Nonlinear Conflict Resolution and Flow Management Using Particle Swarm Optimization

Hong, Youkyung; Choi, Byungjoo; Oh, Gyeongtaek; Lee, Keumjin; Kim, Youdan

doi:10.1109/tits.2017.2684824

Cited by 19 publications

(12 citation statements)

References 29 publications

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“…Among many heuristic algorithms, the swarm intelligence based evolutionary methods are suitable tools for solving planning problems, since they can approximate the optimal solutions by randomly generating initial solution groups and iteratively optimizing the problems of concern. Many research works have been conducted to explore and improve the intelligence based evolutionary algorithms, such as the genetic algorithm [29], particle swarm optimization [29,30], the artificial fish school algorithm [31], the firefly algorithm [32], chicken swarm optimization [33], and the bird swarm algorithm (BSA) [34]. Among these algorithms, BSA can effectively optimize non-convex and non-differentiable optimization problems through simulating the natural behavior of birds [35], and is not subject to local optimal solutions.…”

Section: The Joint Planning Methods For Substations and Lines In The Dmentioning

confidence: 99%

A Joint Planning Method for Substations and Lines in Distribution Systems Based on the Parallel Bird Swarm Algorithm

Wu¹,

Li²,

Liang³

et al. 2018

Energies

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Reasonable distribution network planning schemes can not only improve the power quality and power supply capacity of the power system, but also increase the economic benefits and welfare of the whole society. In this work, a bi-level optimization model is proposed for the joint planning of substations and lines in looped urban distribution systems. The upper-level model aims to address the substation locating and sizing problem, whereas the lower-level model the network planning problem. Both the substations directly supplying power to a load and the contralateral substations that act as backup power source to the load are considered in the bi-level model. In order to solve the bi-level planning model which is mathematically mixed integer programing and with plenty of continuous and discrete variables, the bird swarm algorithm is improved and applied based on the idea of parallel computing of big data theory. Simulations on actual planning problems are employed to verify the effectiveness of the proposed bi-level distribution network planning model and the parallel bird swarm algorithm.

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Section: The Joint Planning Methods For Substations and Lines In The Dmentioning

confidence: 99%

A Joint Planning Method for Substations and Lines in Distribution Systems Based on the Parallel Bird Swarm Algorithm

Wu¹,

Li²,

Liang³

et al. 2018

Energies

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“…The binary decision variable b 2i 1 i 2 determines which aircraft enters the airspace first. Constraint sets (15) and (16) maintain the required separation time between all aircraft pairs for conflicts using different entry points and the same exit point. The binary decision variable b 3i 1 i 2 determines which aircraft leaves the airspace first.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The model uses goal programming and aimed to minimise the deviation from the current heading angle, airspeed and flight level. Hong et al (16) proposed a MINLP model in order to maintain conflict-free flight by HAC and SC. The model adopted the particle swarm optimisation (PSO) technique to resolve conflicts, because the nonlinear constraints increased the solution time and made it difficult to reach a feasible solution.…”

Section: Introductionmentioning

confidence: 99%

Conflict-free en-route operations with horizontal resolution manoeuvers using a heuristic algorithm

Çeçen

Çetek

2020

Aeronaut. j.

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Aircraft conflict resolution is an important part of air traffic control operations. This study presents a mixed integer linear programming model (MILP) using a space discretisation technique to deal with aircraft conflict resolutions in en-route flight operations. The purpose of space discretisation is to concentrate on only the significant points of the airspace. The model integrates the multi entry point approach with an airspeed adjustment technique in the horizontal plane. The model aims to generate conflict-free trajectories while minimising the total changes in entry points and airspeed values. A new heuristic algorithm was developed due to the complexity of the problem. The computational results demonstrated that the proposed approach resolved aircraft conflicts for 450 different traffic scenarios in less than a minute. Considerable fuel savings were achieved with no significant increase in delay or flight time compared to conventional vectoring techniques in a fixed entry point airspace structure.

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“…Ma et al resolved the 3 flights conflict through combining the ADS-B reports with the track optimization and Genetic Algorithm [12]. According to Hong et al, the particle swarm optimization (PSO) was used to solve the nonlinear optimal conflict resolution and flow management problem in their study [13]. Han et al proposed the potential field differential evolution (PFDE).…”

Section: Introductionmentioning

confidence: 99%

Factorial Experiment on Route Structures Using SAAM on a Specified Area in ASEAN

et al. 2018

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Due to a rapid growth in the air transport industry, ASEAN (Association of Southeast Asia Nations) region has experienced a surge in the air traffic movement in recent years. Several forecasting papers foretell to have the continuous traffic growth for ASEAN region [1, 2]. The pilot study of ATS routes forecasts also conclude that there is a need to relook at the current route structures in selected areas in ASEAN [3]. The interaction between the structure of ATS routes and the number of intersections has been studied by using the number of conflicts [4]. This paper will serve as a continuation of the previous study, and the objective is to determine whether there is any consistency in the conclusion when the selected area is changed, using the methodology proposed.

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Nonlinear Conflict Resolution and Flow Management Using Particle Swarm Optimization

Cited by 19 publications

References 29 publications

A Joint Planning Method for Substations and Lines in Distribution Systems Based on the Parallel Bird Swarm Algorithm

A Joint Planning Method for Substations and Lines in Distribution Systems Based on the Parallel Bird Swarm Algorithm

Conflict-free en-route operations with horizontal resolution manoeuvers using a heuristic algorithm

Factorial Experiment on Route Structures Using SAAM on a Specified Area in ASEAN

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