Stability analysis of social foraging swarms

By Veysel Gazi, Kevin M. Passino. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2004

668 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Public Health, Environmental and Occupational Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ff5728c168db27bcf9864b1e13410fb976fc6d3f

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Citations in the corpus

GSA: A Gravitational Search Algorithm (2009, methodology): “…biological immune systems [8], Ant Colony Optimization, ACO, mimics the behavior of ants foraging for food [5], Bacterial Foraging Algorithm, BFA, comes from search and optimal foraging of bacteria [11,19] and Particle Swarm Optimization, PSO, simulates the behavior of ﬂock of birds [3,17].”
Engineering optimisation by cuckoo search (2010, background): “Two important characteristics of metaheuristics are: intensification and diversification (Blum and Roli 2003, Gazi and Passino 2004, Yang 2009).”
Swarm robotics: a review from the swarm engineering perspective (2013, methodology): “Swarm Intell”
Containment of Higher-Order Multi-Leader Multi-Agent Systems: A Dynamic Output Approach (2016, background): “There has been tremendous amount of research to date on consensus control of networked multi-agent systems, due to its potential applications in distributed sensor networks, unmanned air vehicles (UAVs), automated highway systems, and so forth (see [1]–[4], and the references therein).”
Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “Other forms of intensity can be deﬁned in a similar way to the ﬁtness function in ﬁreﬂy algorithm (Yang 2010b), genetic algorithms or the bacterial foraging algorithm (Gazi and Passino 2004).”
Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, background): “…a lot of theoretical analyses have been done on the algorithm (Kennedy, 1998; Ozcan and Mohan, 1999; Clerc and Kennedy, 2002; van den Bergh, 2002; Shi and Eberhart, 1998b; Trelea, 2003; Emara and Fattah, 2004; Gavi and Passino, 2003; Kadirkamanathan et al., 2006; Jiang et al., 2007; Poli, 2008).”
Firefly Algorithm, Lévy Flights and Global Optimization (2010, background): “Other forms of brightness can be defined in a similar way to the fitness function in genetic algorithms or the bacterial foraging algorithm (BFA) [6, 10].”
A connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements (2009, background): “The local artificial potential field produces attractive forces for larger distances and repulsive forces for small distances between agents, which has been widely used to regulate the distances between agents (Gazi and Passino 2003, 2004; Liu, Passino, and Polycarpou 2003a, b).”
Passivity as a Design Tool for Group Coordination (2007, background): “A major focus of research in group coordination is formation stability, where such local rules have been proposed in the form of artificial attraction and repulsion forces with neighboring vehicles [1]–[3].”
A class of attractions/repulsion functions for stable swarm aggregations (2004, methodology): “For more details see Gazi and Passino (2004).”
Firefly algorithm, stochastic test functions and design optimisation (2010, background): “Other forms of brightness can be deﬁned in a similar way to the ﬁtness function in genetic algorithms or the bacterial foraging algorithm (BFA) (Gazi and Passino 2004).”
From Swarm Intelligence to Swarm Robotics (2004, background): “A brief review of these problems, as well as their relation to swarming in general, is given in the introduction section of a recent paper by Passino [16] whose work has focused on swarm robotics control.”
Forced consensus in networks of double integrator systems with delayed input (2010, neutral): “…the potential broad applications in various areas, including congestion control in communication networks, cooperative control of multiple vehicle systems, formation control, swarming and flocking (Gazi & Passino, 2003, 2004; Tanner&Christodoulakis, 2007; Tanner, Jadbabaie, & Pappas, 2007).”
Firefly Algorithms for Multimodal Optimization (2009, background): “In certain sense, there is some conceptual similarity between the firefly algorithms and the bacterial foraging algorithm (BFA) [3, 7].”
Distributed leader-follower flocking control for multi-agent dynamical systems with time-varying velocities (2010, methodology): “Using a simplified Reynolds’ model, stability properties for swarm aggregation were discussed, and an asymptotic bound for the spatial size of the swarmwas computed in [16,17].”
Global target aggregation and state agreement of nonlinear multi-agent systems with switching topologies (2009, background): “…partly due to a broad application of multi-agent systems including consensus, swarming, and formation (referring to Chu, Wang, Chen, and Mu (2006), Cortés (2006), Egerstedt and Hu (2001), Fax and Murray (2004), Gazi and Passino (2004), Hu and Hong (2007) and Martinez, Cortes, and Bullo (2007)).”