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This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominatedPareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four real-world engineering design problems are validated using proposedEMoSOAalgorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from theParetowhich shows high convergence. VC Research

In this paper, the sine-cosine optimization algorithm (SCO) is used to solve the shape optimization of a vehicle clutch lever. The design problem is posed for the shape optimization of a clutch lever with a mass objective function and a stress constraint. Actual function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimization algorithm, and sine-cosine algorithm are used for shape optimization. The results show the ability of the sine-cosine optimization algorithm to optimize automobile components in the industry. King Fahd University of Petroleum and Minerals Khon Kaen University

In today’s world, reducing fuel consumption is one of the most important goals for the automotive industry. For this reason, weight reduction is one of the main topics in this research and for various companies. In this research, topology optimization was conducted on a suspension arm as a means of ensuring balance in automobiles. Subsequently, the model, formed by topology optimization was filled with a lattice structure and re-optimized by size optimization to obtain optimum dimensions for the model. These operations are described as lattice structure optimization. Additive manufacturing (3D printer) is necessary to produce complex models (after topology and lattice structure optimization). A static analysis of the new models was conducted by using the finite element method, and the results were compared with those of the initial design of the model. As a result of the comparison, positive results were obtained, and it was shown that topology optimization and lattice structural optimization could be used in the design of vehicle elements. According to the results obtained from lattice structure optimization, design structure can be formed more reliably than via topology optimization. In addition, both configurations and layouts of the cellular structures have a special effect on the overall performance of the lattice structure.

Due to harsh competitive conditions and the transition to new vehicles such as hybrid and full-electrical, the interest in the design of light and low-cost vehicles is increasing. In this paper, a recent metaheuristic procedure which is an equilibrium optimization algorithm (EOA) is used to solve a structural design optimization problem for a vehicle seat bracket to prove how the EOA can be used in solving industrial design problems. This paper is the first application of the EAO to real-world problems in the literature. The results strongly prove the capability of the EOA for designing optimum components in the automotive industry. Isringhausen-Aunde Teknik King Fahd University of Petroleum and Minerals Khon Kaen University

The population of the world is increasing day by day. Accordingly, the amount of production and consumption are increasing. Due to the continuous and rapid development of technology, the duration of the use of some products becomes shorter. That is why the more efficient use of limited resources is even more important. In the developing and growing automotive industry, companies are currently focusing on weight and cost reduction methods to compete. In this study, the optimum design has been achieved by using topology and shape optimization in the suspension cover used in suspension systems. As a result of the topology and shape optimization efforts, The mass of the optimum design achieved was reduced by 35.203 % according to the first design.

This paper introduces a new hybrid optimization algorithm (HWOANM) based on the Nelder-Mead local search algorithm (NM) and whale optimization algorithm (WOA). The aim of hybridization is to accelerate global convergence speed of the whale algorithm for solving manufacturing optimization problems. The main objective of our study on hybridization is to accelerate the global convergence rate of the whale algorithm to solve production optimization problems. This paper is the first research study of both the whale algorithm and HWOANM for the optimization of processing parameters in manufacturing processes. The HWOANM is evaluated using the well-known benchmark problems such as cantilever beam problem, welded beam problem, and three-bar truss problem. Finally, a grinding manufacturing optimization problem is solved to investigate the performance of the HWOANM. The results of the HWOANM for both the design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, scatter search algorithm, differential evolution algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, improved differential evolution algorithm, harmony search algorithm, hybrid particle swarm algorithm, teaching-learning-based optimization algorithm, cuckoo search algorithm, grasshopper optimization algorithm, salp swarm optimization algorithm, mine blast algorithm, gravitational search algorithm, ant lion optimizer, multi-verse optimizer, whale optimization algorithm, and the Harris hawks optimization algorithm. The results show that the HWOANM provides better exploration and exploitation properties, and can be considered as a promising new algorithm for optimizing both design and manufacturing optimization problems.

