Enhanced Tunicate Swarm Algorithm for Big Data Optimization

Emine Baş

Makale detayları

Enhanced Tunicate Swarm Algorithm for Big Data Optimization

Yazarlar:

Emine Baş

Yayıncı:

Sakarya University

Ana Başlıklar:

Software Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 2, pp.313-334

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001287188

pISSN:

2147-835X

Anahtar kelimeler:

Özet:

 Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm TSA, which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA ETSA was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes 10, 20, 30, 50, 100 and three different stopping criteria 300, 500, 1000. The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.

 Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm TSA, which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA ETSA was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes 10, 20, 30, 50, 100 and three different stopping criteria 300, 500, 1000. The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.

 Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm TSA, which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA ETSA was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes 10, 20, 30, 50, 100 and three different stopping criteria 300, 500, 1000. The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.

 Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm TSA, which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA ETSA was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes 10, 20, 30, 50, 100 and three different stopping criteria 300, 500, 1000. The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.

Makale detayları

Enhanced Tunicate Swarm Algorithm for Big Data Optimization

Yazarlar:

Emine Baş

Yayıncı:

Sakarya University

Ana Başlıklar:

Software Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 2, pp.313-334

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001287188

pISSN:

2147-835X

TSA

tunicate

meta-heuristic

big data

Sayı

Makale detayları

Enhanced Tunicate Swarm Algorithm for Big Data Optimization

Yazarlar: Emine Baş

Yayıncı: Sakarya University

Ana Başlıklar: Software Engineering

Dergi: Sakarya University Journal of Science Vol.27 Issue 2, pp.313-334

Belge Tipi: Makale

Yayın Tarihi: 4/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001287188

pISSN: 2147-835X

TSA

tunicate

meta-heuristic

big data

Sayı

Yazarlar:

Emine Baş

Yayıncı:

Sakarya University

Ana Başlıklar:

Software Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 2, pp.313-334

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001287188

pISSN:

2147-835X