Hybrid intelligent optimization method

Abstract

The invention discloses a hybrid intelligent optimization method and belongs to the technical field of artificial intelligence and data mining. A genetic optimization algorithm and a bacterial foraging optimization algorithm are organically combined by the hybrid intelligent optimization method. First, a preliminary superior solution is obtained through the breadth searching capacity of the genetic optimization algorithm and serves as an initial bacterial population in the posterior bacterial foraging algorithm, by fully utilizing chemotaxis, duplicating and dispersing operation of the bacterial foraging algorithm, excellent individuals are generated continuously, and finally the solution converges towards the optimal solution. On the basis of the technical scheme, further improvements are made in the four aspects of genetic selection operators, optimal joint pints, bacterial chemotaxis and duplicating operation. Compared with the prior art, the convergence speed and the convergence precision of an optimal solution set can be improved, and the hybrid intelligent optimization method is more extensive in applicability.

Description

technical field [0001] The invention relates to an optimization method, in particular to a hybrid intelligent optimization method, and belongs to the technical field of artificial intelligence and data mining. Background technique [0002] Since the 1970s, intelligent optimization algorithms have been researched and applied in various fields due to their efficient optimization performance. Especially for large-scale optimization problems, the traditional linear programming-based method takes a long time to obtain a better solution, while the intelligent optimization algorithm can obtain the optimal solution in a short period of time, so the intelligent optimization algorithm has not only been recognized by the industry , and has become a research hotspot among scholars. At present, intelligent algorithms can be divided into two categories: traditional intelligent optimization algorithms and hybrid intelligent optimization algorithms. [0003] In 1975, a book "Adaptation in...

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Problems solved by technology

[0004] Because a single intelligent optimization algorithm has its own shortcomings, such as the genetic optimization algorithm is easy to fall into local optimum in the early stage, and the search is slow in the later stage; the bacterial foraging algorithm cannot use integer coding, etc., and some intelligent optimization algorithms cannot be well adapted to a certain class applications, such as large-scale dynamic resource scheduling problems, etc., so hybrid optimization algorithms came into being. In view of the advantages and disadvantages of traditional intelligent optimization algorithms, a large number of scholars have proposed hybrid optimization algorithms

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