There is a growing interest in designing lightweight and low-cost vehicles. In this research, the Harris hawks optimization algorithm (the HHO), the salp swarm algorithm (SSA), the grasshopper optimization algorithm(GOA), and the dragonfly algorithm (DA) are introduced to solve shape optimization problems in the automotive industry. This research is the first application of the HHO, the SSA, the GOA, and the DA to shape design optimization problems in the literature. In this paper, the HHO, the SSA, and the DA algorithms are used for shape optimization of a vehicle brake pedal to prove how the HHO, the SSA, the GOA, and the DA can be used for solving shape optimization problems. The results show the ability of the HHO, the SSA, the GOA, and the DA to design better optimal components.

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. British Association for Psychopharmacology Benemérita Universidad Autónoma de Puebla

Many-objective optimisation is a design problem, having more than 3 objective functions, which is found to be difficult to solve. Implementation of such optimisation on aircraft conceptual design will greatly benefit a design team, as a great number of trade-off design solutions are provided for further decision making. In this paper, a many-objective optimisation problem for an unmanned aerial vehicle (UAV) is posed with 6 objective functions: take-off gross weight, drag coefficient, take off distance, power required, lift coefficient and endurance subject to aircraft performance and stability constraints. Aerodynamic analysis is carried out using a vortex lattice method, while aircraft component weights are estimated empirically. A new self-adaptive meta-heuristic based on decomposition is specifically developed for this design problem. The new algorithm along with nine established and recently developed multi-objective and many-objective meta-heuristics are employed to solve the problem, while comparative performance is made based upon a hypervolume indicator. The results reveal that the proposed optimiser is the best performer for this design task. Defence Technology Institute Thailand Research Fund

As a result of the requirements imposed by international organizations and governments on fuel emissions, there is a growing interest in the design of lightweight vehicles with low-fuel emissions. Metaheuristic methods have been widely used for the optimum design of vehicle components in recent years for which successful results have been reported. Encouraged by such results obtained from the methods mentioned, the Henry gas solubility optimization algorithm (HGSO), a recently developed optimization method, is used to solve the shape optimization of a vehicle brake pedal to prove how HGSO can be used for solving shape optimization problems. This paper is the first application of the HGSO in connection with real-world optimization problems in the literature. The results show HGSO's ability to design better optimal components in the automotive industry. Pandit Deendayal Petroleum University King Fahd University of Petroleum and Minerals

The optimum design of a cam mechanism is a very interesting problem in the contact mechanics today, due to the alternative industrial applications as a mechanism of precision. In this paper, a new evolutionary algorithm called modified adaptive differential evolution (MADE) is introduced for multi-objective optimization of a cam mechanism with offset translating roller follower. The optimization procedure is investigated for three objectives among them minimum congestion, maximum efficiency, and maximum strength resistance of the cam. To enhance the design quality of the mechanism in the optimization process, more geometric parameters and more design constraints are included in the problem formulation. In order to validate the developed algorithm, three engineering design problems are solved. The simulation results for the tested problems indicate the effectiveness and the robustness of the proposed algorithm compared to the various existed optimization methods. Finally, the optimal results obtained for the case study example provide very useful decisions for a cam mechanism synthesis. CNEPRU Ministry of Higher Education and Scientific Research

This paper aims to develop a new crash box with improved crashworthiness at reduced cost and weight as a base design for use in the automotive industry. Firstly, a baseline crash box model as presently used by the automotive industry was comprehensively examined by numerical crash analysis using Ls-Dyna software. Considering the initial design geometry, forty-five different crash box designs were developed by making changes in the geometry and wall thickness of the thin walled structures. The effects of the changes in wall thickness and geometry in alternative crash box designs on crash performances such as total energy absorption, peak crush force, mean crush force, specific energy absorption and crush force efficiency were investigated. The optimum crash box design obtained numerically was validated experimentally by means of the drop tower impact system. The numerical crash analysis results clearly agree with the experimental test results. In this study, a new crash box design at a lower cost and performing better in crashes compared with the other forty-six designs has been obtained and can be used in the automotive industry as an energy absorber. The results have revealed that crash box geometry, as well as the number and position of the spot welds and sheet-metal thickness have an important effect on crash performance, weight and cost of the crash boxes.

