Distribution network reconfiguration for loss reduction by ant colony search algorithm

By C. Su, Chung-Fu Chang, J. Chiou. 2005

369 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f56602ffcead66a26dbc71fa7f0b86ef85e8d09d

How researchers see this paper

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

In Control and Systems Engineering

1. Steven H. Low
2. Yujie Tang
3. Qiuyu Peng
4. Khaleequr Rehman Niazi
5. Anil Swarnkar
6. Ravindra Singh
7. Rabih A. Jabr
8. Saeed Teimourzadeh

In Electrical and Electronic Engineering

1. Steven H. Low
2. Rabih A. Jabr
3. Yujie Tang
4. Qiuyu Peng
5. Ravindra Singh
6. Bikash Chandra Pal
7. Khaleequr Rehman Niazi
8. Anil Swarnkar

Citations in the corpus

• Application of binary group search optimization to distribution network reconfiguration (2014, background): “Artificial intelligence (AI) based algorithms, such as simulated annealing (SA) in [10,11], genetic algorithm (GA) in [12], an ant colony search in [13] and particle swarm optimization in [2,14,15] could be listed in third category.”
• Feeder Reconfiguration in Distribution Networks Based on Convex Relaxation of OPF (2015, methodology): “This network has been tested in [7], [24], [27]–[30] using different approaches.”
• Reconfiguration of Distribution Systems for Real Power Loss Minimization Using Adaptive Particle Swarm Optimization (2011, methodology): “Ant colony search algorithm [15] 55, 7, 86, 72, 13, 89, 90, 83, 92, 39, 34, 42, 62 469.”
• Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming (2012, methodology): “Some of the promising meta-heuristic or artificial intelligence techniques include simulated annealing [12], [13], genetic algorithms [14], [15], ant colony optimization [16], mixed-integer [17] and variable scaling [18] hybrid differential evolution, and harmony search [19].”