GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

Wind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind farm layout design problem has traditionally been solved using nature-inspired algorithms. An important issue in nature-inspired algorithms is the termination condition, which governs the execution time of the algorithm. To optimize the execution time, appropriate termination conditions should be employed. This study proposes the concept of a rewarding mechanism to achieve optimization in termination conditions while maintaining the solution quality. The proposed rewarding mechanism, adopted from the stochastic evolution algorithm, is incorporated into a genetic algorithm. The proposed genetic algorithm with the rewarding mechanism (GARM) is empirically tested using real data from a potential wind farm site with different rewarding iterations

Wind and wind power characteristics of the eastern and southern coastal and northern inland regions, South Africa

The objective of this work is to understand the fluctuating nature of wind speed characteristics on different time scales and to find the long-term annual trends of wind speed at different locations in South Africa. The hourly average mean wind speed values over a period of 20 years are used to achieve the set objective. Wind speed frequency, directional availability of maximum mean wind speed, total energy, annual energy yield and plant capacity factors are determined for seven locations situated both inland and along the coast of South Africa. The highest mean wind speed (6.01 m/s) is obtained in Port Elizabeth and the lowest mean wind speed (3.86 m/s) is obtained in Bloemfontein. Wind speed increased with increasing latitudes at coastal sites (Cape Town, Durban, East London and Port Elizabeth), while the reverse trend was observed at inland locations (Bloemfontein, Johannesburg and Pretoria). Noticeable annual changes and relative wind speed values are found at coastal locations compared to inland sites. The energy pattern factor, also known as the cube factor, varied between a minimum of 1.489 in Pretoria and a maximum of 1.858 in Cape Town. Higher energy pattern factor (EPF) values correspond to sites with fair to good wind power potential. Finally, Cape Town, East London and Port Elizabeth are found to be good sites for wind power deployments based on the wind speed and power characteristics presented in this study.The Deanship of Scientific Research at King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.https://link.springer.com/journal/11356hj2023Mechanical and Aeronautical Engineerin

A multinational Delphi consensus to end the COVID-19 public health threat

Publisher Copyright: © 2022, The Author(s).Despite notable scientific and medical advances, broader political, socioeconomic and behavioural factors continue to undercut the response to the COVID-19 pandemic1,2. Here we convened, as part of this Delphi study, a diverse, multidisciplinary panel of 386 academic, health, non-governmental organization, government and other experts in COVID-19 response from 112 countries and territories to recommend specific actions to end this persistent global threat to public health. The panel developed a set of 41 consensus statements and 57 recommendations to governments, health systems, industry and other key stakeholders across six domains: communication; health systems; vaccination; prevention; treatment and care; and inequities. In the wake of nearly three years of fragmented global and national responses, it is instructive to note that three of the highest-ranked recommendations call for the adoption of whole-of-society and whole-of-government approaches1, while maintaining proven prevention measures using a vaccines-plus approach2 that employs a range of public health and financial support measures to complement vaccination. Other recommendations with at least 99% combined agreement advise governments and other stakeholders to improve communication, rebuild public trust and engage communities3 in the management of pandemic responses. The findings of the study, which have been further endorsed by 184 organizations globally, include points of unanimous agreement, as well as six recommendations with >5% disagreement, that provide health and social policy actions to address inadequacies in the pandemic response and help to bring this public health threat to an end.Peer reviewe

Optimization techniques for image restoration, neural networks, and heterogeneous supercomputer system design

Simulated annealing algorithms for optimization over continuous spaces come in two varieties: Markov chain annealing algorithm (MCAA) and gradient annealing algorithm (GAA). There is a large amount of theoretical analysis and practical methodology developed for MCAA. The practical methodology is closely linked to heuristic procedures developed in Monte Carlo simulation of physical systems, from which the MCAA was originally developed. It has been observed that MCAA is not a viable approach to high-dimensional problems with smooth cost function. Theoretical analysis has been developed for the GAA but no practical methodology exists in the literature. As GAA attempts to exploit smoothness by its use of derivatives, an appropriate implementation should outperform the MCAA for high-dimensional problems with smooth cost functions. In this thesis we propose a practical methodology for the GAA. This methodology is tested and compared with other annealing algorithms on a set of benchmark functions. Also, we use the GAA for the restoration of gray-level images corrupted by multiplicative noise, where we seek the maximum a posteriori probability (MAP) estimate of the original image given the degraded one. In this thesis we show that existing stepsize rules for gradient descent can skip over nearby minima, particularly if the minima are deep and have small regions of attraction. Skipping over nearby minima may be undesirable when a local search is used as part of a global optimization strategy. propose a conservative stepsize rule and prove that it has a linear rate of convergence. This stepsize rule depends on the maximum eigenvalue of the Hessian matrix, which is usually hard to compute. We propose two methods to estimate this value from function and gradient values. Using these methods we develop two adaptive stepsize rules which we use in conjunction with the gradient annealing algorithm and for training feedforward neural networks. Distributed Heterogeneous Supercomputing Systems (DHSS) have been suggested to achieve computational speedup for complex applications comprising numerous tasks with varying computational characteristics. DHSS are expected to outperform homogeneous supercomputing systems, whose performance can be degraded severely by an ill-matched segment of code. In this thesis we propose and evaluate a neural network approach for mapping tasks to a suite of heterogeneous supercomputers