A Comparative Study of Recent Non-traditional Methods for Mechanical Design Optimization
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ORIGINAL PAPER
A Comparative Study of Recent Non-traditional Methods for Mechanical Design Optimization Ali Riza Yildiz1 • Hammoudi Abderazek2 • Seyedali Mirjalili3 Received: 24 January 2019 / Accepted: 4 May 2019 CIMNE, Barcelona, Spain 2019
Abstract 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 Introduction The main objective of a mechanical engineer during the design procedure of a machine element is the search for the best compromise between both economic and technological imperatives. The mechanical design optimization problems involve multiple objectives and mixed variables, in addition to several nonlinear constraints on kinematic, geometric conditions and materials resistance. During the three last decades, several mathematical programming algorithms have been developed to solve problems in various engineering and industrial applications. However, most of these methods always require the knowledge of the gradients of the objective function and constraints [5]. In the majority of cases, the classical algorithms are not able to find the global optimal solutions because usually terminate when the gradient of the function is very close to
& Ali Riza Yildiz [email protected] 1
Department of Automotive Engineering, Bursa Uludag˘ University, Go¨ru¨kle, Bursa, Turkey
2
Applied Precision Mechanics Laboratory, Institute of Optics and Precision Mechanics, Setif -1- University, Setif, Algeria
3
School of Information and Communication Technology, Griffith University, Nathan Campus, Brisbane, QLD 4111, Australia
zero, and this can happen both in case of local and global solutions [43, 46]. Unlike the deterministic methods, metaheuristic approaches do not require the gradient information of the optimization problem to achieve the global solution [43]. These algorithms can be broadly classified into three major categories: evolutionary algorithms (EAs), physical algorithms and swarm-based methods. The EAs mimic the process of natural evolutionary principles [7] in order to develop search and optimization techniques. In this class of methods, the most well-known EAs are genetic algorithm (GA) [24], genetic programming (GP) [33], dif
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