Swarm Intelligence From Natural To Artificial Systems

By W. Ziegler. 2016

415 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/522e1c3a5770de5fcec50daf7aa766fe9efa5d3f

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Citations in the corpus

Routing in Flying Ad Hoc Networks: A Comprehensive Survey (2020, background): “As deﬁned by Bonabeau et al. , “swarm intelligence is any effort to design distributed problem solving devices or algorithms motivated by the collective behavior of social insect colonies and other animal societies” [161].”
Swarm Robotic Behaviors and Current Applications (2020, unknown): “For example, Bonabeau et al. (1999) depict phenomena in social insects that had been transferred successfully to algorithms.”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, unknown): “Probably, the most important cornerstone was the birth of the branches which are known today as Evolutionary Computation and Swarm Intelligence [19, 20].”
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, unknown): “SI refers to the artiﬁcial implementation/ simulation of the collective and social intelligence of a group of living creatures in nature [7].”
The Arithmetic Optimization Algorithm (2021, unknown): “The results are compared with the following algorithms: • Genetic Algorithm (GA) [32] • Particle Swarm Optimization (PSO) [33] • Biogeography-based Optimization (BBO) [34] • Flower Pollination Algorithm (FPA) [35] • Grey Wolf Optimizer (GWO) [36] • Bat Algorithm (BAT) [37] • Firefly Algorithm (FA)…”
A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms (2021, background): “It refers to the collective intelligence that emerges from the cooperation of swarm agents [210].”
Everything you need to know about agent-based modelling and simulation (2016, methodology): “Swarm optimization, from the ﬁ eld of bio-inspired evolutionary algorithms (Olariu and Zomaya, 2006), has been used to solve practical, large-scale optimization problems (Bonabeau et al , 1999; Barbati et al , 2011).”
The blockchain: a new framework for robotic swarm systems (2016, background): “With a strong initial inﬂuence from nature and bio-inspired models [30], [48], [5], swarm systems are known for their adaptability to different environments [4] and tasks [6].”