Guest Editorial Special Issue on Particle Swarm Optimization

Eberhart, Russell C.; Shi, Yafei

doi:10.1109/tevc.2004.830335

Cited by 201 publications

(64 citation statements)

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“…PSO, which is introduced in 1995 (Kennedy & Eberhart 1995;Eberhart & Shi 2004), is a population-based search algorithm which exhibits good convergence characteristics. It has since been successfully applied to solve many complex power system optimization problems (AlRashidi & El-Hawary 2009;Heo et al 2006;Vlachogiannis & Lee 2006).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Solving Multi-Objective Voltage Stability Constrained Power Transfer Capability Problem using Evolutionary Computation

Goh¹,

Saha²,

Zhang³

2011

International Journal of Emerging Electric Power Systems

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Competitive market forces and the ever-growing load demand are two of the key issues that cause power systems to operate closer to their system stability boundaries. Open access has since introduced competition and therefore promotes inter-regional electrical power trades. However, the economic flows of electrical energy between interconnected regions are usually constrained by system physical limits, e.g. transmission lines capacity and generation active/reactive power capability etc. As such, there is a limitation to the capacity of electrical power that regions can export or import. This maximum allowable electrical power transfer is normally referred to as Total Transfer Capability (TTC). It is critical to understand that TTC does not necessarily represent a safe and reliable amount of inter-regional power transfer as one or more operational limits are usually binding when quantifying TTC. Hence, it is of interest that power system stability issues are being considered during power transfer capability assessment in order to provide a more appropriate and secure power transfer level.The aim of this paper is to formulate an Optimal Power Flow (OPF) algorithm, which is capable of evaluating inter-area power transfer capability considering mathematically-complex voltage collapse margins. Through a multi-objective optimization setup, the proposed OPF-based approach can reveal the nonlinear relationships, i.e. the pareto-optimal front, between transfer capability and voltage stability margins. The feasibility of this approach has been intensively tested on a 3-machine 9-bus and the IEEE 118-bus systems.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Solving Multi-Objective Voltage Stability Constrained Power Transfer Capability Problem using Evolutionary Computation

Goh¹,

Saha²,

Zhang³

2011

International Journal of Emerging Electric Power Systems

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“…However, implementations of algorithms such as Simplex and SoftPOSIT failed to align given the complexity of the building model and the high clutter of real-site images. We achieved reasonably good results with a powerful evolutionary optimizer, Particle Swarm Optimization (PSO) [7].…”

Section: Objectives and Contributionsmentioning

confidence: 99%

“…Non-linear optimisation of the camera pose then proceeds using a Particle Swarm based approach [7] to minimise the Sum of Squared Distances between the two sets of segments. PSO is a stochastic technique that iteratively searches across the multidimensional problem domain using a "swarm of particles" that are each guided by their own best solution, and by knowledge of the current global best for the entire swarm.…”

Section: Camera To Model Alignmentmentioning

confidence: 99%

Towards Automated Visual Assessment of Progress in Construction Projects

Lukins¹,

Trucco

2007

Procedings of the British Machine Vision Conference 2007

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Current assessment of progress in construction projects is a manual task that is often infrequent and error prone. Images of sites are extremely cluttered and rife with shadows, occlusions, equipment, and people -making them extremely hard to analyse. We present a first prototype system capable of detecting changes on a building site observed by a fixed camera, and classifying such changes as either actual structural events, or as unrelated. We exploit a prior building model to align camera and scene, thus identifying image regions where building components are expected to appear. This then enables us to home in on significant change events and verify the actual presence of a particular type of component. We place our approach within an emerging paradigm for integration in the construction industry, and highlight the benefits of automated image based feedback.

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“…Simplicity and ease of implementation has made PSO a popular area of research .It has wide range of applications such as fuzzy networks, power control, computer graphics, distribution, sensor and communication networks etc. PSO provides best solution for the hard problems and also used to solve real valued, binary and discrete problems [3] .But Standard PSO suffers from the problem of premature convergence in which the particle gets confined to local optima while looking for the best solution. Various PSO variants were developed to overcome this problem.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Particle Swarm Optimization and Particle Swarm Optimization with Aging Leader and Challengers towards Benchmark Functions

Sharma¹,

Kaur²

2015

IJCA

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Particle swarm optimization is the populace based heuristic optimization technique motivated by swarm intelligence and aims to find the best solution in the swarm. Aging leader and challengers with Particle swarm optimization (ALC-PSO) is a PSO variant in which concept of leader and challenger is implanted ALC-PSO has been successful in preventing premature convergence problem of PSO. In this paper, we performed experimental analysis of the performance of ALC-PSO and Standard PSO Algorithm on different benchmark functions and made an effort to list out the performance differences between PSO and ALC-PSO.

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Guest Editorial Special Issue on Particle Swarm Optimization

Cited by 201 publications

References 0 publications

Solving Multi-Objective Voltage Stability Constrained Power Transfer Capability Problem using Evolutionary Computation

Solving Multi-Objective Voltage Stability Constrained Power Transfer Capability Problem using Evolutionary Computation

Towards Automated Visual Assessment of Progress in Construction Projects

Comparative Analysis of Particle Swarm Optimization and Particle Swarm Optimization with Aging Leader and Challengers towards Benchmark Functions

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