A Hybrid Intelligent Optimization Method

Abstract

The invention discloses a hybrid intelligent optimization method, which belongs to the technical field of artificial intelligence and data mining. The present invention organically combines the genetic optimization algorithm with the bacterial foraging optimization algorithm, first obtains a preliminary optimal solution by using the breadth searchability of the genetic optimization algorithm, and uses it as the initial bacterial population in the later bacterial foraging algorithm, and makes full use of the bacterial foraging The chemotaxis, replication and dispersal operations of the eclipse algorithm continuously produce excellent individuals, and finally gradually converge to the optimal solution. On the basis of the above-mentioned technical scheme, the present invention improves respectively from four aspects of genetic selection operator, optimal combination point, bacterial chemotaxis and replication operation. Compared with the prior art, the present invention can improve the convergence speed and precision of the optimal solution set, and has wider 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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