Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem

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Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization (2021, methodology): “In UAV path planning, several variants have been proposed such as the classic PSO [31, 25], phase angle-encoded PSO ( θ -PSO) [39, 40, 30], quantum-behaved PSO (QPSO) [41, 30] and discrete PSO (DPSO) [1, 42].”
Particle swarm optimization-based algorithms for TSP and generalized TSP (2007, methodology): “Clerc proposed a brief outline of the PSO method for solving TSP problems [15].”
An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem (2015, methodology): “However, to further improve this diver-siﬁcation of the algorithm, we impose a distance between particles positions as deﬁned by Clerc in his paper (Clerc 2004).”
An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems (2016, methodology): “…colony optimization ([Dorigo & Gambardella(1997), Jun-man & Yi(2012)]), the variable neighborhood search ([Carrabs et al.(2007)Carrabs, Cordeau & Laporte, Burke et al.(2001)Burke, Cowling & Keuthen]), or the particle swarm optimization ([Clerc(2004), Shi et al.(2007)Shi, Liang, Lee, Lu & Wang]).”
Particle swarm optimization algorithm: an overview (2017, methodology): “Clerc (2004) brought a contraction-expansion coefﬁcient into evolution algorithms to ensure the algo-rithm convergence, while relaxing the speed bound.”