A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.

Optimal voltage support mechanism in distribution networks

Optimal scheduling of voltage regulators (VRs), fixed and switched capacitors and voltage on customer side of transformer (VCT) along with the optimal allocaton of VRs and capacitors are performed using a hybrid optimisation method based on discrete particle swarm optimisation and genetic algorithm. Direct optimisation of the tap position is not appropriate since in general the high voltage (HV) side voltage is not known. Therefore, the tap setting can be determined give the optimal VCT once the HV side voltage is known. The objective function is composed of the distribution line loss cost, the peak power loss cost and capacitors' and VRs' capital, operation and maintenance costs. The constraints are limits on bus voltage and feeder current along with VR taps. The bus voltage should be maintained within the standard level and the feeder current should not exceed the feeder-rated current. The taps are to adjust the output voltage of VRs between 90 and 110% of their input voltages. For validation of the proposed method, the 18-bus IEEE system is used. The results are compared with prior publications to illustrate the benefit of the employed technique. The results also show that the lowest cost planning for voltage profile will be achieved if a combination of capacitors, VRs and VCTs is considered.

Voltage balance improvement in urban low voltage distribution networks

This project is a step forward in developing effective methods to mitigate voltage unbalance in urban residential networks. The method is proposed to reduce energy losses and improve quality of service in strongly unbalanced low-voltage networks. The method is based on phase swapping as well as optimal placement and sizing of Distribution Static Synchronous Compensator (D-STATCOM) using a Particle Swarm Optimisation method.

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains

We present the theoretical foundations for the multiple rendezvous problem involving design of local control strategies that enable groups of visibility-limited mobile agents to split into subgroups, exhibit simultaneous taxis behavior towards, and eventually rendezvous at, multiple unknown locations of interest. The theoretical results are proved under certain restricted set of assumptions. The algorithm used to solve the above problem is based on a glowworm swarm optimization (GSO) technique, developed earlier, that finds multiple optima of multimodal objective functions. The significant difference between our work and most earlier approaches to agreement problems is the use of a virtual local-decision domain by the agents in order to compute their movements. The range of the virtual domain is adaptive in nature and is bounded above by the maximum sensor/visibility range of the agent. We introduce a new decision domain update rule that enhances the rate of convergence by a factor of approximately two. We use some illustrative simulations to support the algorithmic correctness and theoretical findings of the paper.

Integrated Reservoir Operation Modeling for Irrigation Systems - A Multi-Objective Approach

An integrated reservoir operation model is presented for developing effective operational policies for irrigation water management. In arid and semi-arid climates, owing to dynamic changes in the hydroclimatic conditions within a season, the fixed cropping pattern with conventional operating policies, may have considerable impact on the performance of the irrigation system and may affect the economics of the farming community. For optimal allocation of irrigation water in a season, development of effective mathematical models may guide the water managers in proper decision making and consequently help in reducing the adverse effects of water shortage and crop failure problems. This paper presents a multi-objective integrated reservoir operation model for multi-crop irrigation system. To solve the multi-objective model, a recent swarm intelligence technique, namely elitist-mutated multi-objective particle swarm optimisation (EM-MOPSO) has been used and applied to a case study in India. The method evolves effective strategies for irrigation crop planning and operation policies for a reservoir system, and thereby helps farming community in improving crop benefits and water resource usage in the reservoir command area.

Simultaneous source localization and boundary mapping for contaminants

This paper addresses the problem of localizing the sources of contaminants spread in the environment, and mapping the boundary of the affected region using an innovative swarm intelligence based technique. Unlike most work in this area the algorithm is capable of localizing multiple sources simultaneously while also mapping the boundary of the contaminant spread. At the same time the algorithm is suitable for implementation using a mobile robotic sensor network. Two types of agents, called the source localization agents (or S-agents) and boundary mapping agents (or B-agents) are used for this purpose. The paper uses the basic glowworm swarm optimization (GSO) algorithm, which has been used only for multiple signal source localization, and modifies it considerably to make it suitable for both these tasks. This requires the definition of new behaviour patterns for the agents based on their terminal performance as well as interactions between them that helps the swarm to split into subgroups easily and identify contaminant sources as well as spread along the boundary to map its full length. Simulations results are given to demonstrate the efficacy of the algorithm.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Long-Term Economic Model for Allocation of FACTS Devices in Restructured Power System Integrated Wind Generation

This study proposes an approach to optimally allocate multiple types of flexible AC transmission system (FACTS) devices in market-based power systems with wind generation. The main objective is to maximise profit by minimising device investment cost, and the system's operating cost considering both normal conditions and possible contingencies. The proposed method accurately evaluates the long-term costs and benefits gained by FACTS devices (FDs) installation to solve a large-scale optimisation problem. The objective implies maximising social welfare as well as minimising compensations paid for generation re-scheduling and load shedding. Many technical operation constraints and uncertainties are included in problem formulation. The overall problem is solved using both particle swarm optimisations for attaining optimal FDs allocation as main problem and optimal power flow as sub-optimisation problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

Opportunities for improvements in flocking models

Flocking is the capacity of coherent movement between multiple animals, including birds. Prominent research into flocking is presented. Particle Swarm Optimisation (PSO) has been the prominent result from research into flocking. It is considered that opportunities for further research in flocking exist. With the potential for automated traffic systems, it is concluded that flocking should be reinvestigated for this purpose.

Instance generators and test suites for the multiobjective quadratic assignment problem

We describe, and make publicly available, two problem instance generators for a multiobjective version of the well-known quadratic assignment problem (QAP). The generators allow a number of instance parameters to be set, including those controlling epistasis and inter-objective correlations. Based on these generators, several initial test suites are provided and described. For each test instance we measure some global properties and, for the smallest ones, make some initial observations of the Pareto optimal sets/fronts. Our purpose in providing these tools is to facilitate the ongoing study of problem structure in multiobjective (combinatorial) optimization, and its effects on search landscape and algorithm performance.