Cellular particle swarm optimization

Shi, Yang; Liu, Hongcheng; Gao, Liang; Zhang, Guo Hui

doi:10.1016/j.ins.2010.05.025

Cited by 205 publications

(92 citation statements)

References 32 publications

(34 reference statements)

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“…and the references cited therein [31]. Another parameter called constriction coefficient is introduced with the hope that it can insure a PSO to converge.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Economic and Feasibility Studies Using Particle Swarm Optimization for Petroleum and Gas Projects

2017

IJAERD

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“…and the references cited therein [31]. Another parameter called constriction coefficient is introduced with the hope that it can insure a PSO to converge.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Economic and Feasibility Studies Using Particle Swarm Optimization for Petroleum and Gas Projects

2017

IJAERD

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“…The ω can be estimated by the week stable region. The average of ω is 0.75 [31]. Linearly Decreasing Inertia Weight Method (LDIWM)…”

Section: B Wmn-pso System For Mesh Router Node Placementmentioning

confidence: 99%

Investigation of Fitness Function Weight-Coefficients for Optimization in WMN-PSO Simulation System

Sakamoto

Oda

Ikeda

et al. 2016

2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)

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“…It has been found to be effective in solving large-scale problems in many applications (Al-Shihabi et al, 2008;Shi andÓlafsson, 2000a,b;Shi et al, 2011 At each iteration of the NP algorithm, the feasible region is divided into two subsets heuristically; one called "most promising" and the other called "complimentary". The algorithm then further partitions the "most promising" subset into M smaller subregions.…”

Section: Nested Partitionmentioning

confidence: 99%

A bi-level model of dynamic traffic signal control with continuum approximation

Han

Sun

Liu

et al. 2015

Transportation Research Part C: Emerging Technologies

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This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955;Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in , which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure.

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Cellular particle swarm optimization

Cited by 205 publications

References 32 publications

Economic and Feasibility Studies Using Particle Swarm Optimization for Petroleum and Gas Projects

Economic and Feasibility Studies Using Particle Swarm Optimization for Petroleum and Gas Projects

Investigation of Fitness Function Weight-Coefficients for Optimization in WMN-PSO Simulation System

A bi-level model of dynamic traffic signal control with continuum approximation

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