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Design optimization and analysis of selected thermal devices using self-adaptive jaya algorithm

ENERGY CONVERSION AND MANAGEMENT 2017年第May期140卷 24-35页

作者： Rao, R. V. More, K. C. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

The present study explores the use of an improved jaya algorithm called self-adaptive jaya algorithm for optimal design of selected thermal devices viz;heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self adaptive jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Thermal devices Optimization jaya algorithm self-adaptive jaya algorithm

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Optimal design and analysis of mechanical draft cooling tower using improved jaya algorithm

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INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID 2017年 82卷 312-324页

作者： Rao, R. V. More, K. C. BITS Pilani Dept Mech Engn Dubai Campus Dubai 345055 U Arab Emirates SV Natl Inst Technol Dept Mech Engn Surat 395007 India

A cooling tower is an imperative component of industrial plants. The minimization of energy related expenses is critical for conservation of resources and energy savings. Hence, the present study explores the use of an improved jaya algorithm called self-adaptive jaya algorithm for optimal design of cooling tower from economic facets. In this work, six different examples are considered in the design optimization of mechanical draft cooling tower. Various researchers have attempted the same mathematical models by using different methods like Merkel method, Poppe method and artificial bee colony (ABC) algorithm. The results achieved by using the proposed self-adaptive jaya algorithm are compared with the results achieved by using the Merkel method, Poppe method, ABC algorithm and basic jaya algorithm. The proposed self-adaptive jaya algorithm determines the population size automatically and the user need not tune the population size. The proposed self-adaptive jaya algorithm is proved better as compared to the other optimization methods with respect to achieving the optimal value of the objective function at less computational effort. (C) 2017 Elsevier Ltd and IIR. All rights reserved.

关键词： Cooling tower Cost optimization Merkel method Poppe method Artificial bee colony algorithm jaya algorithm self-adaptive jaya algorithm

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