Comparison between Genetic Algorithms and Particle Swarm Optimization

By R. Eberhart, Yuhui Shi. 1998

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Citations in the corpus

Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, methodology): “…series of random search methods based on genetic algorithms (Shiffman et al., 2012), such as evolutionary algorithms (Simon, 2013; Orive et al., 2014), particle swarm optimization (Eberhart and Shi, 1998; Lorenzo et al., 2017), and covariance matrix adaption evolutionary strategy (Hansen, 2016).”
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (2004, methodology): “Therefore, Angeline [8] related these two acceleration factors to the mutation function in evolutionary programming, whereas Shi and Eberhart [13] related these two factors to the crossover operation in genetic algorithms.”
Adapting Particle Swarm Optimizationto Dynamic Environments (2001, methodology): “The particle swarm optimization (PSO) was originally designed by Kennedy and Eberhart [5,11,15] and has been compared to genetic algorithms [6,1,13] for efficiently finding optimal or near-optimal solutions in large search spaces.”
Fitness-distance-ratio based particle swarm optimization (2003, methodology): “PSO was inspired from studies of various animal groups, and has since proven to be a powerful competitor to other evolutionary algorithms such as genetic algorithms [3].”
Stability analysis of the particle dynamics in particle swarm optimizer (2006, background): “The similarity between a population of particles in swarm optimization and a population of genotypes in genetic algorithms has resulted in a comparison between the two [2].”
Particle swarm optimization for integer programming (2002, background): “In other words, PSO is considered as performing mutation with a \conscience", as pointed out by Eberhart and Shi [6].”
On the computation of all global minimizers through particle swarm optimization (2004, background): “The resulting equations for the manipulation of the swarm are [45]–[47]”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, background): “Particle Swarm Optimization (PSO) is a heuristic search technique (which is considered as an evolutionary algorithm by its authors [18]) that simulates the movements of a flock of birds which aim to find food.”
Fuzzy adaptive particle swarm optimization (2001, background): “The inertia weight has characteristics that are reminiscent of the temperature parameter in the simulated annealing ( Eberhart and Shi 1998 ).”
Evolutionary Computation Meets Machine Learning: A Survey (2011, background): “SI algorithms share many common characteristics with EAs and are also regarded to be in the EC algorithm family [ 4 ].”
Multiple trial vectors in differential evolution for engineering design (2007, result): “This behavior agrees with other studies about PSO’s fast convergence (Angeline and Littlefair 1998, Eberhart and Shi 1998, Penev 2005).”
Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization (2021, background): “It is also known to be less sensitive to initial conditions and variations of objective functions and is able to adapt to various environment structures via a small number of parameters including one acceleration coeﬃcient and two weight factors [33].”