Huilian Liao

A Fast Bacterial Swarming Algorithm for high-dimensional function optimization

A novel fast bacterial swarming algorithm (FBSA) for high-dimensional function optimization is presented in this paper. The proposed algorithm combines the foraging mechanism of E-coli bacterium introduced in bacterial foraging algorithm (BFA) with the swarming pattern of birds in block introduced in particle swarm optimization (PSO). It incorporates the merits of the two bio-inspired algorithms to improve the convergence for high-dimensional function optimization. A new parameter called attraction factor is introduced to adjust the bacterial trajectory according to the location of the best bacterium (bacterium with best fitness value). An adaptive step length is adopted to improve the local search ability. The algorithm has been evaluated on standard high-dimensional benchmark functions in comparison with BFA and PSO respectively. The simulation results have demonstrated the fast convergence ability and the improved optimization accuracy of FBSA.

A novel intelligent particle optimizer for global optimization of multimodal functions

A novel intelligent particle optimizer based on subvectors (IPO) is proposed in this paper, which is inspired by conventional particle swarm optimization (PSO). IPO uses only one particle instead of a particle swarm. The position vector of this particle is partitioned into a certain number of subvectors, and the updating process is based on subvectors and evolved to subvectors updating process, in which the particle adjusts the velocity intelligently by introducing a new learning factor. This learning factor utilizes the information contained in the previous updating process. The particle is capable of increasing its velocity towards the global optimum in lower dimensional subspaces and not being trapped in local optima. Experimental results have demonstrated that IPO has impressive ability to find global optimum. IPO performs better than recently developed PSO-based algorithms in solving some complicated multimodal functions.

Function optimisation by learning automata

This paper presents a new algorithm, Function Optimisation by Learning Automata (FOLA), to solve complex function optimisation problems. FOLA consists of multiple automata, in which each automaton undertakes dimensional search on a selected dimension of the solution domain. A search action is taken on a path which is identified in the search space by the path value, and the path value is updated using the values of the states visited in the past, via a state memory that enables better use of the information collected in the optimisation process. In this paper, FOLA is compared with two popularly used particle swarm optimisers and four newly-proposed optimisers, on nine complex multi-modal benchmark functions. The experimental results have shown that in comparison with the other optimisers, FOLA offers better performance for most of the benchmark functions, in terms of its convergence rate and accuracy, and it uses much less computation time to obtain accurate solutions, especially for high-dimensional functions. In order to explore the FOLAs potential for applications, it is also applied to solve an optimal power flow problem of power systems. FOLA is able to minimise the fuel cost and enhance the voltage stability of the power system more efficiently in comparison with the other algorithms.

Identification of Weak Areas of Network Based on Exposure to Voltage Sags—Part II: Assessment of Network Performance Using Sag Severity Index

This paper presents a new stochastic approach to the comprehensive assessment of the impact of voltage sags on large-scale power networks. The approach takes into account the stochastic nature of power system operation, including load variation, uncertainty of fault clearing time by protection relays, fault rates of network components, and the variation/uncertainty in equipment sensitivity to voltage sags. A new duration zone division method is used to derive sag duration and occurrence frequency based on the stochastic distribution of clearing time required by specific protection systems. The adopted probabilistic representation facilitates estimation of network performance based on a single-event characteristic, sag severity index (SSI), developed in the companion paper (Part I). Based on SSI, a new single-site index with respect to voltage sags (bus performance index BPIS) is developed to comprehensively represent the overall bus performance with respect to voltage sags. The index is used to identify the areas of the network, called weak areas of the network in this paper, containing buses that are most exposed to potentially disruptive voltage sags. The application, robustness, and sensitivity of BPIS are thoroughly analyzed and discussed in the paper.

Multi-objective optimization by learning automata

This paper presents a novel method of multi-objective optimization by learning automata (MOLA) to solve complex multi-objective optimization problems. MOLA consists of multiple automata which perform sequential search in the solution domain. Each automaton undertakes dimensional search in the selected dimension of the solution domain, and each dimension is divided into a certain number of cells. Each automaton performs a continuous search action, instead of discrete actions, within cells. The merits of MOLA have been demonstrated, in comparison with a multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm II (NSGA-II), on eleven multi-objective benchmark functions and an optimal problem in the midwestern American electric power system which is integrated with wind power, respectively. The simulation results have shown that MOLA can obtain more accurate and evenly distributed Pareto fronts, in comparison with MOEA/D and NSGA-II.

