The idea of utilizing nature inspired algorithms to find near optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is the task matching problem in large heterogeneous distributed computing environments like Grids and Clouds. Researchers have explored Particle Swarm Optimization(PSO), which is branch of swarm intelligence, to find a near optimal solution for the task matching problem. In this work, I investigated the effectiveness of the smallest position value (SPV) technique in mapping the continuous version of the PSO algorithm to the task matching problem in a heterogeneous computing environment. The experimental evaluation demonstrated that the task matching generated by this technique will result in an imbalanced load distribution. In this work, I have therefore also designed a load-rebalance PSO heuristic (PSO-LR) that results in minimization of makespan and balanced utilization of the available compute nodes even in heterogeneous computing environments.

The idea of utilizing nature inspired algorithms to find near optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is the task matching problem in large heterogeneous distributed computing environments like Grids and Clouds. Researchers have explored Particle Swarm Optimization(PSO), which is branch of swarm intelligence, to find a near optimal solution for the task matching problem. In this work, I investigated the effectiveness of the smallest position value (SPV) technique in mapping the continuous version of the PSO algorithm to the task matching problem in a heterogeneous computing environment. The experimental evaluation demonstrated that the task matching generated by this technique will result in an imbalanced load distribution. In this work, I have therefore also designed a load-rebalance PSO heuristic (PSO-LR) that results in minimization of makespan and balanced utilization of the available compute nodes even in heterogeneous computing environments.

Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Unconstrained optimization problems can be formulated as a D-dimensional minimization problem as follows: Min f (x) x=[x1+x2+……..xD] where D is the number of the parameters to be optimized. subjected to: Gi(x) <=0, i=1…q Hj(x) =0, j=q+1,……m Xε [Xmin, Xmax]D, q is the number of inequality constraints and m-q is the number of equality constraints. The particle swarm optimizer (PSO) is a relatively new technique. Particle swarm optimizer (PSO), introduced by Kennedy and Eberhart in 1995, [1] emulates flocking behavior of birds to solve the optimization problems. / In this paper the concept of dynamic particle swarm optimization is introduced. The dynamic PSO is different from the existing PSO’s and some local version of PSO in terms of swarm size and topology. Experiment conducted for benchmark functions of single objective optimization problem, which shows the better performance rather the basic PSO. The paper also contains the comparative analysis for Simple PSO and Dynamic PSO which shows the better result for dynamic PSO rather than simple PSO.

Various techniques are developed for detecting skin cancer. However, the type of maligned skin cancer is still an open problem. The objective of this study is to diagnose melanoma through design and implementation of a computerized image analysis system. The dataset which is used with the proposed system is Hospital Pedro Hispano (PH²). The proposed system begins with preprocessing of images of skin cancer. Then, particle swarm optimization (PSO) is used for detecting the region of interest (ROI). After that, features extraction (geometric, color, and texture) is taken from (ROI). Lastly, features selection and classification are done using a support vector machine (SVM). Results showed that with a data set of 200 images, the sensitivity (SE) and the specificity (SP) reached 100% with a maximum processing time of 0.03 sec.

Indiana University-Purdue University Indianapolis (IUPUI) / Particle Swarm Optimization is a bio-inspired optimization technique used to approximately solve the non-deterministic polynomial (NP) problem of asset allocation in 3D space, frequency, antenna azimuth [1], and elevation orientation [1]. This research uses QT Data Visualization to display the PSO solutions, assets, transmitters in 3D space from the work done in [2]. Elevation and Imagery data was extracted from ARCGIS (a geographic information system (GIS) database) to add overlapping elevation and imagery data to that the 3D visualization displays proper topological data. The 3D environment range was improved and is now dynamic; giving the user appropriate coordinates based from the ARCGIS latitude and longitude ranges. The second part of the research improves the performance of the PSOs runtime, using OpenMP with CPU threading to parallelize the evaluation of the PSO by particle. Lastly, this implementation uses CPU multithreading with 4 threads to improve the performance of the PSO by 42% - 51% in comparison to running the PSO without CPU multithreading. The contributions provided allow for the PSO project to be more realistically simulate its use in the Electronic Warfare (EW) space, adding additional CPU multithreading implementation for further performance improvements.

