Particle swarm and ant colony algorithms hybridized for improved continuous optimization

By Prakash Shelokar, Patrick Siarry, V. K. Jayaraman, Bhaskar D. Kulkarni. Applied Mathematics and Computation, 2007

362 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8186a138935fa9588279b55ecedc283ca8bf7f33

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. Yaochu Jin
2. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Sanchita Basak
6. Saptarshi Sengupta
7. R. Peters
8. Lei Liu

In Computational Theory and Mathematics

1. Yaochu Jin
2. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Sanchita Basak
6. Saptarshi Sengupta
7. R. Peters
8. Lei Liu

Citations in the corpus

• Particle Swarm Optimization With an Aging Leader and Challengers (2013, neutral): “In the literature, various types of search operators have been used for this, including genetic operators such as selection [19], crossover [20], and mutation [12], [21]–[23]; evolutionary computation paradigms such as genetic algorithm (GA) [24], differential evolution [25], and ant colony optimiza”
• Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, background): “[104] 2007 PSACO Improved continuous optimization”
• A Competitive Swarm Optimizer for Large Scale Optimization (2015, methodology): “One straightforward idea is to combine PSO with different evolutionary algorithms, such as genetic algorithms [26], differential evolution [27], and ant colony optimization [28], [29].”
• Particle swarm optimization algorithm: an overview (2017, background): “…(Liu et al. 2014; Geng et al. 2014), taboo search (Wen and Liu 2005), Levenberg–Marquardt algorithm (Shirkhani et al. 2014), ant colony algorithm (Shelokar et al. 2007), artifi-cial bee colony (Vitorino et al. 2015; Li et al. 2011), chaos algorithm (Yuan et al. 2015), differential evolution (Zhai…”
• A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “Hybrid PSO algorithms, which can be categorized into the second category of PSO variants, combine PSO with other search strategies, such as genetic algorithms [65,39,80], differential evolution [39], and ant colony optimization [70].”