A modified sine cosine algorithm for improving wind plant energy production

This paper presents a Modified Sine Cosine Algorithm (M-SCA) to improve the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The two modifications employed to the original SCA are in terms of the updated step size gain and the updated design variable equation. Those modifications are expected to enhance the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-SCA is applied to maximize energy production of a row of ten turbines. The statistical performance analysis shows that the M-SCA provides the highest total energy production as compared to other existing methods

Performance Analysis of Tree Seed Algorithm for Small Dimension Optimization Functions

Tree-Seed Algorithm (TSA) simulates the growth of trees and seeds on a land. TSA is a method proposed to solve continuous optimization problems. Trees and seeds indicate possible solutions in the search space for optimization problems. Trees are planted in the ground at the beginning of the search and each tree produces several seeds during iterations. While the trees were selected randomly during seed formation, the tournament selection method was used and also hybridized by adding the C parameter, which is the acceleration coefficient calculated according to the size of the problem. In this study, continuous optimization problem has been solved by the hybrid method. First, the performance analyses of the five best known numerical benchmark functions have been done, in both TSA and hybrid method TSA with 2, 3, 4 and 5 dimensions, and 10-50 population numbers. After that, well-known algorithms in the literature like Particle Swarm Optimization (PSO), TSA, Artificial Bee Colony (ABC), Harmony Search (HS), as well as hybrid method TSA (HTSA) have been applied to twenty-four numerical benchmark functions and the performance analyses of algorithms have been done. Hopeful and comparable conclusions based on solution quality and robustness can be obtained with the hybrid method

Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.This research was supported by the Spanish Ministry of Science, Innovation and Universities and the Research State Agency under Grant RTI2018-098156-B-C54 cofinanced by FEDER funds and the Ministry of Science and Innovation and the Research State Agency under Grant PID2020-120213RB-I00 cofinanced by FEDER funds

A novel efficient energy optimization in smart urban buildings based on optimal demand side management

Data availability: The data used for this research and prepatation of this article can be accessed from Brunel University of London repository at: https://doi.org/10.17633/rd.brunel.26049436.v1.Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings.The research has been partially supported by the Faculty of Informatics and Management UHK excellence project “Methodological perspectives on modeling and simulation of hard and soft systems”