Development and applications of an intelligent crow search algorithm based on opposition based learning

2020 ◽  
Vol 99 ◽  
pp. 210-230 ◽  
Author(s):  
Shalini Shekhawat ◽  
Akash Saxena
Keyword(s):  
Search Algorithm ◽  
Opposition Based Learning

scholarly journals Atmospheric PM2.5 Prediction Using DeepAR Optimized by Sparrow Search Algorithm with Opposition-Based and Fitness-Based Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 894
Author(s):  
Feng Jiang ◽  
Xingyu Han ◽  
Wenya Zhang ◽  
Guici Chen
Keyword(s):  
Search Algorithm ◽  
Recurrent Network ◽  
Learning Method ◽  
Probabilistic Forecasting ◽  
Atmospheric Concentration ◽  
Opposition Based Learning ◽  
Hourly Data ◽  
Beijing China ◽  
Important Significance ◽  
Pm2.5 Concentration

There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition, the improved Sparrow Search Algorithm (FOSSA) is employed to optimize the initial weights of probabilistic forecasting model with autoregressive recurrent network (DeepAR). Then, the FOSSA–DeepAR learning method is utilized to achieve the point prediction and interval prediction of PM2.5 concentration in Beijing, China. The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model. Furthermore, hourly data of PM2.5 and O3 concentration in Taian of China, O3 concentration in Beijing, China are used to verify the effectiveness and robustness of the proposed FOSSA–DeepAR learning method. Finally, the empirical results illustrate that the proposed FOSSA–DeepAR learning model can achieve more efficient and accurate predictions in both interval and point prediction.

An improved sparrow search algorithm based on levy flight and opposition-based learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Danni Chen ◽  
JianDong Zhao ◽  
Peng Huang ◽  
Xiongna Deng ◽  
Tingting Lu
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Support Vector ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Opposition Based Learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

scholarly journals A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2160 ◽  
Author(s):  
Xiaomin Wu ◽  
Weihua Cao ◽  
Dianhong Wang ◽  
Min Ding
Keyword(s):  
Power Loss ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Inertial Mass ◽  
Real Time Scheduling ◽  
Opposition Based Learning ◽  
Hybrid Particle Swarm Optimization ◽  
Time Scheduling ◽  
Benchmark Test Functions ◽  
Energy Conversion Devices

With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

scholarly journals Active power loss reduction by opposition based kidney search algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 217
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Loss Reduction ◽  
Opposition Based Learning ◽  
Candidate Solution ◽  
Power Loss Reduction ◽  
Power Problem

In this work Opposition based Kidney Search Algorithm (OKS) is used to solve the optimal reactive power problem. Kidney search algorithm imitates the various sequences of functions done by biological kidney. Opposition based learning (OBL) stratagem is engaged to commence the algorithm. This is to make certain high-quality of preliminary population and to expand the exploration steps in case of stagnation of the most excellent solutions. Opposition based learning (OBL) is one of the influential optimization tools to boost the convergence speed of different optimization techniques. The thriving implementation of the OBL engages evaluation of opposite population and existing population in the similar generation to discover the superior candidate solution of a given reactive power problem.  Proposed Opposition based Kidney Search Algorithm (OKS) has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the proposed algorithm reduced the real power loss efficiently.

scholarly journals OBCHS: AN EFFECTIVE HARMONY SEARCH ALGORITHM WITH OPPOSITIONBASED CHAOS-ENHANCED INITIALIZATION FOR SOLVING UNCAPACITATED FACILITY LOCATION PROBLEMS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 307-311 ◽  
Author(s):  
A. A. Heidari ◽  
O. Kazemizade ◽  
R. A. Abbaspour
Keyword(s):  
Facility Location ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Location Problems ◽  
Facility Location Problems ◽  
Opposition Based Learning ◽  
Spatial Tasks

In this paper, a continuous harmony search (HS) approach is investigated for tackling the Uncapacitated Facility Location (UFL) task. This article proposes an efficient modified HS-based optimizer to improve the performance of HS on complex spatial tasks like UFL problems. For this aim, opposition-based learning (OBL) and chaotic patterns are utilized. The proposed technique is examined against several UFL benchmark challenges in specialized literature. Then, the modified HS is substantiated in detail and compared to the basic HS and some other methods. The results showed that new opposition-based chaotic HS (OBCHS) algorithm not only can exploit better solutions competently but it is able to outperform HS in solving UFL problems.

Harmony Search Algorithm with Opposition-Based Learning for Power System Economic Load Dispatch

2014 ◽  
Vol 1065-1069 ◽  
pp. 3434-3437
Author(s):  
Yi Ning Zhang
Keyword(s):  
Power System ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Opposition Based Learning ◽  
Learning Techniques ◽  
Performance Of Algorithm ◽  
Better Than

A harmony search algorithm with opposition-based learning techniques (HS-OBL) to solve power system economic load dispatch has been presented. The proposed algorithm integrates the opposition-based learning operation with the improvisation process to prevent the HS-OBL algorithm from being trapped into the local optimum effectively. Besides, a new adjusting strategy is designed to dynamic adjust pitch adjusting rate (PAR) and harmony memory consideration rate (HMCR), which is to further improve the performance of algorithm. The HS-OBL is employed to solve 6 units and 13 units power system, the numerical results indicate that the HS-OBL has perform much better than harmony search(HS) algorithm and other improved algorithms that reported in recent literature.

A novel Harmony Search algorithm embedded with metaheuristic Opposition Based Learning

2017 ◽  
Vol 32 (4) ◽  
pp. 3189-3199 ◽  
Author(s):  
Ritesh Sarkhel ◽  
Tithi Mitra Chowdhury ◽  
Mayuk Das ◽  
Nibaran Das ◽  
Mita Nasipuri
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Opposition Based Learning

scholarly journals Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Huang ◽  
Sung-Kwun Oh
Keyword(s):  
Fuzzy Inference ◽  
Search Algorithm ◽  
Fuzzy Model ◽  
Parametric Identification ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Fuzzy Models ◽  
Opposition Based Learning ◽  
Speed Up ◽  
Inference Systems

We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA). The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.

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