The product design process has a very important effect on product costs. It aims to develop products that are able to compete by achieving an optimum design in the product design process. This research presents the first application in the literature of the grey wolf, whale, water cycle, ant lion and sine-cosine optimization algorithms for the optimum design of vehicle components. In this study, the optimal structural model of a vehicle connecting rod was determined. In the optimization process, various design alternatives were created by using the latin hypercube method. Stress analysis was performed for each of these designs. According to the generated responses, equations for objective and constraint functions were obtained. The optimization problem was solved using the above mentioned algorithms which have been newly developed in the literature, resulting in optimum connection rod design. The results demonstrate that the grey wolf, whale, water cycle, ant lion and sine-cosine algorithms are very important options in optimizing design and manufacturing optimization problems.

In recent years, there has been a great deal of interest in lightweight vehicle design due to regulations about reducing fuel consumption and emissions. Lightweight design of vehicle components is one of the most important research topics in vehicle design. Developing the optimum structure in the early stages of design process is very important for minimizing the vehicle weight and production costs. In this paper, an automobile hinge component has been developed using PA66 GF60 glass fiber-reinforced polyamide composite materials instead of conventional steel. The automobile hinge component has been conceived using computer aided design software. Topology optimization was made under specific loadings subject to the constraints of finite element method analysis. As a result of this study, optimum dimensions of the component have been obtained and the weight of the component has been reduced via structural topology optimization techniques while satisfying displacement and stress conditions. The results show that composite materials are an important alterative in lightweight vehicle design.

In this research, the Harris hawks optimization algorithm (HHO), the grasshopper optimization algorithm (GOA) and the multi-verse optimization algorithm (MVO) have been used in solving manufacturing optimization problems. This paper is the first research study for the optimization of processing parameters for manufacturing processes using the HHO, the GOA, and the MVO in the literature, and in particular, for grinding operations. A well-known grinding optimization problem is solved to prove how effective the HHO, the GOA and the MVO are in solving manufacturing problems and to demonstrate superiority over other algorithms. The results of the HHO, the GOA and the MVO are compared with other methods such as the genetic algorithm, the ant colony algorithm, the scatter search, the differential evolution algorithm, the particle swarm optimization algorithm, simulated annealing, the artificial bee colony, harmony search, improved differential evolution, the hybrid particle swarm algorithm, teaching learning-based optimization algorithms, the cuckoo search, and the fractal search algorithm. The results show that the HHO, the GOA, and the MVO are efficient optimization approaches for obtaining optimal manufacturing variables in manufacturing operations. Huazhong University of Science and Technology King Fahd University of Petroleum and Minerals

In this paper, a novel hybrid optimization algorithm (H-HHONM) which combines the Nelder-Mead local search algorithm with the Harris hawks optimization algorithm is proposed for solving real-world optimization problems. This paper is the first research study in which both the Harris hawks optimization algorithm and the H-HHONM are applied for the optimization of process parameters in milling operations. The H-HHONM is evaluated using well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a milling manufacturing optimization problem is solved for investigating the performance of the H-HHONM. Additionally, the salp swarm algorithm is used to solve the milling problem. The results of the H-HHONM for design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, teaching learning-based optimization algorithm, cuckoo search algorithm, multi-verse optimization algorithm, Harris hawks optimization optimization algorithm, gravitational search algorithm, ant lion optimizer, moth-flame optimization algorithm, symbiotic organisms search algorithm, and mine blast algorithm. The results show that H-HHONM is an effective optimization approach for optimizing both design and manufacturing optimization problems.

