Swarm Intelligence

By Marco Dorigo, Mauro Birattari. Lecture Notes in Computer Science, 2012

4,427 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/918a6d57f779b8f8269bc0d1489dcd014afb8120

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In Artificial Intelligence

In Control and Systems Engineering

Citations in the corpus

A comparative study of Artificial Bee Colony algorithm (2009, neutral): “Two important classes of population-based optimization algorithms are evolutionary algorithms [1] and swarm intelligence-based algorithms [2].”
Music-Inspired Harmony Search Algorithm (2009, methodology): “Parameters for the nonlinear Muskingum model, which can be used in the prediction of volumetric water flow rates for flood routing, were fitted in [1, 44] and also discussed in [4, 32].”
An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems (2010, background): “In PSO, each particle makes its decision using its own experience together with its neighbor’s experiences [20], [21].”
Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems (2010, neutral): “Recently, a new class of metaheuristics, called swarm intelligence , was proposed (Bonabeau, Dorigo, & Theraulaz, 1999; Dorigo & Stützle, 2004; Kennedy, Eberhart, & Shi, 2001).”
Social media big data analytics: A survey (2019, neutral): “Swarm intelligence is an e ﬀ ective problem-solving technique in arti ﬁ cial intelligence (Kennedy, 2006).”
Particle Swarm Methods (2018, background): “The theoretical background along with the potential for developing powerful optimization metaheuristics resulted in the emergence of a new category of algorithms under the name of Swarm Intelligence (SI) [12, 34, 62].”
Swarm Robotics: Past, Present, and Future (2021, background): “3072740 Swarm robotics started out as an application of swarm intelligence [3], [4], that is, the computational modeling of collective, self-organizing behavior that has resulted in several successful optimization algorithms [5], [6] now being used in fields ranging from telecommunications [7] to si”
Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects (2008, positive): “It is based on the simulation of simplified social models, such as bird flocking, fish schooling, and the swarming theory [17].”
Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “Other proposed metaheuristics include particle swarm optimization (inspired by the social behavior of bird flocking) [10], bees algorithm (inspired by the behavior of honey bees in collecting nectar) [11], harmony algorithm (inspired by the improvisation process of musicians) [12], etc.”
Well placement optimization subject to realistic field development constraints (2016, background): “5 [54], and the accelerations are chosen such that c1 + c2 = 4 [55].”
A novel SVM-kNN-PSO ensemble method for intrusion detection system (2016, methodology): “In 1995, Eberhart and Kennedy proposed the PSO method, which is a stochastic optimization technique that models the analogy of a swarm of birds in flight [5].”
Opposition based learning: A literature review (2017, methodology): “In [151], OBL was embedded to an orthogonal learning PSO which computes the opposite position of the worst particle in the swarm.”
Gbest-guided artificial bee colony algorithm for numerical function optimization (2010, positive): “The experimental results show that ABC algorithm is competitive with some conventional optimization algorithms, such as GA [1,2], DE [12], and PSO [3,4].”
Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art (2021, background): “…Moscato et al., 1989 ), and Differential Evolution (DE) ( Storn & Price, 1997 ) are inspired by biology; Artiﬁcial Bee Colony (ABC) ( Karaboga, 2005 ), Ant Colony Optimization (ACO) ( Dorigo & Blum, 2005 ), and Particle Swarm Optimization (PSO) ( Kennedy, 2006 ) are inspired by swarm intelligence.”
Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints (2011, methodology): “[11], PSO achieves optimization through a policy which emulates the “social interaction” of a population of atoms (particles) that evolves in a number of generations.”
Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, background): “Such global optimizers are typically simple, flexible and yet surprisingly efficient, which have been shown in many applications and case studies [9, 10, 11, 12, 2].”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, positive): “Ozcan and Mohan conducted several experiments for the special case of a single particle in a one-dimensional problem space in order to examine the effect of a deterministic acceleration constant [67], [91].”