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The simulated annealing algorithm solves the 3SAT problem (C ++ implementation code) and annealing the 3sat
Reprint please indicate the source: http://blog.csdn.net/zhoubin1992/article/details/46453761 ------------------------------------------------------
1 SATProblem des

Problem Description:We have a base, longitude and latitude for (70,40). Let's say our plane is 1000 km/h.We sent a plane from the base to scout out all enemy targets before returning to the original base. In every enemy's eyeThe time spent on the aircraft (assuming that we can take a long cruise time) is not counted for the detection time of the punctuation.This is a traveling business problem. We gave the base number 1 in turn, the enemy target was numbered 2, 3, ...,101, finally our base repea

I, where D is a real symmetric matrix. The problem isFind a shortest path starting from and traveling through all the intermediate points to reach.Given the geographical coordinates (longitude and latitude), we must calculate the actual distance between two points. SetB A, the geographical coordinates of the two points are respectively ),(1 1Y x ,),(2 2Y x, over B A, the arc length of the big circle of two points is two points. Take the earth center as the coordinate origin O, take the equator

Pattern Recognition: Design and Implementation of simulated annealing algorithm, pattern recognition Annealing
This section aims to record the following learning and understanding the Simulated Annealing (SA) process. The

. Otherwise t decreases gradually, and turns to the 2nd step operation.The pseudo code of the simulated annealing algorithm is as follows:{Pick a solution x ' ∈n (x) randomly //randomly Select a solution in the pro domain X ' δf←f (x ')-f (x) ifδf

The purpose of this section is to record the following learning and mastering the process of simulated annealing (simulated annealing, abbreviated SA algorithm). Simulated annealing

ground. However, it gradually sobered up and jumped in the highest direction. This is simulated annealing.
Pseudo-code representations of simulated annealing are given below.
"Simulated ann

Simulation annealingIn 1982, Kirkpatrick introduced the annealing idea into the field of composite optimization and proposed an algorithm to solve the large-scale composite optimization problem, which is particularly effective for the NP full composite optimization problem. This is due to the solid annealing process, that is, first adding the temperature to a hig

following are the specific practices:1. Set the initial temperature T0, and randomly select a traverse path P (i) as the initial path, calculate its length L (P (i));2. Randomly generate a new traverse path P (i+1) to calculate its length L (p (i + 1));3. If L (p (i + 1)) 4. Repeat steps 1 and 2 until the temperature reaches the minimum value of tmin. There are a number of ways to generate a new traversal path, with 3 of them listed below:1. Randomly select 2 nodes, exchanging the order of the

Optimization Algorithm Starter series article catalog (in update):1. Simulated annealing algorithm2. Genetic AlgorithmsI. Mountain climbing algorithm (hill climbing)This paper introduces the algorithm of mountain climbing before simulate

temperature is T, there is a probability that the energy difference for the de's cooling is P (DE), expressed as:P (DE) = exp (de/(KT))where k is a constant, exp represents a natural exponent, and deAs the temperature t decreases, the P (DE) gradually decreases.We see the movement of a poor solution at once as a temperature-hopping process, and we accept such movement with probability P (dE).There is an interesting metaphor for the mountain climbing algorit

up and jumped in the highest direction. This is simulated annealing.
A pseudo-code representation of simulated annealing is given below.
three. Pseudo-code code for

-code representation of simulated annealing is given below.Three. Pseudo-code of simulated annealing algorithm/** J (y): The value of the evaluation function at status y * Y (i): Indica

-code representation of simulated annealing is given below.Three. Pseudo-code of simulated annealing algorithm 1:/* 2: * J (y): evaluation function value at status y 3: * Y (i

Tsp.data is as follows, the number of the first row indicates the number of cities, the 2nd to the last row, each row has two numbers, the city coordinates (planar Cartesian coordinates). For example:620 8016 8423 6662 9011 935 28finally, the reader compiles the execution program and observes the results of the analysis Output.References and recommended reading materials"1" for Hamilton and TSP please refer to the following two information to learn More:
On NP problem from Hamilton Pat

Introduction of Ant colony algorithm, genetic algorithm and simulated annealing algorithm
Exhaustive method
Enumerate all the possibilities and go on to get the best results. As figure one, you need to go straight from point A to point G to know that F is the highest (best

Optimization Algorithm Starter series article catalog (in update):1. Simulated annealing algorithm2. Genetic AlgorithmsI. Mountain climbing algorithm (hill climbing)This paper introduces the algorithm of mountain climbing before simulate

Optimization Algorithm Starter series article catalog (in update):1. Simulated annealing algorithm2. Genetic AlgorithmsI. Mountain climbing algorithm (hill climbing)This paper introduces the algorithm of mountain climbing before simulate

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