Reliability-based design optimization (RBDO) is an effective method for structural optimization due to its ability to take into consideration uncertainties in design variables. Performance measure approach (PMA) based methods are commonly utilized to evaluate the probabilistic constraints of RBDO problems. The advanced mean value (AMY) method is a very commonly used due to its simpleness and effectiveness. However, the AMV method sometimes produces unstable and inefficient results for concave and highly nonlinear limit-state functions. In order to improve robustness and efficiency, many methods have been developed, for example, chaos control based and conjugate gradient-based methods. These methods lead to more stable results as compared with the AMV approach but they are inefficient for use in complex and convex limit-state functions. The RBDO of structural components is often a difficult issue due to complicated constraints. In this paper, a novel hybrid approach, referred to as "hybrid gradient analysis (HGA)" is introduced for the evaluation of both convex and concave constraint functions in RBDO. The HGA method combines AMV and conjugate gradient analysis (CGA). The robustness, simpleness and effectiveness of the proposed HGA method are compared with various PMA methods aimed at reliability such as AMV, chaos control (CC), conjugate mean value (CMV), modified chaos control (MCC), hybrid mean value (HMV) and CGA methods by means of several nonlinear convex/concave limit-state functions and structural RBDO problems. Reliability analysis and RBDO results point out that the HGA approach introduced here is more effective and robust than the well-known approaches. Ministry of Science, Industry and Technology of Turkey National Science Foundation U.S. Department of Energy Mississippi State University

In the present study, the fatigue behaviour of a clutch diaphragm spring is investigated experimentally and numerically. Differential evolution optimisation algorithm and response surface methodology are used to define optimal variables of the diaphragm springs under constraints both minimum stress and required clamp load. The required clamp load is checked by the chi-square theorem vs. to target clamp load curve. Ten design variables are considered for creating a design of experiment with two steps, including finger shape optimisation with eight variables, load, and stress optimisation with two variables as the aim of maximum fatigue definition. One hundred seventy-five different designs are analysed numerically. As a result of the optimisation study, the optimum design is provided an endless lifetime subject to the required load, minimum stress, and minimum weight. The optimum design is manufactured and tested experimentally. Valeo

In this paper, nine recent meta-heuristics have been employed to search for optimal design of an automatic planetary gear train. The function of the designed system is to automatically transmit power and motion in automobiles. Nine mixed decision parameters are considered in the optimisation procedure. The geometric conditions such as the undercutting, the maximum overall diameter of the transmission, as well as the spacing of multiple planets are taken into account to ensure an optimum design. All the above algorithms are tested both quantitatively and qualitatively for solution quality, robustness, and their time complexity is determined. Results obtained illustrate that the utilised approaches can effectively solve the planetary gearbox problem. Besides this, the comparative study indicates that roulette wheel selection-elitist differential evolution (ReDE) outperforms the other algorithms in terms of the statistical results, and FA has the best convergence behaviour. Meanwhile, multi-verse optimisation (MVO) and butterfly optimisation algorithm (BOA) performed better than the other used algorithms when computation time was considered. King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

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)

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.

This paper describes an innovative optimization approach that offers significant improvements in performance over existing methods to solve shape optimization problems. The new approach is based on two-stages which are (1) Taguchi's robust design approach to find appropriate interval levels of design parameters (2) Immune algorithm to generate optimal solutions using refined intervals from the previous stage. A benchmark test problem is first used to illustrate the effectiveness and efficiency of the approach. Finally, it is applied to the shape design optimization of a vehicle component to illustrate how the present approach can be applied for solving shape design optimization problems. The results show that the proposed approach not only can find optimal but also can obtain both better and more robust results than the existing algorithm reported recently in the literature.

This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The put-pose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective 1-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case Study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.

The focus of this research is on a hybrid method combining immune algorithm with a hill climbing local search algorithm for solving complex real-world optimization problems. The objective is to contribute to the development of more efficient optimization approaches with the help of immune algorithm and hill climbing algorithm. The hybrid algorithm combines the exploration speed of immune algorithm with the powerful ability to avoid being trapped in local minimum of hill climbing. This hybridization results in a solution that leads to better parameter values. This research is the first application of immune algorithm to the optimization of machining parameters in turning and also shape design optimization problems in the literature. The results of single-objective benchmark problem, multi-objective disc-brake problem, an automobile shape design optimization problem taken from the literature and case studies for multi-pass turning operation have demonstrated the superiority of the proposed hybrid over the other techniques in terms of solution quality and convergence rates.

