An Extremum Optimization Method for Solving the Maximum Satisfaction Problem

An extreme value optimization and satisfying technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve the problems of inability to obtain optimal performance, lack of guidance in the optimization process of BE-EO algorithm, etc., and achieve good final optimization. Performance, easy to expand the application, the effect of broad application prospects

Inactive Publication Date: 2011-12-21
WENZHOU UNIVERSITY
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Problems solved by technology

However, the power-law distribution used by the extreme value optimization algorithm is very likely not the best evolutionary probability distribution, which will inevitably lead to th...

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  • An Extremum Optimization Method for Solving the Maximum Satisfaction Problem
  • An Extremum Optimization Method for Solving the Maximum Satisfaction Problem
  • An Extremum Optimization Method for Solving the Maximum Satisfaction Problem

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Embodiment

[0050] The extreme value optimization method based on the initial solution of the Bose-Einstein distribution and the extended evolution probability distribution can be applied to the maximum satisfaction problem for implementation. As mentioned in the background technology section, practical problems such as engineering plan scheduling, large-scale integrated circuit chip detection, and protein folding can be transformed into maximum satisfiability problems, and the phase transition point of the random MAX-3-SAT problem α c Instances around ≈4.267 correspond to the highest computational complexity, which is a great challenge for combinatorial optimization and is usually used as a test benchmark to measure the pros and cons of algorithms. Therefore, the present invention selects such examples from the internationally recognized database SATLIB as an embodiment. Here, satisfiable instances are labeled uf- n : m , such as "uf-50:218" indicates the number of variables in the in...

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Abstract

The invention discloses an extremum optimization method for solving the maximum satisfiability problem. The method defines local fitness and global fitness functions that meet the requirements of the extremum optimization method according to the principle of decomposability and linearity; uses Bose-Einstein probability distribution to construct optimization The initial solution; when selecting poor local variables for mutation, multiple probability distributions such as exponential distribution or mixed distribution are used as the extended evolution probability distribution. Compared with the traditional optimization method, the optimization process of this method is more instructive, and it is easier to obtain a near-optimal solution or even a global optimal solution. Its principle is simple and clear, with fewer adjustment parameters, simple design, and easy implementation.

Description

technical field [0001] The present invention relates to the field of combinatorial optimization and other research fields such as combinatorial optimization methods, in particular, relates to an extreme value optimization method (Extremal Optimization for Maximum Satisfiability Problems, EOSAT). Background technique [0002] Satisfiability Problem (SAT problem for short) is the first problem proved to be NP-Complete (NP-Complete), not only one of the core problems of theoretical computer science, but also in artificial intelligence, circuit design automation, It has important theoretical significance and application value in many fields such as engineering optimization and bioinformatics. As Gomes & Selman (2002) pointed out, theoretical problems and practical applications in many fields, including protein folding, engineering planning scheduling, and large-scale integrated circuit chip detection, can be transformed into SAT problems and their corresponding optimization pro...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 郑崇伟曾国强张正江
Owner WENZHOU UNIVERSITY
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