A comparative study of differential evolution variants for global optimization

By Efrén Mezura-Montes, Jesús Velázquez-Reyes, Carlos Artemio Coello Coello. Annual Conference on Genetic and Evolutionary Computation, 2006

621 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/23ec90b7f51ebb1a290a760ed9b9ebfe8ba68eb6

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. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Zhihua Cai
4. Wenyin Gong
5. Ryoji Tanabe
6. Pinar Çivicioglu
7. Alex S. Fukunaga
8. Shahryar Rahnamayan

In Computational Theory and Mathematics

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Zhihua Cai
4. Wenyin Gong
5. Ryoji Tanabe
6. Pinar Çivicioglu
7. Alex S. Fukunaga
8. Yong Wang

Citations in the corpus

• Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “, while comparing the different variants of DE for global optimization in [17], have noted that DE/target-to-best/1 shows a poor performance and remains inefficient in exploring the search space, especially for multimodal functions.”
• Accelerating Differential Evolution Using an Adaptive Local Search (2008, result): “A recent study that empirically compares some of the variants of DE is presented in [30].”
• Spider Monkey Optimization algorithm for numerical optimization (2014, background): “[20] analyzed DE and its variants for global optimization and found that DE has deficiency of premature convergence and stagnation.”
• Success-history based parameter adaptation for Differential Evolution (2013, background): “However, it has been shown that on multimodal problems, this strategy often leads to premature convergence and poor performance [10].”
• Differential Evolution With Ranking-Based Mutation Operators (2013, background): “However , the choice of the best mutation operators for DE is not easy for a specific problem [16], [17], [18].”
• Opposition-Based Differential Evolution (2008, background): “There are other DE variants which are indicated by the notation DE/a/b/c [Mezura-Montes et al. 2006].”
• A Comprehensive Review of Swarm Optimization Algorithms (2015, background): “Mezura-Montes et al . have discussed several variants of DE and done a comparative study between them [73].”
• An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization (2012, background): “Each of these mutation and crossover operations may be effective over certain problems but poorly perform over the others, e.g., see works like [10] for claims, counter-claims, and comparisons among these evolutionary operators in DE. Similarly, in order to make the performance of DE more robust,…”
• Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, background): “It has been observed that “current-to-best/1” strategy performs poorly on exploring the search space when solving multimodal problems [11].”
• Population size reduction for the differential evolution algorithm (2008, background): “[20] conducted a comparative study of DE variants.”