An Effective Precision Enhancement Approach to Estimate Software Development Cost: Nature Inspired Way

In recent years, many researchers and practitioners have explored the possibility of estimating effort and cost using nature inspired algorithms. The purpose of this paper is to investigate the relevance of bacterial foraging optimization algorithm (BFOA) for optimizing the COCOMO model coefficients to estimate the software development time. The goal of this research is to minimize the fitness function value which is the measure of the deflection of estimated time from the real time taken in the software development. Results of the experimental study conducted shows that the proposed approach produces promising results in comparison to COCOMO model and other existing approaches listed in literature. Results show that COCOMO model and other existing approaches are less accurate in comparison to BFOA with MMRE as 0.16 and PRED(25) as 0.9. Thus BFOA can help software industry in predicting accurate and reliable values for planning and maintenance of software project

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Do bacteria thrive when the ocean acidifies? Results from an off-­shore mesocosm study

Marine bacteria are the main consumers of the freshly produced organic matter. In order to meet their carbon demand, bacteria release hydrolytic extracellular enzymes that break down large polymers into small usable subunits. Accordingly, rates of enzymatic hydrolysis have a high potential to affect bacterial organic matter recycling and carbon turnover in the ocean. Many of these enzymatic processes were shown to be pH sensitive in previous studies. Due to the continuous rise in atmospheric CO2 concentration, seawater pH is presently decreasing at a rate unprecedented during the last 300 million years with so-far unknown consequences for microbial physiology, organic matter cycling and marine biogeochemistry. We studied the effects of elevated seawater pCO2 on a natural plankton community during a large-scale mesocosm study in a Norwegian fjord. Nine 25m-long Kiel Off-Shore Mesocosms for Future Ocean Simulations (KOSMOS) were adjusted to different pCO2 levels ranging from ca. 280 to 3000 µatm by stepwise addition of CO2 saturated seawater. After CO2 addition, samples were taken every second day for 34 days. The first phytoplankton bloom developed around day 5. On day 14, inorganic nutrients were added to the enclosed, nutrient-poor waters to stimulate a second phytoplankton bloom, which occurred around day 20. Our results indicate that marine bacteria benefit directly and indirectly from decreasing seawater pH. During both phytoplankton blooms, more transparent exopolymer particles were formed in the high pCO2 mesocosms. The total and cell-specific activities of the protein-degrading enzyme leucine aminopeptidase were elevated under low pH conditions. The combination of enhanced enzymatic hydrolysis of organic matter and increased availability of gel particles as substrate supported higher bacterial abundance in the high pCO2 treatments. We conclude that ocean acidification has the potential to stimulate the bacterial community and facilitate the microbial recycling of freshly produced organic matter, thus strengthening the role of the microbial loop in the surface ocean

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Performance Analysis of Flexible A.C. Transmission System Devices for Stability Improvement of Power System

When large power systems are interconnected by relatively weak tie line, low-frequency oscillations are observed. Recent developments in power electronics have led to the development of the Flexible AC Transmission Systems (FACTS) devices in power systems. FACTS devices are capable of controlling the network condition in a very fast manner and this feature of FACTS can be exploited to improve the stability of a power system. To damp electromechanical oscillations in the power system, the supplementary controller can be applied with FACTS devices to increase the system damping. The supplementary controller is called damping controller. The damping controllers are designed to produce an electrical torque in phase with the speed deviation. The objective of this thesis is to develop some novel control techniques for the FACTS based damping controller design to enhance power system stability. Proper selection of optimization techniques plays an important role in for the stability enhancement of power system. In the present thesis Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gravitational search algorithm (GSA) along with their hybrid form have been applied and compared for a FACTS based damping controller design. Important conclusions have been drawn on the suitability of optimization technique. The areas of research achieved in this thesis have been divided into two parts: The aim of the first part is to develop the linearized model (Philip-Hefron model) of a single machine infinite bus power system installed with FACTS devices, such as Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC). Different Damping controller structures have been used and compared to mitigate the system damping by adding a component of additional damping torque proportional to speed change through the excitation system. The various soft-computing techniques have been applied in order to find the controller parameters. The recently developed Gravitational Search Algorithm (GSA) based SSSC damping controller, and a new hybrid Genetic Algorithm and Gravitational Search Algorithm (hGA-GSA) based UPFC damping controller seems to the most effective damping controller to mitigate the system oscillation. The aim of second part is to develop the Simulink based model (to over-come the problem associated with the linearized model) for an SMIB as well as the multi-machine power system. Coordinated design of PSS with various FACTS devices based damping controllers are carried out considering appropriate time delays due to sensor time constant and signal transmission delays in the design process. A hybrid Particle Swarm Optimization and Gravitational Search Algorithm (hPSO-GSA) technique is employed to optimally and coordinately tune the PSS and SSSC based controller parameters and has emerged as the most superior method of coordinated controller design considered for both single machine infinite bus power system as well as a multi-machine power system. Finally, the damping capabilities of SSSC based damping controllers are thoroughly investigated by considering a new derived modified signal known as Modified Local Input Signal which comprises both the local signal (speed deviation) and remote signal (line active power). Appropriate time delays due to sensor time constant and signal transmission delays are considered in the design process. The hybrid Particle Swarm Optimization and Gravitational Search Algorithm (hPSO-GSA) technique is used to tune the damping controller parameters. It is observed that the new modified local input signal based SSSC controller provides the best system performance compared to other alternatives considered for a single machine infinite bus power system and multi-machine power system

Intelligent simulation of coastal ecosystems

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto, Faculdade de Ciência e Tecnologia. Universidade Fernando Pessoa. 201