Static and Dynamic Algorithms for k-Point Clustering Problems

Static and Dynamic Algorithms for k-Point Clustering Problems

Let S be a set of n points in d-space aild let 1 :5 k :5 n be an integer. A unified approach is given for solving the problem of finding a sub set of S of size k that mjnimizes some closeness measure, such as the diameter, perimeter or the circumradius. Moreover, data structures are given that maintain such a subset under insertions and deletions of points.

UPFC operating point evaluation to solve static security and dynamic stability problems by Genetic Algorithms

In this paper the authors propose a Genetic Algorithm (GA) to determine the operating point of a Unified Power Flow Controller (UPFC) for simultaneous solving of static security and dynamic stability problems. In particular, the interest is focused on damping of the electromechanical oscillations. The proposed algorithm is based on genetic mutation operator and it acts directly on the control p...

A Framework for Adapting Population-Based and Heuristic Algorithms for Dynamic Optimization Problems

In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the ...

semi-analytical solution for static and forced vibration problems of laminated beams through smooth fundamental functions method

در این پایان نامه روش جدیدی مبتنی بر روش حل معادلات دیفرانسیل پارهای بر اساس روش توابع پایه برای حل مسایل ارتعاش اجباری واستاتیک تیرها و صفحات لایه ای ارایه شده است که می توان تفاوت این روش با روش های متداول توابع پایه را در استفاده از توابع هموار در ارضاء معادلات حاکم و شرایط مرزی دانست. در روش ارایه شده در این پایاننامه از معادله تعادل به عنوان معادله حاکم بر رفتار سیستم استفاده شده است که مو...

Online algorithms for clustering problems