فایل ورد کامل روش بهینه سازی جدید: پژواک سازی دلفین

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تعداد صفحات این فایل: ۵۵ صفحه

در این مطالعه، روش بهینه سازی جدیدی براساس پژواک سازی دلفین توسعه یافته است. مزیت روش جدید، کارکردن طبق تلاشهای محاسباتی کاربر جهت نیل به بهینه سازی می باشد. در این الگوریتم، برای اجرای بهینه سازی مناسب، ضریب همگرایی تعریف شده توسط Kaveh و Farhoudi کنترل شده است.
برای مثالهای بهینه شده در این مقاله، DE به نتایج بهتری با نرخ های همگرایی بالاتر نسبت به سایر الگوریتم های فراابتکاری موجود نظیر GA، ACO، PSO، BB-BC، HS، ES، SGA، TS ، ICA، IACO، PSOPC، HPSACO، و CSS قبلاً بکاررفته برای این مسائل دست می یابد. مولفین براین باورند که نتایج بدست آمده از الگوریتم های فراابتکاری، عمدتاً به تنظیم پارامترهای الگوریتم ها بستگی دارد. همچنین عقیده برآن است که با اجرای تعداد محدودی مثال عددی، نمی توان به ارشدیت و برتری یک روش نسبت به روشهای دیگر پی برد. پژواک سازی دلفین، یک مسئله بهینه سازی است که قادر انطباق خود با تیپ مسئله موجود را دارد، دارای نرخ همگرایی مطلوبی است و منجر به یک پاسخ بهینه قابل قبول در تعداد حلقه های تعیین شده توسط کاربر می گردد.

عنوان انگلیسی:A new optimization method: Dolphin echolocation~~en~~

Introduction The recent generation of the optimization methods is metaheuristics that are proposed to solve complex problems. Every meta-heuristic method consists of a group of search agents that explore the feasible region based on both randomization and some specified rules [1]. The rules are usually inspired by the laws of natural phenomena. Genetic Algorithm (GA) proposed by Holland [2] and Goldberg [3] is inspired by Darwin’s theory about biological evolutions. Particle swarm optimization (PSO) proposed by Eberhart and Kennedy [4] simulates social behavior, and it is inspired by the movement of organisms in a bird flock or fish school. Truss optimization with dynamic constraints using a particle swarm algorithm can be found in the work of Gomes [5]. Ant Colony Optimization (ACO) formulated by Dorigo et al. [6] imitates foraging behavior of ant colonies. Many other natural-inspired algorithms such as Simulated Annealing (SA) proposed by Kirkpatrick et al. [7], Harmony Search (SA) presented by Geem et al. [8], Gravitational Search Algorithm (GSA) proposed by Rashedi et al. [9], Big Bang–Big Crunch algorithm (BB–BC) proposed by Erol and Eksin [10], and improved by Kaveh and Talatahari [11]. Due to their good performance and ease of implementation, these methods have been widely applied to various problems in different fields of science and engineering. Structural optimization is one of the active branches of the applications of optimization algorithms [12–۱۵]. One of the most recent meta-heuristic algorithms is the Charged System Search (CSS) proposed by Kaveh and Talatahari [16] that uses the electric laws of physics and the Newtonian laws of mechanics to guide the Charged Particles (CPs) to explore the locations of the optimum. Bat-inspired algorithm introduced by Yang [17] is another recent meta-heuristic algorithm which mimics the behavior of bats in detecting prey. Dolphin echolocation is a new optimization method which is presented in this paper. This method mimics strategies used by dolphins for their hunting process. Dolphins produce a kind of voice called sonar to locate the target, doing this dolphin change sonar to modify the target and its location. Dolphin echolocation is depicted in Fig. 1. This fact is mimicked here as the main feature of the new optimization method. After this introduction, Section 2 presents the dolphin’s echolocation in nature. Section 3 introduces dolphin echolocation algorithm, Section 4 presents structural optimization problems to be solved. This follows by Section 5 which includes some numerical examples from truss and frame structures. The last section is devoted to concluding remarks indicating the capabilities of the new method in comparison to some other meta-heuristic algorithms like GA, ACO, PSO, BB, HS, SA, and CSS. 2. Dolphin echolocation in nature The term ‘‘echolocation’’ was initiated by Griffin [18] to describe the ability of flying bats to locate obstacles and preys by listening to echoes returning from high-frequency clicks that they emitted. Echolocating animals include some mammals and a few birds. The best studied echolocation in marine mammals is that of the bottlenose dolphins, Au [19]. A dolphin is able to generate sounds in the form of clicks. Frequency of these clicks is higher than that of the sounds used for communication and differs between species. When the sound strikes an object, some of the energy of the sound-wave is reflected back towards the dolphin. As soon as an echo is received, the dolphin generates another click. The time lapse between click and echo enables the dolphin to evaluate the distance from the object; the varying strength of the signal as it is received on the two sides of the dolphin’s head enabling him to evaluate the direction. By continuously emitting clicks and receiving echoes in this way, the dolphin can track objects and home in on them, May [20]. The clicks are directional and are for echolocation, often occurring in a short series called a click train. The click rate increases when approaching an object of interest, Au [19]. Though bats also use echolocation, however, they differ from dolphins in their sonar system. Bats use their sonar system at short ranges of up to approximately 3–۴ m, whereas dolphins can detect their targets at ranges varying from a few tens of meters to over a hundred meters. Many bats hunt for insects that dart rapidly toand-fro, making it very different from the escape behavior of a fish chased by dolphin. The speed of sound in air is about one fifth of that of water, thus the information transfer rate during sonar transmission for bats is much shorter than that of the dolphins.

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