Optimization Methods to Improve Efficiency and Fairness in P...

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37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Kusatake, Eiichi Shinomiya, Norihiko Soka Univ Grad Sch Sci & Engn Hachioji Japan

ISBN: (数字)9781665485593

ISBN: (纸本)9781665485593

In recent years, many countries have been promoting the distributed energy systems for clean energy utilization. Direct energy trading is an efficient way to trade electricity, but not everyone benefits fairly in the market, since some suppliers have limited capacity of supplying electricity. This occasion leads to a decrease in the consumer's benefit. Existing studies are mainly focused on not the equity of consumer's benefit but the efficiency of resource allocation. This paper proposes a multi-objective optimization problem and single-objective one in order to improve both the equity of consumer's benefit and the efficiency of resource allocation. For solving the problem, our study adopts six Evolutionary Algorithms (EAs) and two single-objective algorithms. Simulation results demonstrate that the selected EAs can optimize both the efficiency of resource allocation and the equity of consumer's benefit.

关键词： Peer-to-peer Evolutionary algorithm multi-objective optimization problem Graph theory

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A computational intelligence approach to system-of-systems architecting incorporating multi-objective optimization 1

A computational intelligence approach to system-of-systems a...

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Conference on Systems Engineering Research

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： System-of-Systems SoS Architecting Genetic Algorithm GA multi-objective optimization problem MOP multi-objective Evolutionary Algorithm MOEA Meta-Architecture

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Solving multi-Stage Weapon Target Assignment problems by C-TAEA 39

Solving Multi-Stage Weapon Target Assignment Problems by C-T...

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39th Chinese Control Conference (CCC)

作者： Zou, Shiqi Shi, Xiaoping Guo, Rui Lin, Xiaohan Harbin Inst Technol Control & Simulat Ctr Harbin 150080 Peoples R China

ISBN: (纸本)9789881563903

multi-stage weapon target assignment (MSWTA) problem is usually treated as a classical constrained multi-objective optimization problem, which tends to balance the attack efficiency and economic loss for weapons after adding engagement resource constraints. In the context of multi-stage confrontations, the MSWTA becomes more complex and challenging to solve. To handle this constrained problem, this paper develops the advantageous two-archive evolutionary algorithm (C-TAEA) to combine the density estimation mechanism with a fast non-dominated sorting method to obtain solutions to this problem efficiently, while maintaining the convergence-oriented archive (CA) to speed up the generation of solutions and the diversity-oriented archive (DA) to enhance the diversity of populations. Extensive experiments are conducted to compare the proposed C-TAEA with the traditional NSGA-II according to the average of IGD and HV values. The numerical simulation results demonstrate that our method intuitively shows the performance improvement after adding the feasibility and can find the approximate Pareto Front more effectively.

关键词： Weapon Target Assignment (WTA) multi-objective optimization problem Two-Archive C-TAEA NSGA-II Density Estimation

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A Surrogate-assisted Memetic Algorithm for Interval multi-objective optimization

A Surrogate-assisted Memetic Algorithm for Interval Multi-ob...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Sun, Jing Miao, Zhuang Gong, Dunwei Huaihai Inst Technol Sch Sci Lianyungang Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9781538627266

Interval multi-objective optimization problems (IMOPs) are ubiquitous and challenging. There are many optimizers for solving them;however, their drawbacks, such as the high computational cost and big uncertainty of the final front, hinder their applications in real-world situation. This paper proposes a surrogate-assisted interval multi-objective memetic algorithm (SA-IMOMA) that incorporates a surrogate model into the local search. In the framework of interval multi-objective memetic algorithms (IMOMAs), the fitness function of a local search is first defined by both the contribution of an individual to hyper-volume and the imprecision of the individual, and then a support vector machine (SVM) is trained and employed to evaluate local individuals so as to cut down the high computational cost of IMOMAs and further reduce the imprecision of the final front. The proposed algorithm was tested on 10 benchmark IMOPs and an IMOP in solar desalination. The empirical results indicate that SA-IMOMA is more economical than non-surrogate IMOMAs and superior to non-local-search IP-MOEA.

关键词： multi-objective optimization problem interval memetic algorithm Surrogate model

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