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An optimization algorithm based the feasibility rules mimicry physics constraints

Author: YinJian
Tutor: TanZuoï¼›XieLiPing
School: Taiyuan University of Science and Technology
Course: Applied Computer Technology
Keywords: The mimicry physics optimization algorithm Virtual force Feasibility rules Constrained Optimization Mass function
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 19
Quote: 0
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Abstract


Constrained optimization problems widely exist in the various fields of science and engineering research , academia, research has been a hot issue . Mimicry physics optimization algorithm as a new kind of random optimization algorithm has been successfully applied to solve global optimization problems . Feasibility rules of law is a very simple principle constraint processing technology , which has been proposed to avoid the choice of the penalty factor , and simple to implement , and feasibility rules law mimicry physics optimization algorithm for solving constrained optimization the framework of the problem . After the introduction of the feasibility of the rules , the individual can be divided into feasible individuals and infeasible individuals , this paper studies the feasible individual and infeasible individuals and two infeasible individuals between different force rules , both from theoretical analysis and simulation experiments Description different feasibility rules proposed state physics - based optimization algorithm for solving constrained optimization problems , feasibility and effectiveness . In the algorithm , the feasible individual quality is a function of individual fitness value of a user-defined , and the the infeasible individual quality is a user-defined function related to the amount of constraint violations , and the quality of all feasible individuals are greater than infeasible individual quality . Feasible the individual quality function and infeasible individual choice of the mass function directly affects the performance of the algorithm , so this combination of the nature and characteristics of constrained optimization problems , respectively, for both quality function analysis of three types , a straight-line basis over the convex curve , concave curve construct a different mass function , three quality function instance simulation and experimental results show that the mass function of the concave curve algorithm better performance .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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