Una de las mayores limitaciones para lograr el rendimiento y la calidad deseada de los cultivos en la mayoría de los sistemas agronómicos del mundo es la presencia de malezas. Para mitigar su propagación e influencia, en los últimos años se comenzó a implementar el Manejo Integrado de Malezas (MIM), que pretende evitar los efectos negativos resultantes del uso exclusivo de herbicidas como único mecanismo de control al combinar diferentes técnicas de prevención y control que también incluyen rotaciones de cultivos, prácticas mecánicas y medidas culturales. Motivados por esta realidad, en esta tesis se planteó, primeramente, extender las prestaciones de un modelo de simulación desarrollado en el ámbito del grupo de investigación. El objetivo del mismo fue estimar los efectos de diferentes estrategias de manejo sobre la dinámica demográfica de una maleza anual (Avena fatua L.) en competencia con cereales de invierno (trigo y cebada) en un plan de rotación multianual, y proporcionar suficiente detalle agronómico, económico y medioambiental para orientar la toma de decisiones. Adicionalmente, con el propósito de explorar sistemáticamente la gran cantidad de posibles estrategias de MIM que se pueden representar con el modelo desarrollado e identificar automáticamente aquellas que resultan más prometedoras, se implementó un optimizador que proporciona un conjunto de soluciones en la frontera de los objetivos de desempeño considerados. Este se basó en un algoritmo estocástico no-lineal por enjambre de partículas (PSO), al que se le incorporaron técnicas para el manejo de restricciones, de manipulación de variables binarias y de consideración de objetivos múltiples. Así, el optimizador desarrollado permite identificar los mejores esquemas de rotación de cultivos y de tratamientos para controlar la maleza teniendo en cuenta, simultáneamente, el beneficio económico y el impacto ambiental. La herramienta desarrollada se considera de potencial utilidad para guiar el complejo proceso de toma de decisiones de la actividad agrícola. / One of the greatest limitations to achieve the desired yields and quality of crops in most agronomic systems around the world is the presence of weeds. To prevent their spread and influence, in recent years Integrated Weed Management (IWM) practices began to be implemented, which aims at avoiding the negative effects of the sole use of herbicides as control mechanism by combining different prevention and control techniques that also include rotations, mechanical practices and cultural measures. Motivated by this reality, in this thesis it was firstly proposed to extend the features of a simulation model developed in our research group. It aimed at estimating the effects of different management strategies on the demographic dynamics of an annual weed (Avena fatua L.) in competition with winter cereals (wheat and barley) in a multi-year rotation plan, providing sufficient agronomic, economic and environmental detail to guide decision making. Additionally, in order to systematically explore the large number of possible IWM strategies that can be represented with the developed model and automatically identify the most promising ones, an optimizer that provides a set of solutions on the frontier of the considered performance objectives was implemented. It was based on a non-linear stochastic particle swarm algorithm (PSO), enhanced with techniques for constraint management, binary variables handling and multiple objectives consideration. In this way, the developed optimizer allows identifying the best crop rotation and treatment schemes for weeds control, simultaneously considering the economic benefit and the environmental impact. The developed tool is considered of potential utility to guide the complex decisionmaking process of the agricultural activity.

