David Cornforth

Memetic algorithms for solving job-shop scheduling problems

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. In this paper, we developed a memetic algorithm (MA) for solving JSSPs. Three priority rules were designed, namely partial re-ordering, gap reduction and restricted swapping, and used as local search techniques in our MA. We have solved 40 benchmark problems and compared the results obtained with a number of established algorithms in the literature. The experimental results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time.

Arctan Power–Frequency Droop for Improved Microgrid Stability

The term microgrid is usually reserved for a modest sized, local distributed generation network that will largely operate standalone (i.e., without a grid connection.) The most common power flow control method utilized in a standalone microgrid is a technique known as power-frequency droop. This paper introduces the concept of utilizing an arctan function for the power-frequency droop profile. The use of this arctan function improves the small signal stability of the two-inverter microgrid, provides natural frequency bounding, and is flexible in its application. SABER simulations are performed to obtain the operating points about which the system is linearized for the stability analysis. Experimental results obtained from a dSPACE-controlled, low-voltage, two-inverter hardware system are presented to verify the theoretical and simulation results.

Localized genetic algorithm for vehicle routing problem with time windows

This paper introduces the Localized Optimization Framework (LOF). This framework is an iterative procedure between two phases, Optimization and De-optimization. Optimization is done on the problem parts rather than the problem as a whole, while de-optimization is done on the whole problem. To test our hypothesis, we have chosen a genetic algorithm as an optimization methodology and Vehicle Routing Problem with Time Windows (VRPTW) as a domain space. We call this new scheme the Localized Genetic Algorithm (LGA). We demonstrate that the LGA is, on average, able to produce better solutions than most of the other heuristics on small scale problems of VRPTW. Furthermore the LGA has attained several new best solutions on popular datasets.

Evolution of Retinal Blood Vessel Segmentation Methodology Using Wavelet Transforms for Assessment of Diabetic Retinopathy

Diabetes is a chronic disease that affects the body’s capacity to regulate the amount of sugar in the blood. One in twenty Australians are affected by diabetes, but this figure is conservative, due to the presence of subclinical diabetes, where the disease is undiagnosed, yet is already damaging the body without manifesting substantial symptoms. This incidence rate is not confined to Australia, but is typical of developed nations, and even higher in developing nations. Excess sugar in the blood results in metabolites that cause vision loss, heart failure and stroke, and damage to peripheral blood vessels.These problems contribute significantly to the morbidity and mortality of the Australian population, so that any improvement in early diagnosis would therefore represent a significant gain. The incidence is projected to rise, and has already become a major epidemic [16].

Powering Through the Storm: Microgrids Operation for More Efficient Disaster Recovery

Disasters, whether natural or man-made, compromise the quality of life for all involved. In such situations, expeditious recovery activities are deemed imperative and irreplaceable for the restoration of normalcy. However, recovery activities rely heavily on the critical infrastructures that supply basic needs like electricity, water, information, and transportation. When disasters strike, it is likely that the critical infrastructures themselves are affected significantly, hampering efficient recovery processes, thus presenting a Catch-22 conundrum. In this article, we present examples from different parts of the world where distributed energy resources, organized in a microgrid, were used to provide reliable electricity supply in the wake of disasters, allowing recovery and rebuilding efforts to occur with relatively greater efficiency.

AMA: a new approach for solving constrained real-valued optimization problems

Memetic algorithms (MA) have recently been applied successfully to solve decision and optimization problems. However, selecting a suitable local search technique remains a critical issue of MA, as this significantly affects the performance of the algorithms. This paper presents a new agent based memetic algorithm (AMA) for solving constrained real-valued optimization problems, where the agents have the ability to independently select a suitable local search technique (LST) from our designed set. Each agent represents a candidate solution of the optimization problem and tries to improve its solution through co-operation with other agents. Evolutionary operators consist of only crossover and one of the self-adaptively selected LSTs. The performance of the proposed algorithm is tested on five new benchmark problems along with 13 existing well-known problems, and the experimental results show convincing performance.

Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem

The job-shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem. Many algorithms have been proposed for solving JSSP in the last few decades, including algorithms based on evolutionary techniques. However, there is room for improvement in solving medium to large scale problems effectively. In this paper, we present a hybrid genetic algorithm (HGA) that includes a heuristic job ordering with a genetic algorithm. We apply HGA to a number of benchmark problems. It is found that the algorithm is able to improve the solution obtained by traditional genetic algorithm.

Optimal allocation and sizing of capacitors to minimize the transmission line loss and to improve the voltage profile

To allocate and size capacitors in a distribution system, an optimization algorithm, called Discrete Particle Swarm Optimization (DPSO), is employed in this paper. The objective is to minimize the transmission line loss cost plus capacitors cost. During the optimization procedure, the bus voltage, the feeder current and the reactive power flowing back to the source side should be maintained within standard levels. To validate the proposed method, the semi-urban distribution system that is connected to bus 2 of the Roy Billinton Test System (RBTS) is used. This 37-bus distribution system has 22 loads being located in the secondary side of a distribution substation (33/11 kV). Reducing the transmission line loss in a standard system, in which the transmission line loss consists of only about 6.6 percent of total power, the capabilities of the proposed technique are seen to be validated.

Cardiac Autonomic Dysfunction in Type 2 Diabetes – Effect of Hyperglycemia and Disease Duration

Heart rate variability (HRV) is reduced in diabetes mellitus (DM) patients, suggesting dysfunction of cardiac autonomic regulation and an increased risk for cardiac events. The aim of this paper was to examine the associations of blood glucose level (BGL), glycated hemoglobin (HbA1c), and duration of diabetes with cardiac autonomic regulation assessed by HRV analysis. Resting electrocardiogram (ECG), recorded over 20 min in supine position, and clinical measurements of 189 healthy controls and 93 type 2 DM (T2DM) patients were analyzed. HRV was assessed using several time-domain, frequency-domain, and non-linear methods. HRV parameters showed a clear difference between healthy controls and T2DM patients. Hyperglycemia was associated with increase in mean heart rate and decrease in HRV, indicated by negative correlations of BGL and HbA1c with mean RR interval and most of the HRV parameters. Duration of diabetes was strongly associated with decrease in HRV, the most significant decrease in HRV was found within the first 5–10 years of the disease. In conclusion, elevated blood glucose levels have an unfavorable effect on cardiac autonomic function and this effect is pronounced in long-term T2DM patients. The most significant decrease in HRV related to diabetes and thus presence of autonomic neuropathy was observed within the first 5–10 years of disease progression.

Automated classification reveals morphological factors associated with dementia

Dementia is believed to be associated with changes in the physical structure of brain tissue, particularly in the pattern of small blood vessels. This study investigates one of the current research questions in the understanding of dementia, that is, whether there are differentiating factors in the structure of blood vessels of the cortex associated with different dementia subtypes and controls. Our approach is to use automated classification techniques to build predictive models based on fractal and non-fractal morphological descriptors, in order to label images of post mortem brain tissue with the appropriate pathology. Our goal is not to provide automated diagnosis, but to confirm or deny the presence of a relationship between morphological features and disease. The use of a variety of machine learning methods allows the exploration of the complex relationships that may exist. This study also addresses the choice of suitable features and the role of fractal analysis in medical image processing. The results suggest that there are differentiating factors, but these are difficult to detect, and vary between different areas of the cortex. Features derived from multi-fractal analysis showed more promise in this application than the non-fractal features we studied.