This paper presents a novel optimization approach that is a new hybrid optimization approach based on the particle swarm optimization algorithm and receptor editing property of immune system. The aim of the present research is to develop a new optimization approach and then to apply it in the solution of optimization problems in both the design and manufacturing areas. A single-objective test problem, tension spring problem, pressure vessel design optimization problem taken from the literature and two case studies for multi-pass turning operations are solved by the proposed new hybrid approach to evaluate performance of the approach. The results obtained by the proposed approach for the case studies are compared with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, and integration of simulated annealing and Hooke-Jeeves pattern search.

In order to meet today's vehicle design requirements and to improve the cost and fuel efficiency, there is an increasing interest to design light-weight and cost-effective vehicle components. In this study, a topology design approach, which aims at defining of an optimum model at design stage, is used to decrease product cost and lead time to markets. An example of a vehicle component design is presented to illustrate the application of the topology optimisation approach for the optimal design of vehicle components.

Most structural products have complex geometry to meet customer’s demand of high functionality. Since manufacturing those products in one piece is either impossible or uneconomical, most structural products are assemblies of components with simpler geometries. The conventional way to design structural assemblies is to design overall geometry first, and then decompose the geometry to determine the part boundary and joint locations. This two-step process, however, can lead to sub-optimal designs since the product geometry, even if optimized as one piece, would not be optimal after decomposition. This paper presents a method for synthesizing structural assemblies directly from the design specifications, without going through the two-step process. Given an extended design domain with boundary and loading conditions, the method simultaneously optimizes the topology and geometry of an entire structure and the location and configuration of joints, considering structural performance, manufacturability, and assembleability. As a relaxation of our previous work utilizing a beam-based ground structure [1], this paper presents a new formulation in a continuum design domain, which greatly enhances the ability to represent complex structural geometry observed in real-world products. A multi-objective genetic algorithm is used to obtain Pareto optimal solutions that exhibits trade-offs among stiffness, weight, manufacturability, and assembleability.Copyright © 2008 by ASME

Harmony search algorithms have recently gained a lot of attention from the optimization research community. In this chater, a new optimization approach based on harmony search algorithm and Taguchi's method is presented to solve shape optimization problems. The validity and efficiency of the proposed approach are evaluated in an optimum design problem of a vehicle component by illustrating how the present approach can be applied for solving shape optimization problems. The first application of harmony search algorithm to the shape optimization problems in the literature is presented in this chapter. The results of the shape optimization problem indicate that the proposed approach is highly competitive and it can be considered as a viable alternative in solving real-world optimization problems, finding beter solutions compared to other approaches that are representative of the state-of-the-art in the optimization literature.

This paper introduces a new hybrid optimisation approach by combining immune algorithm and simulated annealing algorithm. The main goal of the present research is to develop a novel optimisation approach for the solution of engineering optimisation problems. To demonstrate the effectiveness and robustness of the proposed approach, it is applied to engineering optimisation problems including the multi-pass turning problem taken from the literature and a shape design optimisation problem from automotive industry. Compared with other existing approaches in the literature, the proposed approach is very useful for optimal design and manufacturing.

Most structural products have complex geometry to meet customer's demand of high functionality. Since manufacturing those products in one piece is either impossible or uneconomical, most structural products are assemblies of components with simpler geometries. The conventional way to design structural assemblies is to design overall geometry first, and then decompose the geometry to determine the part boundary and joint locations. This two-step process, however, can lead to sub-optimal designs since the product geometry, even if optimized as one piece, would not be optimal after decomposition. This paper presents a method for synthesizing structural assemblies directly from the design specifications, without going through the two-step process. Given an extended design domain with boundary and loading conditions, the method Simultaneously optimizes the topology and geometry of an entire structure and the location and configuration of joints, considering structural performance, manufacturability, and assembleability. As a relaxation of our previous work utilizing a beam-based ground structure [1], this paper presents a new formulation in a continuum design domain, which greatly enhances the ability to represent complex structural geometry observed in real-world products. A multi-objective genetic algorithm is used to obtain Pareto optimal solutions that exhibits trade-offs among stiffness, weight, manufacturability, and assembleability.

