Discrete Capacity Assignment in IP networks using Particle Swarm Optimization

Wille, Emílio C. G.; Yabcznski, Eduardo; Lopes, Heitor Silvério

doi:10.1016/j.amc.2010.12.003

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…The flocks achieve their best condition simultaneously through communication among members who already have a better situation. PSO is used for continuous problems, whereas parallel PSO is an efficient solution for optimal task assignment in distributed systems, which can be implemented with two parallel techniques: MPSO and MRPSO.…”

Section: Review Of Parallel Evolutionary Algorithmsmentioning

confidence: 99%

Parallel imperialist competitive algorithms

Majd

Sahebi

Daneshtalab

et al. 2018

Concurrency and Computation

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Summary The importance of optimization and NP‐problem solving cannot be overemphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods; they are mostly sequential but some of them have parallel implementations as well. We propose a multi‐population method to parallelize the Imperialist Competitive Algorithm. The algorithm has been implemented with the Message Passing Interface on 2 computer platforms, and we have tested our method based on shared memory and message passing architectural models. An outstanding performance is obtained, demonstrating that the proposed method is very efficient concerning both speed and accuracy. In addition, compared with a set of existing well‐known parallel algorithms, our approach obtains more accurate results within a shorter time period.

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Section: Review Of Parallel Evolutionary Algorithmsmentioning

confidence: 99%

Parallel imperialist competitive algorithms

Majd

Sahebi

Daneshtalab

et al. 2018

Concurrency and Computation

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show abstract

“…PSO for constrained optimization problem finds optimal location of particle which has best fitness and satisfies the constraints. The approaches focus on several ways, such as adding penalty function to fitness function [12], valuing the infeasible solution by constraints violation [13,14], adapting PSO parameters according to constraints violation [15,16], generating new feasible solution [17], and adjusting parameters to make infeasible solution become feasible [18].…”

Section: Solution Proceduresmentioning

confidence: 99%

Cooperative Passenger Inflow Control in Urban Mass Transit Network with Constraint on Capacity of Station

Guo

Jia

Qin

et al. 2015

Discrete Dynamics in Nature and Society

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In urban mass transit network, when passengers' trip demands exceed capacity of transport, the numbers of passengers accumulating in the original or transfer stations always exceed the safety limitation of those stations. It is necessary to control passenger inflow of stations to assure the safety of stations and the efficiency of passengers. We define time of delay (TD) to evaluate inflow control solutions, which is the sum of waiting time outside of stations caused by inflow control and extra waiting time on platform waiting for next coming train because of insufficient capacity of first coming train. We build a model about cooperative passenger inflow control in the whole network (CPICN) with constraint on capacity of station. The objective of CPICN is to minimize the average time of delay (ATD) and maximum time of delay (MTD). Particle swarm optimization for constrained optimization problem is used to find the optimal solution. The numeral experiments are carried out to prove the feasibility and efficiency of the model proposed in this paper.

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“…The PSO algorithm has been widely and successfully applied in the fields of multi-objective optimization [3,4], dynamic and uncertain environment [5][6][7], IP network [8], etc.…”

Section: Introductionmentioning

confidence: 99%