Adaptive symbiotic organisms search technique for cost optimization of shell and tube heat exchanger

N.A.

Makale detayları

Adaptive symbiotic organisms search technique for cost optimization of shell and tube heat exchanger

Yazarlar:

N.A.

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Thermodynamics and Statistical Physics

Dergi:

Journal of Thermal Engineering Vol.10 Issue 4, pp.857-867

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001324083

pISSN:

2148-7847

Anahtar kelimeler:

Özet:

 Nature inspired meta heuristics like swarm intelligence SI, Artificial neural networks ANN, evolutionary computing EC etc. have been used by researchers to solve single and multi-objective optimization problems of different fields. This work uses a novel α-SOS Adaptive sym-biotic organisms search algorithm for cost optimization of shell and tube heat exchanger. This algorithm is implemented for cost optimization of two benchmark STHX problem which are used by several researchers. Validation of the results is presented by comparing the geometric, flow and operational parameters of the same design problems when solved using particle swarm optimization PSO, Alpha tuned elephant herding optimization technique α-EHO and Gravitation search algorithm GSA. Result indicates a 4.73 and 11.3 reduction in cost for both the case study respectively when compared to same problems solved using PSO. Although when comparing with α-EHO, results does not indicate any substantial difference. Furthermore, operational, and geometric dimensions are also calculated. This algorithm can be eventually applied to real world design engineering problems.

 Nature inspired meta heuristics like swarm intelligence SI, Artificial neural networks ANN, evolutionary computing EC etc. have been used by researchers to solve single and multi-objective optimization problems of different fields. This work uses a novel α-SOS Adaptive sym-biotic organisms search algorithm for cost optimization of shell and tube heat exchanger. This algorithm is implemented for cost optimization of two benchmark STHX problem which are used by several researchers. Validation of the results is presented by comparing the geometric, flow and operational parameters of the same design problems when solved using particle swarm optimization PSO, Alpha tuned elephant herding optimization technique α-EHO and Gravitation search algorithm GSA. Result indicates a 4.73 and 11.3 reduction in cost for both the case study respectively when compared to same problems solved using PSO. Although when comparing with α-EHO, results does not indicate any substantial difference. Furthermore, operational, and geometric dimensions are also calculated. This algorithm can be eventually applied to real world design engineering problems.

 Nature inspired meta heuristics like swarm intelligence SI, Artificial neural networks ANN, evolutionary computing EC etc. have been used by researchers to solve single and multi-objective optimization problems of different fields. This work uses a novel α-SOS Adaptive sym-biotic organisms search algorithm for cost optimization of shell and tube heat exchanger. This algorithm is implemented for cost optimization of two benchmark STHX problem which are used by several researchers. Validation of the results is presented by comparing the geometric, flow and operational parameters of the same design problems when solved using particle swarm optimization PSO, Alpha tuned elephant herding optimization technique α-EHO and Gravitation search algorithm GSA. Result indicates a 4.73 and 11.3 reduction in cost for both the case study respectively when compared to same problems solved using PSO. Although when comparing with α-EHO, results does not indicate any substantial difference. Furthermore, operational, and geometric dimensions are also calculated. This algorithm can be eventually applied to real world design engineering problems.

 Nature inspired meta heuristics like swarm intelligence SI, Artificial neural networks ANN, evolutionary computing EC etc. have been used by researchers to solve single and multi-objective optimization problems of different fields. This work uses a novel α-SOS Adaptive sym-biotic organisms search algorithm for cost optimization of shell and tube heat exchanger. This algorithm is implemented for cost optimization of two benchmark STHX problem which are used by several researchers. Validation of the results is presented by comparing the geometric, flow and operational parameters of the same design problems when solved using particle swarm optimization PSO, Alpha tuned elephant herding optimization technique α-EHO and Gravitation search algorithm GSA. Result indicates a 4.73 and 11.3 reduction in cost for both the case study respectively when compared to same problems solved using PSO. Although when comparing with α-EHO, results does not indicate any substantial difference. Furthermore, operational, and geometric dimensions are also calculated. This algorithm can be eventually applied to real world design engineering problems.

Makale detayları

Adaptive symbiotic organisms search technique for cost optimization of shell and tube heat exchanger

Yazarlar:

N.A.

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Thermodynamics and Statistical Physics

Dergi:

Journal of Thermal Engineering Vol.10 Issue 4, pp.857-867

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001324083

pISSN:

2148-7847

Adaptive SOS

Alpha EHO

Cost

GSA

Optimization

Nature Inspired Metaheuristics

STHX

Sayı

Makale detayları

Yazarlar: N.A.

Yayıncı: Yildiz Technical University

Ana Başlıklar: Thermodynamics and Statistical Physics

Dergi: Journal of Thermal Engineering Vol.10 Issue 4, pp.857-867

Belge Tipi: Makale

Yayın Tarihi: 7/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001324083

pISSN: 2148-7847

Sayı

Yazarlar:

N.A.

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Thermodynamics and Statistical Physics

Dergi:

Journal of Thermal Engineering Vol.10 Issue 4, pp.857-867

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001324083

pISSN:

2148-7847