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Improved backtracking search optimization algorithm based on multiple strategies

A backtracking search and optimization algorithm technology, applied in the field of evolutionary algorithms

Inactive Publication Date: 2019-12-20
XIAN UNIV OF TECH
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Problems solved by technology

[0004] However, neither the backtracking search optimization algorithm nor the above two improved algorithms can improve the convergence speed of the algorithm while ensuring the diversity of the population. Based on this, this paper studies an improved backtracking search optimization algorithm based on multi-strategy ( An Improved Backtracking Search Optimization Algorithm Based OnMulti-strategy, MBSA)

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  • Improved backtracking search optimization algorithm based on multiple strategies
  • Improved backtracking search optimization algorithm based on multiple strategies
  • Improved backtracking search optimization algorithm based on multiple strategies

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Embodiment Construction

[0085] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0086] The present invention is an improved backtracking search optimization algorithm based on multiple strategies, and its process is as follows figure 1 shown, follow the steps below:

[0087] Step 1, set the maximum number of iterations and initialize the population

[0088] Set the maximum number of iterations K and randomly generate the initial population pop i,j and historical population historical_pop i,j , which is generated according to the following formula:

[0089] pop i,j =rand×(up j -low j )+low j (1)

[0090] historical_pop i,j =rand×(up j -low j )+low j (2)

[0091] In the formula, i=1,2,3,...,N, j=1,2,3,...,D, N is the population number, D is the problem dimension, and rand is (0,1) uniform distributed random numbers, low j and up j are the lower and upper bounds of the variable, respectively;

[0092] Step ...

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Abstract

The invention discloses an improved backtracking search optimization algorithm based on multiple strategies. The improved backtracking search optimization algorithm comprises the following steps: 1, setting the maximum number of iterations and population initialization, 2, updating a historical population to generate a new historical population, and randomly sorting individuals in the new historical population; 3, fusing a variation strategy; 4, performing crossover operation to form a crossover population; 5, updating the niche population based on simulated annealing to generate a new initialpopulation; 6, comparing fitness values of individuals in the crossed population and the generated new initial population, reserving individuals with small fitness values, recording a current optimalsolution and an optimal individual, and updating the generated new initial population at the same time; and 7, judging the current number of iterations, if the current number of iterations is greaterthan the maximum number of iterations, outputting the current optimal solution, and if the current number of iterations is less than or equal to the maximum number of iterations, returning to the step 2 to perform the next iteration. According to the invention, the convergence rate of the algorithm is improved while the diversity of the population is ensured.

Description

technical field [0001] The invention belongs to the technical field of evolutionary algorithms, and relates to an improved backtracking search optimization algorithm based on multiple strategies. Background technique [0002] Backtracking Search Optimization Algorithm (BSA) is a new evolutionary algorithm proposed in 2013. The algorithm has a simple structure, few control parameters for group update, and is simple to implement. It plays an important role in some optimization problems. Compared with other optimization algorithms, the BSA algorithm not only uses the current information, but also uses the historical information to update the population, makes full use of the information in different periods in the evolution process, and can solve different numerical optimization problems well, and has been widely used in many engineering field. [0003] Although BSA has been applied in many engineering fields, it still has many deficiencies. First, the population update proce...

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Application Information

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IPC IPC(8): G06N3/00G06N3/12
CPCG06N3/006G06N3/126
Inventor 魏锋涛史云鹏
Owner XIAN UNIV OF TECH
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