A Comparative Study of Recent Non-traditional Methods for Mechanical Design Optimization

Solving practical mechanical problems is considered as a real challenge for evaluating the efficiency of newly developed algorithms. The present article introduces a comparative study on the application of ten recent meta-heuristic approaches to optimize the design of six mechanical engineering optimization problems. The algorithms are: the artificial bee colony (ABC), particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO), ant lion optimizer (ALO), water cycle algorithm (WCA), evaporation rate WCA (ER-WCA), grey wolf optimizer (GWO), mine blast algorithm (MBA), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). The performances of the algorithms are tested quantitatively and qualitatively using convergence speed, solution quality, and the robustness. The experimental results  on the six mechanical problems demonstrate the efficiency and the ability of the algorithms used in this article.

[1]  Ali R. Yildiz,et al.  A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations , 2013, Appl. Soft Comput..

[2]  Tung Khac Truong,et al.  An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints , 2016, Neural Computing and Applications.

[3]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[4]  Richard A. Formato,et al.  Central force optimization: A new deterministic gradient-like optimization metaheuristic , 2009 .

[5]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[6]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[7]  Ali R. Yildiz,et al.  Structural design of vehicle components using gravitational search and charged system search algorithms , 2015 .

[8]  Amir H. Gandomi,et al.  Construction Cost Minimization of Shallow Foundation Using Recent Swarm Intelligence Techniques , 2018, IEEE Transactions on Industrial Informatics.

[9]  MirjaliliSeyedali.  Moth-flame optimization algorithm , 2015 .

[10]  Ali Rıza Yıldız,et al.  Optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm , 2015 .

[11]  Jiang Jianjun,et al.  A Dolphin Partner Optimization , 2009, 2009 WRI Global Congress on Intelligent Systems.

[12]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[13]  Morteza Kiani,et al.  A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and NVH Optimization , 2016 .

[14]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[15]  Ali R. Yildiz,et al.  A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry , 2012 .

[16]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[17]  Hammoudi Abderazek,et al.  Adaptive mixed differential evolution algorithm for bi-objective tooth profile spur gear optimization , 2017 .

[18]  Hammoudi Abderazek,et al.  A differential evolution algorithm for tooth profile optimization with respect to balancing specific sliding coefficients of involute cylindrical spur and helical gears , 2015 .

[19]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[20]  Ali Rıza Yıldız,et al.  Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod , 2018 .

[21]  Trung Nguyen-Thoi,et al.  An improved constrained differential evolution using discrete variables (D-ICDE) for layout optimization of truss structures , 2015, Expert Syst. Appl..

[22]  Tetsuyuki Takahama,et al.  Efficient constrained optimization by the ε constrained adaptive differential evolution , 2010, IEEE Congress on Evolutionary Computation.

[23]  Ardeshir Bahreininejad,et al.  Mine blast algorithm for optimization of truss structures with discrete variables , 2012 .

[24]  Pascal Lafon,et al.  A Comparison of Evolutionary Algorithms for Mechanical Design Components , 2002 .

[25]  Ardeshir Bahreininejad,et al.  Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures , 2015 .

[26]  Yun Li,et al.  Optimization and robustness for crashworthiness of side impact , 2001 .

[27]  Ali Rıza Yıldız,et al.  Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes , 2017 .

[28]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[29]  Ali Rıza Yıldız,et al.  A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects , 2017 .

[30]  Ardeshir Bahreininejad,et al.  Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems , 2013, Appl. Soft Comput..

[31]  Ali Riza Yildiz,et al.  A new design optimization framework based on immune algorithm and Taguchi's method , 2009, Comput. Ind..

[32]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[33]  C. Coello.  TREATING CONSTRAINTS AS OBJECTIVES FOR SINGLE-OBJECTIVE EVOLUTIONARY OPTIMIZATION , 2000 .

[34]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[35]  Betül Sultan Yıldız,et al.  A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems , 2017 .

[36]  Anyong Qing.  Differential Evolution: Fundamentals and Applications in Electrical Engineering , 2009 .

[37]  Xiaodong Wu,et al.  Small-World Optimization Algorithm for Function Optimization , 2006, ICNC.

[38]  C. Darwin.  The origin of species by means of natural selecti : or, The preservation of favored races in the struggle for life / , 2022 .

[39]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[40]  Ali Rıza Yıldız,et al.  Optimum design of cam-roller follower mechanism using a new evolutionary algorithm , 2018 .

[41]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[42]  Rajendran Saravanan.  Manufacturing Optimization through Intelligent Techniques , 2017 .

[43]  Arturo González,et al.  Characterization of non-linear bearings using the Hilbert–Huang transform , 2015 .

[44]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[45]  Mohamed Cheriet,et al.  Curved Space Optimization: A Random Search based on General Relativity Theory , 2012, ArXiv.

[46]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[47]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[48]  Long Wang,et al.  The Crucial Problem of the NSS in the Ecommerce , 2007 .

[49]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[50]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[51]  M. J. Mahjoob,et al.  A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search , 2010, Comput. Math. Appl..

[52]  Xuyan Tu,et al.  Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search , 2007 .

[53]  Ali R. Yildiz,et al.  Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations , 2013, Appl. Soft Comput..

[54]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[55]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[56]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[57]  Kazuhiro Saitou,et al.  Topology Synthesis of Multicomponent Structural Assemblies in Continuum Domains , 2011 .

[58]  Ali R. Yildiz,et al.  Cuckoo search algorithm for the selection of optimal machining parameters in milling operations , 2012, The International Journal of Advanced Manufacturing Technology.

[59]  Ali Rıza Yıldız,et al.  Topography and topology optimization of diesel engine components for light-weight design in the automotive industry , 2019, Materials Testing.

[60]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[61]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[62]  J. Gentle.  Random number generation and Monte Carlo methods , 1998 .

[63]  Ali R. Yildiz,et al.  A novel hybrid immune algorithm for global optimization in design and manufacturing , 2009 .

[64]  Ali Kaveh,et al.  Colliding bodies optimization: A novel meta-heuristic method , 2014 .

[65]  Ardeshir Bahreininejad,et al.  Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems , 2015, Appl. Soft Comput..

[66]  Nantiwat Pholdee,et al.  Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame , 2017, International Journal of Vehicle Design.

[67]  Rajiv Tiwari,et al.  Multi-objective design optimisation of rolling bearings using genetic algorithms , 2007 .

[68]  Gaurav Dhiman,et al.  Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications , 2017, Adv. Eng. Softw..

[69]  Ali R. Yildiz,et al.  Comparison of evolutionary-based optimization algorithms for structural design optimization , 2013, Eng. Appl. Artif. Intell..

[70]  Hamed Shah-Hosseini,et al.  Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation , 2011, Int. J. Comput. Sci. Eng..

[71]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[72]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[73]  Bilal Alatas,et al.  ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization , 2011, Expert Syst. Appl..

[74]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[75]  Betül Sultan Yıldız,et al.  Natural frequency optimization of vehicle components using the interior search algorithm , 2017 .

[76]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[77]  Ali R. Yildiz,et al.  A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing , 2013, Appl. Soft Comput..