A new hybrid PSOGSA algorithm for function optimization
By S. Mirjalili, Siti Zaiton Mohd Hashim. International Conference on Computer and Information Application, 2010
600 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/12ffd7084bedaab0904a683b35643071883b22f3
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
- Seyedali Mirjalili
- Yongquan Zhou
- Andrew Lewis
- Qifang Luo
- Ying-Biao Ling
- Sen Zhang
- Zhiming Li
- Junmin Song
In Biomedical Engineering
- Seyedali Mirjalili
- Andrew Lewis
- Yongquan Zhou
- Qifang Luo
- Ying-Biao Ling
- Sen Zhang
- Zhiming Li
- Junmin Song
In Control and Systems Engineering
- Seyedali Mirjalili
- Andrew Lewis
- Yongquan Zhou
- Qifang Luo
- Ying-Biao Ling
- Sen Zhang
- Zhiming Li
- Junmin Song
Citations in the corpus
- Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm (2012, background): “The above-mentioned remarks make PSOGSA powerful enough to solve a wide range of optimization problems [30].”
- Let a biogeography-based optimizer train your Multi-Layer Perceptron (2014, methodology): “It has been proven by the well-known No Free Lunch theorem (NFL) that there is no heuristic algorithm best suited to solving all optimization problems [22,77,7,39].”
- Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems (2016, methodology): “In addition, the 𝑝 values of Wilcoxon’s rank-sum in Table 9 show that the result of LMFO in 𝑓 16 is not significantly better than DA andGGSA algorithms (5% significance level), but it is significantly different compared with ABC, MFO, PSOGSA, and BA.”
- Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization (2017, methodology): “Such values represent the best parameter sets for these algorithms according to the original paper of MFO [28], PSOGSA [29], BA [3], ABC [30], and WOA [8].”
- Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection (2019, methodology): “Other similar works are: a PSO with DE (PSODE) [26], hybrid PSO and Gravitational Search Algorithm (GSA) (PSOGSA) [27].”
- Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, unknown): “Function Performance PSO [208] PSO [209] PSO [210] PSOGSA [210] DEPSO [150]”
- Evolutionary population dynamics and grey wolf optimizer (2014, unknown): “• Social thinking of PSO was integrated to GSA in [24].”