A Trigonometric Mutation Operation to Differential Evolution

By Hui-Yuan Fan, Jouni Lampinen. Journal of Global Optimization, 2003

548 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/faead4a84e25a3e9cb2581f3faf06f54432ed4bd

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. Pinar Çivicioglu
2. Swagatam Das
3. Ponnuthurai Nagaratnam Suganthan
4. Zhihua Cai
5. Wenyin Gong
6. Shahryar Rahnamayan
7. Bahriye Akay
8. Janez Brest

In Computational Theory and Mathematics

1. Pinar Çivicioglu
2. Swagatam Das
3. Zhihua Cai
4. Ponnuthurai Nagaratnam Suganthan
5. Wenyin Gong
6. Shahryar Rahnamayan
7. Janez Brest
8. Charles X. F. Ling

Citations in the corpus

• Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives (2008, methodology): “In [31], a cooperative particle swarm optimizer (CPSO) is implemented.”
• Differential Evolution With Ranking-Based Mutation Operators (2013, background): “Fan and Lampinen [9] proposed a new version of DE which uses an additional mutation operation called trigonometric mutation operation.”
• Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Kukkonen and Lampinen extended DE/rand/1/bin to solve multiobjective optimization problems in their approach called generalized differential evolution (GDE).”
• Metaheuristics: review and application (2013, background): “Select 3 parents at random (Fan & Lampinen, 2003) or a neighbourhood-based mutation operator.”
• Opposition-Based Differential Evolution (2008, background): “The FADE algorithm was introduced by Liu and Lampinen [13].”
• Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, unknown): “Mutation Operators Following initialization, the evolution process begins with the current population a new individual, called the mutant”
• Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks (2007, methodology): “In order to overcome the disadvantages of gradient based algorithms, many global optimization methods have been proposed for training feed-forward neural networks such as Genetic Algorithms [4,5,6,7,8,9,10,11,12,13,14,15,16,17], The Particle Swarm Optimization algorithm [18, 19, 20, 22, 21, 23], Dif”
• Population size reduction for the differential evolution algorithm (2008, methodology): “The difference between the TDE algorithm and the original DE algorithm is in the new trigonometric mutation operation added into the TDE.”
• A novel population initialization method for accelerating evolutionary algorithms (2007, background): “…nature of algorithms on measured metric, the reported number of function calls (NFC) for each algorithm is the average value over 100 runs for each test function (this number has commonly been set to a value between 30 and 100 for many studies [15] [16] [17] [18] [19] [20] [21] [22] [23]).”