Identification of Weak Areas of Power Network Based on Exposure to Voltage Sags—Part I: Development of Sag Severity Index for Single-Event Characterization

This paper presents a new single-event characteristic, sag severity index (SSI), derived with respect to equipment sensitivity to voltage sags. It is more inclusive of associated uncertainties/variation in equipment response to voltage sags than existing single-event characteristics. By changing parameter settings, the index appropriately accounts for sag duration and adequately addresses the variation in equipment sensitivity. The value of the index changes continuously at the joining regions of different sag severity levels and reflects realistically the sensitivity trend of equipment embedded in voltage tolerance curves. The properties of the SSI are analyzed and discussed through comparison with characteristics of five previously reported single-event indices including four numerical indices and one fuzzy voltage sag index.

A novel Genetic Particle-Pair Optimizer for Vector Quantization in image coding

This paper presents a novel genetic particle-pair optimizer (GPPO) for vector quantization of image coding. GPPO only applies a particle-pair that consists of two particles, which contributes to the relief of huge computation load in most existing vector quantization algorithms. GPPO combines the advantage both in genetic algorithms and particle swarm optimization, due to the use of genetic operators and particle operators at each generation. Experimental results have demonstrated that the quality of the codebook design optimized by GPPO is better than that optimized respectively by fuzzy K-means (FKM), fuzzy reinforcement learning vector quantization (FRLVQ, improved FRLVQ which uses fuzzy vector quantization (FVQ as post-process, called FRLVQ-FVQ, and particle-pair optimizer (PPO). GPPO provides a satisfactory solution to vector quantization, and shows a steady trend of improvement in the quality of codebook design. The dependence of the final codebook on the selection of the initial codebook is also reduced.

Requantization codebook design using particle-pair optimizer

A new algorithm of optimal requantization codebook design using particle-pair optimizer (PPO) is proposed to provide an effective way for image transmission over multi-speed communication system with minimal transmission delay. PPO is used for optimal codebook design in the first and second quantization respectively. In the second quantization, global distortion is used as the fitness value instead of second quantization distortion. Simulation results demonstrated that the proposed algorithm is able to achieve higher PSNR value with less transmission delay in comparison with conventional codebook optimization strategies.

A Novel Optimizer Based on Particle Swarm Optimizer and LBG for Vector Quantization In Image Coding

This paper presents an optimizer based on particle swarm optimization and LBG (PSO-LBG) for vector quantization in image coding. Three swarms, including two initial swarms and one elitist swarm whose particles are selected from two initial swarms respectively, are applied to find the global optimum. At each iteration of a swarms updating process, particles perform the basic operations of PSO, but with smaller parameter values and population size compared with conventional PSO, followed by the well-known vector quantizer, i.e. LBG algorithm. Experimental results have demonstrated that the quality of codebook design using this optimizer is much better than that of fuzzy k-means (FKM), fuzzy reinforcement learning vector quantization (FRLVQ) and FRLVQ as the pre-process of fuzzy vector quantization (FRLVQ-FVQ) consistently with shorter computation time and faster convergence rate. The final codevectors are scattered reasonably and the dependence of the final optimum codebook on the selection of the initial codebook is reduced effectively.

Global assessment of power quality performance of networks using the analytic hierarchy process model

Power Quality is one of the critical issues when operating contemporary distribution networks. The increased interest in PQ is due to the increased employment of sensitive equipment loads and renewable generation technologies. Currently, the evaluation of PQ network performance is based on evaluating different phenomena separately, and a standardised way to evaluate the PQ as a whole for a bus or a network is yet to be applied or widely accepted. The paper presents a methodology for combining the performances of different PQ phenomena and expressing the PQ performance of a bus using a single index. The proposed methodology adopts an Analytic Hierarchy Process (AHP) model to combine the harmonics, unbalance and voltage sag performances in one proposed index; i.e. Compound Bus PQ Index (CBPQI). The separate and cumulative PQ performances of a 295-bus generic distribution network were evaluated, compared and demonstrated using heat maps.