Constraint multi-target optimization method based on improved artificial bee colony algorithm

Abstract

The invention brings forward a constraint multi-target optimization method based on an improved artificial bee colony algorithm, for solving the defect of solving a multi-target optimization problem by use of a basic artificial bee colony optimization method. The constraint multi-target optimization method based on the improved artificial bee colony algorithm takes the material scheduling problem at a first emergency rescue phase as an application background and brings forward a food source initialization process integrated with reverse learning on the basis of the definition of a reverse solution for improving the quality of 50% of an initial solution. At the same time, for the purse of balancing the development capability and the exploration capability of an optimization process, the method integrates a reverse learning strategy and an extensive learning strategy into a honeybee search process so as to improve the search efficiency. The method constructs a non-linear deletion loss based many-to-many disposable consumption emergency material scheduling constraint multi-target optimization model, and embodiments are formed. Numerous test results indicate that compared to a conventional artificial bee colony optimization method, the method provided by the invention has the following advantages: more non-dominated leading-edge solutions are solved, the distribution on a solution space is wider and more uniform, and the solution are closer to a Pareto optimal solution.

Description

technical field [0001] The invention belongs to the field of artificial intelligence research, in particular to a constrained multi-objective optimization method based on an improved artificial bee colony algorithm. Background technique [0002] Optimization is the most frequently encountered problem in the fields of economics, science, engineering, and society, such as emergency decision-making, production scheduling, system control, and economic models. The practice of the above application fields shows that under the same conditions, the optimization technology can significantly improve the system efficiency, resources, and economic benefits. Moreover, the larger the scale of the processing problem, the more obvious the corresponding effect. Therefore, designing a more efficient and practical optimization algorithm has become a hot issue widely concerned by scholars in various fields at home and abroad. [0003] With the development of engineering technology and science,...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the shortcomings of artificial bee colony algorithm for optimization problems, the purpose of the present invention is to propose a constrained multi-objective optimization method based on improved artificial bee colony algorithm, which is mainly oriented to the new application field of emergency dispatch optimization , extend the artificial bee colony algorithm from single-objective optimization to multi-objective, and introduce strategies such as reverse initialization, reverse learning search, and extensive learning search to improve the efficiency of the optimization method

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