Application of artificial bee colony algorithm on surface wave data

Xiao, Song; Gu, Hanming; Tang, Li; Zhao, Sutao; Zhang, Xueqiang; Li, Lei; Huang, Jing

doi:10.1016/j.cageo.2015.07.010

Cited by 26 publications

(7 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on various aspects of ant foraging behavior, ACO was originally proposed for numerical problems, and was used to solve the optimization problem in many fields [60,61]. Currently, there are many studies on the AIS [62] and ABC algorithms [63,64]. These methods and models typically emulate the biological characteristics of response, memory, and learning to domain-specific problem-solving.…”

Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

View full text Add to dashboard Cite

Abstract:In this article, we conducted the evaluation of artificial intelligence research from 1990-2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular.

show abstract

Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

View full text Add to dashboard Cite

show abstract

“…When the selected food sources have been mined out, the employed bees become scout bees. Those scout bees randomly find out new food sources to replace the one which has been mined out [45,47,48] (12) where i = 1, 2, . .…”

Section: Artificial Bee Colonymentioning

confidence: 99%

“…. D.SN = CS/2 is the number of food sources is equal to the number of employed bees [48]. In addition, employed bees are equal to the onlooker bees.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

et al. 2019

View full text Add to dashboard Cite

Blade design of the horizontal axis wind turbine (HAWT) is an important parameter that determines the reliability and efficiency of a wind turbine. It is important to optimize the capture of the energy in the wind that can be correlated to the power coefficient ( C p ) of HAWT system. In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony (ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can give the maximum value of C p for HAWT. The parameters are tip speed ratio, blade radius, lift to drag ratio, solidity ratio, and chord length. The performance of these three algorithms in obtaining the optimal blade design based on the C p are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the C p of wind turbine blades for investigation of algorithm performance based on the coefficient determination (R2) and root mean square error (RMSE). The optimized blade design parameters are validated with experimental results from the National Renewable Energy Laboratory (NREL). It was found that the optimized blade design parameters were obtained using an ABC algorithm with the maximum value power coefficient higher than ACO and PSO. The predicted C p using ANFIS-ABC also outperformed the ANFIS-ACO and ANFIS-PSO. The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms.

show abstract

“…In modelling this behavior, if the qualification rate of a point is not improved after several repetition (of which is shown by the parameter 'limit'), this means that we are at an optimal local. Accordingly, the relevant point is removed and a new point is randomly produced [45,46]. For obtaining the optimization parameters using the ABC algorithm the following steps must be taken: 1-The system data and ABC algorithm parameters like the colony bees (number of employed bees + onlooker bees) (Np), the limit value and the number of cycles (max cycles) are specified.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Artificial Bee Colony Algorithm Based Approach for Power System Oscillation Damping with PSS and STATCOM

2019

International Journal of Research Studies in Electrical and Ele

View full text Add to dashboard Cite

This paper develops a design procedure for simultaneous coordinated designing of the static synchronous compensator controller parameters (STATCOM) and power system stabilizer (PSS) in multimachine power system. The artificial bee colony (ABC) algorithm is employed to search for optimal controllers parameters. ABC algorithm is a collective intelligence based on optimization algorithm inspired by the feeding behavior of bees in finding food. Rapid convergence and high precision are the capabilities of this algorithm which in this study to demonstrate its effectiveness and robustness, a two zone-four machines system is used and analyzed through non-linear simulation of time-domain and the results are compared with particle swarm optimization (PSO) algorithm. The optimization after a large turbulence shows that the coordinated design of the STATCOM and PSS controllers' parameters using the ABC algorithm considerably improves the system stability together with rapid damping of the system fluctuations as compared to the state without optimization. On the other hand, the optimization results using the ABC algorithm shows the superiority of this method over the PSO algorithm.

show abstract

Application of artificial bee colony algorithm on surface wave data

Cited by 26 publications

References 59 publications

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

Artificial Bee Colony Algorithm Based Approach for Power System Oscillation Damping with PSS and STATCOM

Contact Info

Product

Resources

About