XGRouter: high-quality global router in X-architecture with particle swarm optimization

2015 ◽  
Vol 9 (4) ◽  
pp. 576-594 ◽  
Author(s):  
Genggeng Liu ◽  
Wenzhong Guo ◽  
Rongrong Li ◽  
Yuzhen Niu ◽  
Guolong Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
High Quality ◽  
Swarm Optimization ◽  
X Architecture

Particle swarm optimization as alternative tool to sensory evaluation to produce high-quality low-sodium fish sauce via electrodialysis

2018 ◽  
Vol 228 ◽  
pp. 84-90
Author(s):  
San Ratanasanya ◽  
Nathamol Chindapan ◽  
Jumpol Polvichai ◽  
Booncharoen Sirinaovakul ◽  
Sakamon Devahastin
Keyword(s):  
Particle Swarm Optimization ◽  
Sensory Evaluation ◽  
Particle Swarm ◽  
Fish Sauce ◽  
High Quality ◽  
Swarm Optimization ◽  
Low Sodium

scholarly journals PSOFuzzer: A Target-Oriented Software Vulnerability Detection Technology Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (3) ◽  
pp. 1095
Author(s):  
Chen Chen ◽  
Han Xu ◽  
Baojiang Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Target Point ◽  
Vulnerability Detection ◽  
High Quality ◽  
Swarm Optimization ◽  
Detection Technology ◽  
Test Process ◽  
Quality Features ◽  
Historical Samples

Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing efficiency. However, the sample generation algorithm used in target-oriented vulnerability detection technology has some problems, such as weak guidance, weak sample penetration, and difficult sample generation. This paper proposes a new target-oriented fuzzer, PSOFuzzer, that uses particle swarm optimization to generate samples. PSOFuzzer can quickly learn high-quality features in historical samples and implant them into new samples that can be led to execute the suspected vulnerable point. The experimental results show that PSOFuzzer can generate more samples in the test process to reach the target point and can trigger vulnerabilities with 79% and 423% higher probability than AFLGo and Sidewinder, respectively, on tested software programs.

scholarly journals Swarm Intelligence Algorithms for Weapon-Target Assignment in a Multilayer Defense Scenario: A Comparative Study

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 824 ◽  
Author(s):  
Ming Cao ◽  
Weiguo Fang
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Large Scale ◽  
Particle Swarm ◽  
Small Scale ◽  
Benchmark Problem ◽  
High Quality ◽  
Swarm Optimization ◽  
Target Assignment ◽  
And Performance

Weapon-target assignment (WTA) is a kind of NP-complete problem in military operations research. To solve the multilayer defense WTA problems when the information about enemy’s attacking plan is symmetric to the defender, we propose four heuristic algorithms based on swarm intelligence with customizations and improvements, including ant colony optimization (ACO), binary particle swarm optimization (BPSO), integer particle swarm optimization (IPSO) and sine cosine algorithm (SCA). Our objective is to assess and compare the performance of different algorithms to determine the best algorithm for practical large-scale WTA problems. The effectiveness and performance of various algorithms are evaluated and compared by means of a benchmark problem with a small scale, the theoretical optimal solution of which is known. The four algorithms can obtain satisfactory solutions to the benchmark problem with high quality and high robustness, while IPSO is superior to BPSO, ACO and SCA with respect to the solution quality, algorithmic robustness and computational efficiency. Then, IPSO is applied to a large-scale WTA problem, and its effectiveness and performance are further assessed. We demonstrate that IPSO is capable of solving large-scale WTA problems with high efficiency, high quality and high robustness, thus meeting the critical requirements of real-time decision-making in modern warfare.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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