A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems

By Wei-neng Chen, Jun Zhang, Henry Shu-Hung Chung, Wen-liang Zhong, Weigang Wu, Yu-hui Shi. IEEE Transactions on Evolutionary Computation, 2010

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/3de362b2fca20ec8c47e30be28f4d918191f4f77

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohammad Reza Bonyadi
2. Maoguo Gong
3. Qing Cai
4. Z. Michalewicz
5. Sanchita Basak
6. Zahra Beheshti
7. Xiaowei Chen
8. Saptarshi Sengupta

In Computational Theory and Mathematics

1. Mohammad Reza Bonyadi
2. Maoguo Gong
3. Qing Cai
4. Z. Michalewicz
5. Zhiwei Ni
6. Sanchita Basak
7. Zahra Beheshti
8. Xiaowei Chen

In Industrial and Manufacturing Engineering

1. Zhiwei Ni
2. Sanchita Basak
3. Saptarshi Sengupta
4. R. Peters
5. Zhangjun Wu
6. Yun Yang
7. Dong Yuan
8. Xiao Liu

Citations in the corpus

• Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, positive): “aint on such approaches. Deligkaris et al [185] used a mutation operator on particle velocities to render better exploration capabilities to the Binary PSO. 6.1.3 Set Theoretic Approaches Chen et al. [186] used a set representation approach to characterize the discrete search spaces of combinatorial”
• A market-oriented hierarchical scheduling strategy in cloud workflow systems (2011, methodology): “To solve the workflow scheduling problem, a discrete version of PSO (DPSO) is presented in this paper as a candidate evolutionary algorithm [9].”
• Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “Also note, for reasons of space, articles which have investigated/applied PSO for/to a specific application, such as solving the knapsack problem (Bonyadi and Michalewicz, 2012) or the traveling salesman problem (Chen et al., 2010), are not reviewed in this study.”
• Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, background): “Recently, Chen et al. in [44] have advanced a set-based DPSO (S-PSO) in which the candidate solution and velocity are defined as a crisp set, and a set with possibilities, respectively.”
• A review of population-based meta-heuristic algorithm (2013, background): “…of meta-heuristic algorithms have shown promising results and many researchers have tried to improve the performance of these algorithms in optimization problems [85, 86, 87, 88, 89], more research is still needed to overcome the disadvantages of these algorithms and to enhance their efficiency.”