A fuzzy-controller design by the hybrid of genetic algorithm and particle-swarm optimisation (F-HGAPSO) is employed for a thyristor-controlled series capacitor (TCSC) to improve the transient stability of flexible AC transmission systems (FACTS). According to the variation of rotation speed, a fuzzy controller decides an approximate series capacitance to achieve a better dynamic response of FAC...

The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accu...

In recent years Evolutionary Computation has its growth to extent. Amidst various Evolutionary computation approaches, Genetic Algorithms and Particle swarm optimisation are used in optimisation problems. The two approaches find a solution to a given objective function employing different procedures and computational techniques; as a result their performance can be evaluated and compared. The p...

The Particle Swarm Optimisation (PSO) algorithm consists of a population (or swarm) of particles that are “flown” through an n-dimensional space in search of a global best solution to an optimisation problem. PSO operates in Cartesian space, producing Cartesian solution vectors. By making use of an appropriate mapping function the algorithm can be modified to search in polar space. This mapping...

We introduce Division of Labor (DoL) from social insects to improve local optimisation of the Particle Swarm Optimiser (PSO). We compared the performance with the basic PSO, a GA and simulated annealing and found improvements around local optima. The PSO with DoL outperforms the basic PSO on most testcases and is comparable in local op-

In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functio...

Multiple-input multiple-output (MIMO) technologies are capable of substantially improving the achievable system’s capacity, coverage and/or quality of service. The system’s ability to approach the MIMO capacity depends heavily on the designs of MIMO receiver and/or transmitter, which are generally expensive optimisation tasks. Hence, researchers and engineers have endeavoured to develop efficie...

Particle Swarm Optimisers (PSOs) search using a set of interacting particles flying over the fitness landscape. These are typically controlled by forces that encourage each particle to fly back both towards the best point sampled by it and towards the swarm’s best. Here we explore the possibility of evolving optimal force generating equations to control the particles in a PSO using genetic prog...

In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO upd...

Information Retrieval techniques traditionally depend on the setting of one or more parameters. Depending on the problem and the techniques the number of parameters can be one, two or even dozens of them. One crucial problem in Information Retrieval research is to achieve a good parameter setting of its methods. The tuning process, when dealing with several parameters, is a time consuming and c...