Novel particle swarm optimization algorithms with applications in power systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOptimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods by analysing the experimental results. Two novel PSO variants are introduced in this thesis. The first one is named as N State Markov Jumping Particle Swarm Optimization, which is based on the stochastic technique and Markov chain in updating the particle velocity. We have named the second variant as N State Switching Particle Swarm Optimization, which is based on the evolutionary factor information for updating the velocity. The proposed algorithms are then applied to some widely used mathematical benchmark functions. The statistical results of 30 independent trails illustrate the robustness and accuracy of the proposed algorithms for most of the benchmark functions. The better results in terms of mean minimum evaluation errors and the shortest computation time are illustrated. In order to verify the satisfactory performance and robustness of the proposed algorithms, we have further formulated some basic applications in power system operations. The first application is about the static Economic Load Dispatch and the second application is on the Dynamic Economic Load Dispatch. These are highly complex and non-linear problems of power system operations consisting of various systems and generator constraints. Basically, in the static Economic Load Dispatch, a single load is considered for calculating the cost function. In contrast, the Dynamic Economic Load Dispatch changes the load demand for the cost function dynamically with time. In such a challenging and complex environment the proposed algorithms can be applied. The empirical results obtained by applying both of the proposed methods have substantiated their adaptability and robustness into the real-world environment. It is shown in the numerical results that the proposed algorithms are robust and accurate as compared to the other algorithms. The proposed algorithms have produced consistent best values for their objectives, where satisfying all constraints with zero penalty

A migration aware scheduling technique for real-time aperiodic tasks over multiprocessor systems

Multi-processor systems consist of more than one processor and are mostly used for computationally intensive applications. Real-time systems are those systems that require completing execution of tasks within a pre-defined deadline. Traditionally, multiprocessor systems are given attention in periodic models, where tasks are executed at regular intervals of time. Gradually, as maturity in a multiprocessor design had increased; their usage has become very common for real-time systems to execute both periodic and aperiodic tasks. As the priority of an aperiodic task is usually but not essentially greater than the priority of a periodic task, they must be completed within the deadline. There is a lot of research works on multiprocessor systems with scheduling of periodic tasks, but the task scheduling is relatively remained unexplored for a mixed workload of both periodic and aperiodic tasks. Moreover, higher energy consumption is another main issue in multiprocessor systems. Although it could be reduced by using the energy-aware scheduling technique, the response time of aperiodic tasks still increases. In the literature, various techniques were suggested to decrease the energy consumption of these systems. However, the study on reducing the response time of aperiodic tasks is limited. In this paper, we propose a scheduling technique that: 1) executes aperiodic tasks at full speed and migrates periodic tasks to other processors if their deadline is earlier than aperiodic tasks-reduces the response time and 2) executes aperiodic tasks with lower speed by identifying appropriate processor speed without affecting the response time-reduces energy consumption. Through simulations, we demonstrate the efficiency of the proposed algorithm and we show that our algorithm also outperforms the well-known total bandwidth server algorithm

A heuristic approach for finding similarity indexes of multivariate data sets

Multivariate data sets (MDSs), with enormous size and certain ratio of noise/outliers, are generated routinely in various application domains. A major issue, tightly coupled with these MDSs, is how to compute their similarity indexes with available resources in presence of noise/outliers - which is addressed with the development of both classical and non-metric based approaches. However, classical techniques are sensitive to outliers and most of the non-classical approaches are either problem/application specific or overlay complex. Therefore, the development of an efficient and reliable algorithm for MDSs, with minimum time and space complexity, is highly encouraged by the research community. In this paper, a non-metric based similarity measure algorithm, for MDSs, is presented that solves the aforementioned issues, particularly, noise and computational time, successfully. This technique finds the similarity indexes of noisy MDSs, of both equal and variable sizes, through utilizing minimum possible resources i.e., space and time. Experiments were conducted with both benchmark and real time MDSs for evaluating the proposed algorithm`s performance against its rival algorithms, which are traditional dynamic programming based and sequential similarity measure algorithms. Experimental results show that the proposed scheme performs exceptionally well, in terms of time and space, than its counterpart algorithms and effectively tolerates a considerable portion of noisy data

A Short Overview of Service Discovery Protocols for MANETS

MANETs (Mobile Ad Hoc Networks) are infrastructure-less, temporary wireless networks, consisting of several stations. No specific topology is defined in MANETs. MANETs have various applications in computer networks, such as providing communication in a domicile lacking network groundwork and proper infrastructure. In a MANET, a data packet may crisscross numerous hops until reaching its target location, making it exposed to various network attacks. The packets in a MANET are exposed to various packet dropping attacks. Mobility is there but security is the main issue still. The technology used for finding, advertising services to other nodes in the network is Service Discovery. Different Protocols are available for Service Discovery. Our focus in this paper will be on Service Discovery, available Service Discovery Architecture & their modes of operation, some proposed protocols. We will discuss Mobile Service Discovery Protocol (MSDP), which have steady performance & reduced massage overhead. Currently there is a diversity of service discovery protocols, most important Jini, SLP, Salutation, MSDP, Chord and UPnP. Bluetooth has also a slightly modest service discovery protocol. We have compared these tactics and listed their benefits and weaknesses

PerficientCloudSim: a tool to simulate large-scale computation in heterogeneous clouds

The major reason for using a simulator, instead of a real test-bed, is to enable repeatable evaluation of large-scale cloud systems. CloudSim, the most widely used simulator, enables users to implement resource provisioning, and management policies. However, CloudSim does not provide support for: (i) interactive online services; (ii) platform heterogeneities; (iii) virtual machine migration modelling; and (iv) other essential models to abstract a real datacenter. This paper describes modifications needed in the classical CloudSim to support realistic experimentations that closely match experimental outcomes in a real system. We extend, and partially re-factor CloudSim to “PerficientCloudSim” in order to provide support for large-scale computation over heterogeneous resources. In the classical CloudSim, we add several classes for workload performance variations due to: (a) CPU heterogeneities; (b) resource contention; and (c) service migration. Through plausible assumptions, our empirical evaluation, using real workload traces from Google and Microsoft Azure clusters, demonstrates that “PerficientCloudSim” can reasonably simulate large-scale heterogeneous datacenters in respect of resource allocation and migration policies, resource contention, and platform heterogeneities. We discuss statistical methods to measure the accuracy of the simulated outcomes