Ant colony optimization for continuous domains

By Ping Guo, Lin Zhu. International Conference on Computing, Networking and Communications, 2012

328 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e325b2541e3e2b139908e6ceac65375b05008a5c

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Citations in the corpus

Genetic Learning Particle Swarm Optimization (2016, methodology): “In this section, we further compare GL-PSO with other evolutionary and swarm intelligence algorithms, including a continuous ant colony optimization (ACOR) [51], self-adaptive DE (SaDE) [52], EDA [53], artificial bee colony (ABC) [54], and evolution strategy with covariance matrix adaptation (CMA-ES”
Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems (2018, methodology): “A large number of nature-inspired metaheuristics have been proposed in the last decades [4], for example, Ant Colony Optimization (ACO) [5], the Artificial Bee Colony (ABC) [6], Bacterial Colony Foraging (BCF) [7], the Bat-Inspired Algorithm (BA) [8], the Dolphin Echolocation (DE) [9], the Firefly A”
A Comprehensive Review of Swarm Optimization Algorithms (2015, background): “The fourth experiment is available in supplement section where the comparison between seven algorithms discussed on twenty benchmark functions with all results being collected from literature [140–157].”
A social spider algorithm for global optimization (2015, background): “Although in recent years there are ACO-variant algorithms designed mainly to solve continuous problems [27], the performance is not as good as the performance of the original ACO in solving combinatorial problems like the Traveling Salesman Problem.”
Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization (2010, background): “In [12], a direct extension of ant colony optimization (ACO) is proposed for continuous optimization.”
Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction (2019, methodology): “optimization problems in continuous domains [71].”