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Your search keyword '"Bursa Uludağ Üniversitesi/Mühendislik Fakültesi."' showing total 2 results
Start Over Author "Bursa Uludağ Üniversitesi/Mühendislik Fakültesi." Journal international journal of vehicle design
2 results on '"Bursa Uludağ Üniversitesi/Mühendislik Fakültesi."'

1. Mechanical engineering design optimisation using novel adaptive differential evolution algorithm

Author

Sadiq M. Sait, Ali Rıza Yıldız, Hammoudi Abderazek, Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü., Yıldız, Ali Rıza, and F-7426-2011

Subjects
Engineering, Mathematical optimization, Engineering design optimisation, Structural desing, Crossover, Engineering, mechanical, Differential evolution algorithm, Adaptive parameter control, Crashworthiness, Transportation, 02 engineering and technology, Metaheuristics, DE, 0203 mechanical engineering, Mechanical design, Mechanical engineering designs, Constrained optimisation problems, Objective functions, Metaheuristic, Transportation science & technology, Mechanical engineering design, Adaptive algorithm, business.industry, Engineering problems, Mechanical Engineering, Spur gears, Comparison results, 020302 automobile design & engineering, Adaptive differential evolution algorithms, Genetic algorithms, Comparison result, Self-adaptive mechanisms, Differential evolution, Parameters, Automotive Engineering, Optimisation problems, business, Water cycle, Constrained Optimization Problem, Constraint Handling, Evolutionary Algorithm, Gravitational Search
Abstract

This paper introduces a new adaptive mixed differential evolution (NAMDE) algorithm for mechanical design optimisation problems. The algorithm uses a self-adaptive mechanism to update the values of mutation and crossover factors. Moreover, elitism is used where the best-found individual in each generation is retained. The performance of NAMDE is evaluated by solving 11 well-known constrained mechanical design problems and two industrial applications. Further, comparison results between NAMDE and other recently published methods, for the first problems, clearly illustrate that the proposed approach is an important alternative to solve current real-world optimisation problems. Besides this, new optimal solutions for some engineering problems are obtained and reported in this paper. For the coupling with a bolted rim problem, the objective function improved by 10%. Whereas for the spur minimisation problem, the final design provides a reduction in gearing mass by 7.5% compared to those published in previous works.

Published
2019
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2. Multi-surrogate-assisted metaheuristics for crashworthiness optimisation

Author

Cho Mar Aye, Sadiq M. Sait, Ali Rıza Yıldız, Sujin Bureerat, Nantiwat Pholdee, Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü., Yıldız, Ali Rıza, and F-7426-2011

Subjects
Engineering, Performance, Crashworthiness, Evolutionary algorithm, Transportation, 02 engineering and technology, Constrained optimisation, Crash box, Crash box design, Surrogate model, 0203 mechanical engineering, Objective functions, Total energy, Desing, Constrained optimization, Energy Absorption, Tube, Kriging model, Uncertainty, Search, 020302 automobile design & engineering, Algorithm, Crashworthiness optimisation, Mass constraints, Optimisation problems, Mathematical optimization, Shape and size, Engineering, mechanical, Grey wolf, Heuristic algorithms, Surrogate-assisted optimisation, Metaheuristic, Transportation science & technology, Numerical experiments, business.industry, Mechanical Engineering, Meta-heuristics, Sine-cosine algorithm, Meta heuristics, Ant lion, Thin-wall structures, Sine cosine algorithm, Constraint (information theory), Accidents, Automotive Engineering, Water cycle, business, Aluminum
Abstract

This work proposes a multi-surrogate-assisted optimisation and performance investigation of several newly developed metaheuristics (MHs) for the optimisation of vehicle crashworthiness. The optimisation problem for car crashworthiness is posed to find the shape and size of a crash box while the objective function is to maximise the total energy absorption subject to a mass constraint. Two main numerical experiments are conducted. Firstly, the performance of different surrogate models along with the proposed multi-surrogate model is investigated. Secondly, several MHs are applied to tackle the proposed crashworthiness optimisation problem by employing the best obtained surrogate model. The results reveal that the proposed multi-surrogate model is the best performer. Among the several MHs used in this study, sine cosine algorithm is the best algorithm for the proposed multi-surrogate model. Based on this study, the application of the proposed multi-surrogate model is better than using one particular traditional surrogate model, especially for constrained optimisation. Thailand Research Fund (TRF)

Published
2019
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