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Article title ADAPTIVE ALGORITHM OF THE PACK OF GREY WOLVES FOR SOLVING DESIGN OBJECTIVES
Authors E. V. Kuliev, S. N. Sheglov, E. A. Pantelyuk, N. V. Kulieva
Section SECTION I. DESIGN AUTOMATION
Month, Year 07, 2017 @en
Index UDC 004.896
DOI
Abstract This article is related to the solution of one of the key tasks of the automated design stage – a placement of components of super-large integrated circuits. Recently, started have been a study of application possibilities and the development of algorithms inspired by natural systems for effective decision making in CAD tasks. At the same time, there is a constant conflict between the complexity of CAD and the requirements for making effective decisions in real time. These problems can not be completely solved by parallelizing the decision-making process, increasing the number of operators, users, etc. One of the possible approaches to solving this problem is the use of new technologies at the junction of informatics, bionics and design automation. In this regard, the development of new principles and approaches for making effective decisions in design and management tasks is of great economic and social importance and is, at present, relevant and important. The article describes the algorithm of living nature, which is based on the example of a pack of gray wolves. The formulation of the problem of placing elements of ECE schemes on the set of given positions of a discrete working field is given. A modified technology for the development of nature-inspired algorithms is presented. The main steps of the algorithm for the behavior of a pack of gray wolves in relation to the allocation problem are shown. Comparative results of computational experiments are presented. The main purpose of the study is to assess the feasibility of using integrated methods inspired by natural systems to solve CAD design problems using the example of the behavior of the gray wolf pack in wildlife.

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Keywords Swarm algorithm; genetic algorithm; objective function; neighborhood; a flock of gray wolves.
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