Artificial algae algorithm (AAA) for nonlinear global optimization

Uymaz, Sait Ali; Tezel, Gülay; Yel, Esra

doi:10.1016/j.asoc.2015.03.003

Cited by 227 publications

(99 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this section, the performance evaluation of the proposed method for enhancing CBIR is presented. In order to test the effectiveness of the proposed algorithm, the traditional k‐means, k‐nearest neighbors (KNN) methods and other approaches based on biologically inspired algorithms designed for CBIR, including the GA, PSO, BA, and artificial algae algorithm (AAA), were used for comparison with the CBIR results that were obtained from a number of different images. The experiment was conducted on the standard image dataset obtained from the Flickr Material Database .…”

Section: Experimental Settings and Resultsmentioning

confidence: 99%

Artificial bee colony algorithm for content‐based image retrieval

Banharnsakun

2020

Computational Intelligence

View full text Add to dashboard Cite

Image retrieval is the process of searching for digital images from a large database. There exist two distinctive research groups, which employ the content‐based and description‐based approaches, respectively. However, research in the content‐based domain is currently dominant in the field, while the other approaches are not as widely utilized. Although there are a number of different techniques that are available for image retrieval, the development of more effective methods is still necessary. In recent years, previous research has shown that biologically inspired metaheuristic algorithms have great potential for use in solving problems in many science and engineering domains. The artificial bee colony (ABC) algorithm is one of the more promising biologically inspired metaheuristic approaches used to find optimal solutions as it has the advantages of convenient implementation and efficient performance. In this article, a new efficient method based on a combination of the gray‐level cooccurrence matrix (GLCM) with the ABC, referred to as “GLCM‐ABC,” is proposed for use in content‐based image retrieval (CBIR). The experimental results demonstrate that the proposed approach works well for CBIR and can classify specific types of material surfaces in images with a reasonably high level of accuracy as well as outperform other existing algorithms.

show abstract

Section: Experimental Settings and Resultsmentioning

confidence: 99%

Artificial bee colony algorithm for content‐based image retrieval

Banharnsakun

2020

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Burada, , i. alg kolonisinin t anındaki açlık seviyesi, enAç t ise t anındaki en yüksek açlık seviyesine sahip alg kolonisi ve rand ise 0 ile 1 arasında rasgele üretilen gerçek değerli bir sayıdır [20]. ( ) ise i. alg kolonisinin t anındaki sürtünme katsayısıdır [20]. Algoritmaya ait sözde kod Şekil 3'te verilmiştir.…”

Section: Adaptasyon (Adaptation)unclassified

A Study on the Performance of Artificial Alg Algorithm in Design Optimization Problems: Compressing Spring Example

Atila

Dörterler

Şahin

2018

Bilişim Teknolojileri Dergisi

View full text Add to dashboard Cite

Özet-Makine elemanlarının optimum tasarımı mühendislikte yaygın olarak çalışılan bir araştırma konusudur. Basınç yaylarının minimum ağırlığa veya hacme göre tasarımını bu alanda en çok çalışılan problemlerden birisidir. Bu problem ayrıca optimizasyon yöntemleri için değerlendirme problemi olarak kullanılmaktadır. Yapay Alg Algoritması (YAA) bir optimizasyon yöntemidir ve besin üretmek için ihtiyaç duydukları maddelere erişmek üzere ortam şartlarına uyumda doğal bir yeteneğe sahip alglerin davranışlarından esinlenmiştir. Bu çalışmada, basınç yaylarının minimum hacme göre tasarımı YAA ile optimize edilmiştir ve YAA'nın problem üzerindeki başarımı incelenmiştir. YAA'nın başarımı daha önceki çalışmalarda probleme uygulanmış optimizasyon yöntemleri ile karşılaştırılmıştır. Deneysel çalışmalar YAA'nın tasarım optimizasyon problemini tutarlı ve düşük yakınsama oranıyla birlikte başarıyla çözme yeteneğinin olduğunu göstermiştir.

show abstract

“…[16] UPFC HICA-PS Voltage Collapse Prevention [17] UPFC ABC, GSA Improve Dynamic Stability of the System [18] SVC, TCSC RGA To improve Available Transfer Capability (ATC) [19] STATCOM Fuzzy, GA Voltage Stability Enhancement [20] UPFC BAT Search Algorithm Minimize real power losses in power system [21] DSTATCOM IA Reduce Power loss and improve voltage profile [22] UPFC NSGA-II, CMAES Increase loadability and total minimize cost of the system [23] UPFC FA, CS Enhance dynamic stability of the system [24] UPFC TS, EPSO Improve power transmission capability Recently, a novel, robust and intelligence optimization technique "Artificial Algae Algorithm (AAA)" has been developed from the living behaviors of microalgae [25]. For the author's knowledge, the application of the AAA concretely for ideal location of FACTS devices (UPFC) has not tested yet in the research publications.…”

Section: Ref Facts Devices Techniques/algorithms Objectivesmentioning

confidence: 99%

“…AAA is a novel biologically inspired meta-heuristic optimization algorithm first proposed by [25]. It was established from the motivation of lifestyle of microalgae.…”

Section: Overview Of Aaamentioning

confidence: 99%

New Approach for Optimal Location and Parameters Setting of UPFC for Enhancing Power Systems Stability under Contingency Analysis

Zahid

Chen

et al. 2017

Energies

View full text Add to dashboard Cite

Operation of power system within specified limits of voltage and frequency are the major concerns in power system stability studies. As power system is always prone to disturbances, which consequently affect the voltage instability and optimal power flow, and therefore risks the power systems stability and security. In this paper, a novel technique based on the "Artificial Algae Algorithm" (AAA) is introduced, to identify the optimal location and the parameters setting of Unified Power Flow Controller (UPFC) under N-1 contingency criterion. In the first part, we have carried out a contingency operation and ranking process for the most parlous lines outage contingencies while taking the transmission lines overloading (NOLL) and voltage violation of buses (NVVB) as a performance parameter (PP = NOLL + NVVB). As UPFC possesses too much prohibitive cost and larger size, its optimal location and size must be identified before the actual deployment. In the second part, we have applied a novel AAA technique to identify the optimal location and parameters setting of UPFC under the discovered contingencies. The simulations have been executed on IEEE 14 bus and 30 bus networks. The results reveals that the location of UPFC is significantly optimized using AAA technique, which has improved the stability and security of the power system by curtailing the overloaded transmission lines and limiting the voltage violations of buses.

show abstract

Artificial algae algorithm (AAA) for nonlinear global optimization

Cited by 227 publications

References 19 publications

Artificial bee colony algorithm for content‐based image retrieval

Artificial bee colony algorithm for content‐based image retrieval

A Study on the Performance of Artificial Alg Algorithm in Design Optimization Problems: Compressing Spring Example

New Approach for Optimal Location and Parameters Setting of UPFC for Enhancing Power Systems Stability under Contingency Analysis

Contact Info

Product

Resources

About