HEPSO: High exploration particle swarm optimization

Mahmoodabadi, Mohammad Javad; Mottaghi, Zahra Salahshoor; Bagheri, Ahmad

doi:10.1016/j.ins.2014.02.150

Cited by 79 publications

(43 citation statements)

References 16 publications

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“…The final PSO variant investigated is the High Exploration PSO (HEPSO) [49], which combines elements of artificial bee colony and GA. In this case, the inertia weight is adapted in the same manner as APSO-09 and the acceleration coefficients are updated using the same method as APSO-12.…”

Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

“…In this case, the inertia weight is adapted in the same manner as APSO-09 and the acceleration coefficients are updated using the same method as APSO-12. The main method for updating the particles' position is based on a GA crossover technique as described in [49]; at the initial stages, however, a particle's position may be updated based on an artificial bee colony algorithm.…”

Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

“…The parameters for the various PSOs is typically inherited from the papers they were proposed in, and shown in Table 2. In the table ξ is the search iteration number and Ξ is the maximum number of iterations which was set at 5000. f is defined in [46,49] is the evolutionary factor that defines the spread of particles relative to the current optimum. For CEPSO the sinusoidal map which performed best in the benchmarks in [47] is used for the chaotic map.…”

Section: Algorithm Parametersmentioning

confidence: 99%

“…For CEPSO the sinusoidal map which performed best in the benchmarks in [47] is used for the chaotic map. The probabilities for the artificial bee colony and GA operators used by HEPSO are the same as those in [49]. For GPSO and LPSO better results were obtained by using the parameter values suggested in [51] rather than the typically used c 1 = c 2 = 2.0.…”

Section: Algorithm Parametersmentioning

confidence: 99%

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Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are Preprint submitted to Applied Soft Computing September 18, 2015 discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle Swarm Optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.

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Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

Section: Algorithm Parametersmentioning

confidence: 99%

Section: Algorithm Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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“…Several different topologies have been proposed in the literature [4,68,42,71,37,75]. Another growing area in the refinement of PSO algorithm aims towards hybridization of PSO with other methods to overcome its weaknesses [16,21,38]. A comprehensive survey for hybridized PSO variants is given in [67,17].…”

Section: Accepted Manuscriptmentioning

confidence: 99%