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Urban congestion in major cities of Malaysia is getting severe over decades with increasing active vehicles and travelling time on the road. Part of Intelligent Transportation Systems development involves advanced computation in traffic management to cope for the projecting congestion trend. This work simulates traffic system and develop an optimising algorithm to instruct the traffic signal timing...
With the recent developments in energy storage systems (ESS), there is a global trend to harness the potential advantages of these devices to enhance the operation and control of power systems. In this paper, a study on optimal allocation of STATCOM accompanied by ESS to improve static voltage stability of power systems is carried out. A method for clustering the power system buses, from the voltage...
A gene regulatory network reveals the regulatory relationships among genes at a cellular level. The accurate reconstruction of such networks using computational tools, from time series genetic expression data, is crucial to the understanding of the proper functioning of a living organism. Investigations in this domain focused mainly on the identification of as many true regulations as possible. This...
In integration of renewable power the use of storage is gaining prominence and various types of storage and its combinations have been investigated in detail. Practical applications of storage require an accurate method for sizing them. This paper discusses a method of using an optimization technique to find the size of a flywheel storage device inertia required for smoothing the power output from...
Here, we have proposed a statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data. The recurrent neural network formalism has been implemented to extract the underlying dynamics from time series microarray datasets accurately. The proposed swarm intelligence framework has...
In this paper a design approach for a sensorless controlled, brushless, interior permanent magnet machine is attained. An initial study based on established electrical machine formulas provides the machine's basic geometrical sizing. The next design stage combines a particle swarm optimisation (PSO) search routine with a magneto-static finite element (FE) solver to provide a more in depth optimisation...
In multi-robot exploration operation, each robot has to continuously decide which place to move next, after exploring their current location. In this paper we use the extended version of Particle Swarm Optimization (PSO) to robotic application, which is referred to as Robotic Particle Swarm Optimization (RPSO), a technique to compute robots' new location. To better adapt this technique to the collective...
This paper aimed at exploring the performance of Particle Swarm Optimisation with Exponentially Varying Inertia Weight Factor (PSO-EVIWF) for solving Multi-Area Economic Dispatch (MAED) problem with tie line constraints considering valve-point loading in each area. The effectiveness of the proposed algorithm has been verified on 4 interconnected areas with 16 generators standard test system. The paper...
Joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between...
The fuzzy c-partition entropy approach for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper we applied fuzzy entropy in image segmentation, used it to select the fuzzy region of membership function automatically so that an image can be transformed into fuzz domain with maximum fuzzy entropy, and implemented...
This paper presents the use of a bio-inspired method in robotics research. We discuss the Particle swarm optimization (PSO) for two ground robots: an omnidirectional rolling robot and a biped walker robot. For the wheeled robot, we studied the navigation in a flat environment with eventual obstacles. Thus, for the biped robot, we applied on the gesture of the straight walk. The PSO algorithm shows...
Power system structure is undergoing through restructuring process since a decade. Everyday a bulk amount of power is generated, transmitted and distributed via transmission network. The active power or the real power generated from the generator needs the reactive power for supporting its own transmission. This reactive power generation has some minimum limit which if generator fails to produce it...
Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle...
This paper proposes a new multi-objective optimization model to minimize congestion cost, load curtailment and generation cost simultaneously to restore the equilibrium of operating point of the system under contingency. The solution algorithms of the proposed method are based on the Particle Swarm Optimization (PSO) in which load curtailment and generation cost have been optimized without breaching...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. However, straightforward application of PSO suffers from premature convergence and lacks of intensification around the local best locations. In this paper, we propose a new particle swarm optimization strategy, namely, particle swarm optimization...
This paper proposes new optimization algorithms for the optimal tuning of PI controllers dedicated to a class of second-order processes with integral component and variable parameters. The sensitivity analysis with respect to the parametric variations of the controlled process leads to the sensitivity models. The augmentation of the output sensitivity functions over the integral of absolute error...
A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved, and the hierarchy particle swarm optimization (HPSO) algorithm solving the model is proposed, the algorithm can handle the level multi-objective optimization problem...
This paper applies Particle Swarm Optimization algorithm (PSO) in a Multi-objective Vehicle Routing Problem with Time Window (MVRPTW). Firstly, through the problem analysis, establish a versatile mathematical model. Secondly, introduce an effective particle code to successfully implement the algorithm. Finally, examples prove that PSO can be obtained the optimal solution quickly and efficiently of...
When the robotic belt grinding system needs to control the removal rate accurately, to optimize the grinding parameters is an important task after the model is obtained. In this paper, what is different from the previous methods is that the output of the model is not the removal rate but the workpiece feedrate vw or the normal grinding force Fn, so the reverse resolution of the model doesn't need...
Two-ray model is the basic line-of-sight propagation model for radio-wave propagation. Fermat's principle of Geometrical optics theory shows that the optical ray path is the minimum distance, which can make the propagation problem of radio-wave into the optimization problem of path function. Particle Swarm Optimization (PSO) with the characteristic of better searching optimization, was used to calculate...
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