Design of heat exchangers using a novel multiobjective free search differential evolution paradigm. (5th February 2016)

• Record Type: Journal Article
• Authors: Hultmann Ayala, Helon Vicente; Keller, Patrick; Morais, Márcia de Fátima; Mariani, Viviana Cocco; Coelho, Leandro dos Santos; Rao, Ravipudi Venkata
• Is Part Of: Applied thermal engineering. Volume 94(2016:Feb.)
• Page Start: 170
• Page End: 177
• Publication Date: 2016-02-05
• Subjects: Heat exchanger design -- Multiobjective optimization -- Free search -- Differential evolution; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2015.10.066
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A novel approach combining differential evolution and free search (MOFSDE) is proposed. Simulations showed that MOFSDE produces competitive results to heat exchanger design. Results illustrate that MOFSDE efficiently achieves goals of multiobjective optimization. Abstract: Free search (FS) is a recently proposed population-based metaheuristic algorithm, inspired from the animals' behavior. FS can be applied to real value numerical optimization problems, as well as evolutionary algorithms and swarm intelligence techniques. In this paper, a novel multiobjective FS approach combined with differential evolution (MOFSDE) to heat exchanger optimization is presented. Two case studies of heat exchanger design are carried out to illustrate the efficiency of the MOFSDE. Simulation results for the two multiobjective case studies using the proposed MOFSDE are compared with those obtained by using the nondominated sorting genetic algorithm, version II (NSGA-II). The results from this comparison indicate that the MOFSDE performs better than the NSGA-II. The results illustrate that MOFSDE efficiently achieves two goals of multiobjective optimization problems: to find the solutions that converge to an approximated Pareto-front which is well spread, having the advantage of no parameter tuning apart from the population size and the number of generations.