Chaos-enhanced accelerated particle swarm optimization

By Amir H. Gandomi, Gun Jin Yun, Xin-She Yang, Siamak Talatahari. Communications in nonlinear science & numerical simulation, 2013

376 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b9cd7dc3546fb9d6c3c6a41bc2382f4ae588aa59

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. Arpan Kumar Kar
2. Xingshi He
3. Xin-She Yang
4. Sankalap Arora
5. Amir H. Gandomi
6. Seyedali Mirjalili
7. Satvir Singh
8. Gaige Wang

In Control and Systems Engineering

1. Arpan Kumar Kar
2. Sankalap Arora
3. Xingshi He
4. Satvir Singh
5. Priyanka Anand
6. Xin-She Yang
7. Mengchu Zhou
8. Yang Yu

Citations in the corpus

• Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “Cuckoo search (Yang & Deb, 2009; Gandomi et al., 2013; Yang & Deb, 2013) was developed for addressing single or multi-objective problems under complex nonlinear constraints.”
• Chaotic Krill Herd algorithm (2014, background): “Some major efforts in this area includes hybridizing chaotic sequences with memetic differential evolution algorithm [22], FA [13], gravitational search algorithm [20], imperialist competitive algorithm [41], charged system search [33] [42], PSO [14] [46], and GAs [21].”
• Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “The heuristic methods that have been adopted to optimize this problem are: Chaotic variant of Accelerated PSO (CAPSO) (Gandomi et al. 2013b), 1993).”
• Bat algorithm: literature review and applications (2013, methodology): “Furthermore, cuckoo search (CS) was based on the brooding behaviour of some cuckoo species (Yang and Deb, 2009; Gandomi et al, 2013) which was combined with L´evy flights.”
• Chaotic grasshopper optimization algorithm for global optimization (2019, methodology): “Some major efforts in this area include PSO [27], FA [26], BOA [28], GWO [29], GAs [30], big bang–big crunch algorithm [31], hybridizing chaotic sequences with memetic differential evolution algorithm [32], water cycle algorithm [33], imperialist competitive algorithm [34], artificial immune system ”
• Chaotic Local Search-Based Differential Evolution Algorithms for Optimization (2021, methodology): “Gandomi et al. [33] improved a PSO by using multiple chaotic maps to adjust its attraction parameter.”