A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM

By R. Hassan, B. Cohanim, O. D. Weck, Gerhard Venter. 2005

1,360 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computational Theory and Mathematics

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In Aerospace Engineering

Ruben E. Perez

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Citations in the corpus

Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016, background): “…over other black-box optimization meth-ods such as surrogate models [3]—which require that the adversary has the training data and enough computational resources to train an eﬀective surrogate—and genetic algorithms [2]—which though similar to PSO are much less computationally eﬃcient [15, 34].”
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey (2011, background): “3) High-quality solutions because of its ability to escape from local optima [8], [9].”
Examination of energy price policies in Iran for optimal configuration of CHP and CCHP systems based on particle swarm optimization algorithm (2010, positive): “At each iteration, velocities and positions of particles are updated according to (Hassan et al., 2005) Viðkþ1Þ ¼oViðkÞþc1r1ðpiðkÞ XiðkÞÞþc2r2ðpgðkÞ XiðkÞÞ ð49Þ Xiðkþ1Þ ¼ XiðkÞþViðkþ1Þ ð50Þ where o is the inertia weight and ranges from 0.2 to 1.0.”
Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches (2019, methodology): “e er, it can be observed that the PSOANFIS model was slightly be ter than GAANFIS, as the PSO optimiz tion algorithm was a better c nd date in terms of computational efficiency [63].”
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, background): “In the classification phase, a point x is assigned a label y according to y = sgn[w x + b].”
Particle Swarm Optimization in Structural Design (2007, background): “It is also more efficient, requiring a smaller number of function evaluations, while leading to better or the same quality of results (Hu et al., 2003; and Hassan et al., 2005).”
Optimization of sensor placement for structural health monitoring: a review (2019, methodology): “The results indicate that the BSSP and FSSP techniques are sensitive to the initial candidate locations and also the number of sensors, but given the right starting conditions, they have the ability to outperform the GA.”