Financial derivatives play significant role in an investor's success. Financial option is one form of derivatives. Option pricing is one of the challenging and fundamental problems of computational finance. Due to highly volatile and dynamic market conditions, there are no closed form solutions available except for simple styles of options such as, European options. Due to the complex nature of the governing mathematics, several numerical approaches have been proposed in the past to price American style and other complex options approximately. Bio-inspired and nature-inspired algorithms have been considered for solving large, dynamic and complex scientific and engineering problems. These algorithms are inspired by techniques developed by the insect societies for their own survival. Nature-inspired algorithms, in particular, have gained prominence in real world optimization problems such as in mobile ad hoc networks. The option pricing problem fits very well into this category of problems due to the ad hoc nature of the market. Particle swarm optimization (PSO) is one of the novel global search algorithms based on a class of nature-inspired techniques known as swarm intelligence. In this research, we have designed a sequential PSO based option pricing algorithm using basic principles of PSO. The algorithm is applicable for both European and American options, and handles both constant and variable volatility. We show that our results for European options compare well with Black-Scholes-Merton formula. Since it is very important and critical to lock-in profit making opportunities in the real market, we have also designed and developed parallel algorithm to expedite the computing process. We evaluate the performance of our algorithm on a cluster of multicore machines that supports three different architectures: shared memory, distributed memory, and a hybrid architectures. We conclude that for a shared memory architecture or a hybrid architecture, one-to-one mapping of particles to processors is recommended for performance speedup. We get a speedup of 20 on a cluster of four nodes with 8 dual-core processors per node.

Financial derivatives play significant role in an investor's success. Financial option is one form of derivatives. Option pricing is one of the challenging and fundamental problems of computational finance. Due to highly volatile and dynamic market conditions, there are no closed form solutions available except for simple styles of options such as, European options. Due to the complex nature of the governing mathematics, several numerical approaches have been proposed in the past to price American style and other complex options approximately. Bio-inspired and nature-inspired algorithms have been considered for solving large, dynamic and complex scientific and engineering problems. These algorithms are inspired by techniques developed by the insect societies for their own survival. Nature-inspired algorithms, in particular, have gained prominence in real world optimization problems such as in mobile ad hoc networks. The option pricing problem fits very well into this category of problems due to the ad hoc nature of the market. Particle swarm optimization (PSO) is one of the novel global search algorithms based on a class of nature-inspired techniques known as swarm intelligence. In this research, we have designed a sequential PSO based option pricing algorithm using basic principles of PSO. The algorithm is applicable for both European and American options, and handles both constant and variable volatility. We show that our results for European options compare well with Black-Scholes-Merton formula. Since it is very important and critical to lock-in profit making opportunities in the real market, we have also designed and developed parallel algorithm to expedite the computing process. We evaluate the performance of our algorithm on a cluster of multicore machines that supports three different architectures: shared memory, distributed memory, and a hybrid architectures. We conclude that for a shared memory architecture or a hybrid architecture, one-to-one mapping of particles to processors is recommended for performance speedup. We get a speedup of 20 on a cluster of four nodes with 8 dual-core processors per node.

This thesis proposes a new hybrid particle swarm optimizer, which employs landscape estimation and the cooperative behavior of different particles to significantly improve the performance of the original algorithm. The landscape estimation is to explore the landscape of the function in order to predict whether the function is unimodal or multimodal. Then we can decide how to optimize the function accordingly. The cooperative behavior is achieved by using two swarms, in which one swarm explores only a single dimension at a time, and the other explores all dimensions simultaneously. Furthermore, we also propose a movement tracking-based strategy to adjust the maximal velocity of the particles. This strategy can control the exploration and exploitation abilities of the swarm efficiency. Finally, we testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other recent variants of the PSO. The results show that our approach outperforms other methods in most of the benchmark problems.

Asim M. Mazin, for the Master of Science degree in Electrical and Computer Engineering, presented on Mar 27, 2013, at Southern Illinois University Carbondale. TITLE: REDUCING THE PEAK TO AVERAGE POWER RATIO OF MIMO-OFDM USING PSO BASED PTS. MAJOR PROFESSOR: Dr. Garth V. Crosby, In this thesis we proposed PSO based PTS to accomplish the lowest Peak-to-Average Power Ratio of MIMO-OFDM system. We applied the PSO based PTS on each antenna of the system in order to find the optimal phase factors which is a straightforward method to get the minimum PAPR in such a system. The performance of PSO based PTS algorithm in MIMO-OFDM with a wide range of phase factor tends to give a high performance according to the simulation results. In addition, there is no need to increase the number of particles of the PSO algorithm to enhance the performance of the system, which keeps the complexity of finding the minimum PAPR reasonable.