Harmony search algorithms have recently gained a lot of attention from the optimization research community. In this paper, an improved harmony search algorithm is introduced to solve engineering optimization problems. To demonstrate the effectiveness and robustness of the proposed approach, it is applied to an engineering design and manufacturing optimization problem taken from the literature. The results obtained by the new hybrid harmony search approach for the case studies are compared with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, and feasible direction method and handbook recommendation. The results of case studies show that the proposed optimization approach is highly competitive and that can be considered a viable alternative to solve design and manufacturing optimization problems. Significance: This paper presents a novel hybrid algorithm based on harmony search algorithm and Taguchi method. The new approach is applied to the case studies for turning and milling and design optimization problems.

Although genetic algorithm and multi-objective optimization techniques are widely used to solve problems in the design and manufacturing area, further improvements are required to develop more efficient techniques regarding multi-objective optimization problems. The main goal of the present research is to further develop and strengthen the genetic algorithm based multi-objective optimization approach to generate real-world design solutions in the automotive industry. In this research, a new hybrid approach based on Taguchi's method and a genetic algorithm is presented to achieve better Pareto-optimal set solutions for multi-objective design optimization problems. In addition, fatigue damage and life are also considered to evaluate the results of the design optimization process. The validity and efficiency of the proposed approach are evaluated and illustrated with test problems taken from the literature. It is then applied to a vehicle component taken from the automotive industry.

The current paper presents a hybrid enhanced genetic algorithm that is developed for solving the optimization problems in design and manufacturing. The present approach is applied to optimize turning operation for the determination of cutting parameters considering minimum production cost under a set of machining constraints. A refined design space for population is introduced by integrating the robust parameter design concept into the genetic algorithm to solve multi-objective and single-objective optimization problems. First, the proposed approach is validated using test problems and metrics taken from literature. Finally, it is applied to the turning optimization problem. The computational experimental results show the effectiveness of the proposed approach in the turning optimization problem.

This article describes an integrated and optimized product design framework to support the design optimization applications in concurrent engineering (CE). The significant consideration is given to show the effectiveness of hybrid approaches and how they can be used to improve the performance of integrated design optimization applications. The proposed approach is based on two-stages which are (1) the use of neural networks (NNs) and genetic algorithm (GA) with feature technology for integrated design activities and (2) the use of Taguchi’s method and GA for design parameters optimization. The first stage resulted in better integrated design solutions in terms of computational complexity and later resulted in a solution, which leads to better and more robust parameter values for multi-objective shape design optimization. The effectiveness and validity of the proposed approach are evaluated with examples.

In this paper, neural network- and feature-based approaches are introduced to overcome current shortcomings in the automated integration of topology design and shape optimization. The topology optimization results are reconstructed in terms of features, which consist of attributes required for automation and integration in subsequent applications. Features are defined as cost-efficient simple shapes for manufacturing. A neural network-based image-processing technique is presented to match the arbitrarily shaped holes inside the structure with predefined features. The effectiveness of the proposed approach in integrating topology design and shape optimization is demonstrated with several experimental examples.

An important problem in automotive industry is how to achieve better design concepts by considering structure performance and manufacturing cost in the early stages of product development. The topology design and optimisation provide an initial design concept for downstream applications, which leads to achieving better design by using computer-aided techniques. In this research, engine mount bracket design is presented to illustrate the application of topology optimisation approach for optimal design of vehicle components under dynamic loading conditions. The objective of the proposed research is to create an initial design concept, which has optimal structural layout. The effectiveness and verification of proposed approach are demonstrated with experimental results.