دانلود مقاله ISI انگلیسی شماره 7578
عنوان فارسی مقاله

یک الگوریتم جدید ترکیبی مصنوعی کلنی زنبور برای طراحی و ساخت بهینه مقاوم

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
7578 2013 7 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Applied Soft Computing, Volume 13, Issue 5, May 2013, Pages 2906–2912

کلمات کلیدی
ساخت - فرز - بهینه سازی سازه - روش دوگانه سوز
پیش نمایش مقاله
پیش نمایش مقاله یک الگوریتم جدید ترکیبی مصنوعی کلنی زنبور برای طراحی و ساخت بهینه مقاوم

چکیده انگلیسی

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem. A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.

مقدمه انگلیسی

Designing and manufacturing new products possessing desired property are important in industry. With the advent of ever faster computing platforms, the computer aided design and optimization tools are becoming more attractive due to its great contribution to cost, material and time savings in the procedures of the engineering design. The application of these tools allows a more rapid design process and more detailed design studies. Over the past decades, a number of optimization algorithms have been used extensively in structural and manufacturing optimization tasks. The early works on the topics mostly use various mathematical techniques. These methods may not be used efficiently in finding global optimum solutions. As an alternative to traditional techniques, population-based optimization approaches, such as, genetic algorithm, particle swarm optimization algorithm, artificial immune algorithm, cuckoo search algorithm and artificial bee colony algorithm have been developed by mimicking natural phenomena and widely applied in various fields of science [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11] and [12]. Artificial bee colony algorithm (ABC) is one of the most recently introduced swarm-based algorithms based on the intelligent foraging behaviour of honey bee swarm [13]. The ABC has been found to be successful in a wide variety of optimization tasks [13], [14] and [15]. On the other hand, researchers are paying more and more interest on hybrid algorithms to solve optimization problems. The hybrid algorithms have shown outstanding reliability and efficiency in application to the engineering optimization problems [16], [17], [18], [19] and [20]. The main goal of the present research is to develop a robust optimization approach based on artificial bee colony algorithm and Taguchi method to solve design and manufacturing optimization problems. In the new hybrid approach, S/N values are calculated and ANOVA (analysis of variance) table for the objective function is formed using S/N ratios respectively. According to results of ANOVA table, appropriate interval levels of design variables are found and then, initial population of artificial bee colony algorithm is defined according to these interval levels. Then optimum results of the problem are obtained using artificial bee colony algorithm. Since the ABC has been found to be successful in a wide variety of optimization tasks, it is used in this paper. The developed new hybrid optimization approach entitled hybrid robust artificial bee colony algorithm (HRABC) is applied to optimum design of a vehicle part taken from automotive industry and to optimization of the machining parameters in multi-tool milling operations. The results of the HRABC for each case study show that the proposed optimization method converges rapidly to the global optimum solution and provides reliable and accurate solutions. The remaining contents of the paper are organized as follows. Literature review is given in Section 2. The standard ABC and Taguchi method are presented in Section 3. In Section 4, the proposed approach is used for optimization of a vehicle component. The results are discussed in Section 5. An application of the HRABC to optimization of the machining parameters in multi-tool milling operations is given in the Appendix A.

نتیجه گیری انگلیسی

This paper describes a new optimization approach (HRABC) based on artificial bee colony algorithm and Taguchi method for solving structural design and manufacturing optimization problems. The Taguchi method is used to define robust initial population to achieve better initialize the artificial bee colony algorithm. The solution space of the artificial bee colony algorithm is refined based on the effect of the various design variables on the objective function. The HRABC is applied to the optimization of a vehicle component taken from automotive industry and the multi-tool milling problem. Results obtained from the HRABC for structural design and multi-tool milling problems have been compared with those obtained differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, hybrid robust genetic algorithm, scatter search algorithm, ant colony algorithm and genetic algorithm. The results and comparisons in Table 2 and Table 3 demonstrate the effectiveness and robustness of the HRABC in solving optimization problems in the different area of the engineering problems. The HRABC can not only improve the solution quality but also reduce the computational effort. The studies clearly indicate that the HRABC outperforms the state-of-the-art evolutionary algorithms for the problems solved in this article. The future work is to apply the HRABC for solving other engineering optimization problems.

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