Evolutionary Large-Scale Multiobjective Optimization: Benchmarks and Algorithms

With the rise of network strategies, various businesses using the Internet as a platform have been vigorously developed, among which the scale of e-commerce transactions has increased on a large scale. In order to deeply explore the role and advantages of data fusion and multiobjective optimization technology in consumer online reviews, this paper uses the new and old evaluation model comparison method, algorithm design method, and multiobject research method to collect samples, analyze the technical model, and streamline the algorithm. And it will create an analysis algorithm model that can improve and optimize the consumer’s current online reviews. First, we choose the electricity supplier on the platform of a total of four mobile phones grabbed 32,145 comments. Based on this research on the number of online comment fields of consumers, the results show that 78% of the comments are less than 55 words, indicating that most of the online comments left by consumers are short comments; at the same time, a small number of consumers have left detailed comments. Description, the longest of is reached 612 words. On this basis, further study the efficiency and function analysis of the algorithm proposed in this paper, and we can see that DCDG-MOMA is used in 14-7 and 28-7 use cases as 1 and 2, respectively, which is the least, and at 40-7 and 50-7, the time used is 15 and 20 which is close to PBI, but it is also much less time than the MOMAD algorithm. This further shows that the algorithm really plays an effective role in the actual decision-making process. It has basically realized a more efficient algorithm for consumer online reviews under the background of applying data fusion and multiobjective optimization technology.

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Evolutionary Multiobjective Optimization with Endmember Priori Strategy for Large-Scale Hyperspectral Sparse Unmixing

Electronics ◽

10.3390/electronics10172079 ◽

2021 ◽

Vol 10 (17) ◽

pp. 2079

Author(s):

Zhao Wang ◽

Jinxin Wei ◽

Jianzhao Li ◽

Peng Li ◽

Fei Xie

Keyword(s):

Multiobjective Optimization ◽

Large Scale ◽

High Efficiency ◽

Hyperspectral Image ◽

Simulated Data ◽

Reconstruction Error ◽

Initial Population ◽

Spectral Library ◽

Nondominated Solutions ◽

Mixed Pixels

Mixed pixels inevitably appear in the hyperspectral image due to the low resolution of the sensor and the mixing of ground objects. Sparse unmixing, as an emerging method to solve the problem of mixed pixels, has received extensive attention in recent years due to its robustness and high efficiency. In theory, sparse unmixing is essentially a multiobjective optimization problem. The sparse endmember term and the reconstruction error term can be regarded as two objectives to optimize simultaneously, and a series of nondominated solutions can be obtained as the final solution. However, the large-scale spectral library poses a challenge due to the high-dimensional number of spectra, it is difficult to accurately extract a few active endmembers and estimate their corresponding abundance from hundreds of spectral features. In order to solve this problem, we propose an evolutionary multiobjective hyperspectral sparse unmixing algorithm with endmember priori strategy (EMSU-EP) to solve the large-scale sparse unmixing problem. The single endmember in the spectral library is used to reconstruct the hyperspectral image, respectively, and the corresponding score of each endmember can be obtained. Then the endmember scores are used as a prior knowledge to guide the generation of the initial population and the new offspring. Finally, a series of nondominated solutions are obtained by the nondominated sorting and the crowding distances calculation. Experiments on two benchmark large-scale simulated data to demonstrate the effectiveness of the proposed algorithm.

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Multiobjective Multivariate Optimization of Joint Spectrum Sensing and Power Control in Cognitive Wireless Networks

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2016040102 ◽

2016 ◽

Vol 10 (2) ◽

pp. 26-51 ◽

Cited By ~ 1

Author(s):

Hieu Van Dang ◽

Witold Kinsner

Keyword(s):

Multiobjective Optimization ◽

Power Control ◽

Spectrum Sensing ◽

Large Scale ◽

Optimization Problem ◽

Decision Threshold ◽

Multiobjective Optimization Problem ◽

Barrier Method ◽

Joint Spectrum ◽

Multiple User

In this paper, the authors investigate trade-off factors in designing efficient spectrum sensing and optimal power control techniques for a multichannel, multiple-user cognitive wireless network. They introduce the problem of joint spectrum sensing and power control as a maximization of the network throughput and a minimization of the interference to the network. These two optimization objectives can be achieved by a joint determination of sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. The authors propose an analytical approach based on Newton's methods and nonlinear barrier method to solve this large-scale joint multiobjective optimization problem.

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Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation

IEEE Transactions on Evolutionary Computation ◽

10.1109/tevc.2019.2896002 ◽

2019 ◽

Vol 23 (6) ◽

pp. 949-961 ◽

Cited By ~ 11

Author(s):

Cheng He ◽

Lianghao Li ◽

Ye Tian ◽

Xingyi Zhang ◽

Ran Cheng ◽

...

Keyword(s):

Multiobjective Optimization ◽

Large Scale ◽

Problem Reformulation

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