Method for solving SEVM model problem based on artificial bee colony algorithm

Abstract

The invention discloses a method for solving an SEVM model problem based on an artificial bee colony algorithm. The method comprises the steps of firstly, establishing a model; secondly, performing analgorithm flow of the artificial bee colony algorithm; thirdly, solving a random expected value function flow by using a random expected value estimation algorithm of random simulation, and finally,providing specific steps for solving the SEVM model problem by combining the artificial bee colony algorithm and random simulation and experimental data comparison of typical simulation examples. According to the invention, a solving method which is more effective than the classical genetic algorithm is provided for solving the SEVM model problem; the algorithm has the characteristics of strong global convergence and optimization capabilities, high solving speed, high solving quality and the like due to a search strategy combining local search and global search, the method fills the blank ofapplication research of the artificial bee colony algorithm in a stochastic programming problem, and has a certain practical value; and meanwhile, an idea is provided for efficient solving of other uncertain planning problems.

Description

technical field

[0001] The invention relates to the technical field of stochastic programming, in particular to a method for solving SEVM model problems based on an artificial bee colony algorithm. Background technique

[0002] Stochastic programming has shown great vitality in the application fields of optimal control, power dispatching, logistics management, signal and image processing, etc. However, due to the injection of random parameters in the solution of such problems, the solution algorithm of this kind of problems comes from Tsinghua University No significant progress has been made since Professor Liu Baoding of the University proposed to use the genetic algorithm to solve the problem. Due to some inherent shortcomings of the genetic algorithm itself, such as many control variables, slow convergence, complex genetic operation process, poor local search ability, and large amount of calculation, etc., more efficient SEVM model problem solving algorithms are still ext...

Examples

Embodiment 1

[0051] Solve the following SEVM single-objective model:

[0052]

[0053] where the random variable ξ 1 , ξ 2 , ξ 3 They are subject to uniform distribution U(1,2), normal distribution N(3,1) and exponential distribution EXP(4) respectively.

Embodiment 2

[0055] Solve the following SEVM goal program:

[0056]

[0057] In the formula, ξ 1 Subject to normal distribution N(1, 1), ξ 2 Subject to normal distribution N(2,1), ξ 3 Subject to normal distribution N(3,1), ξ 4 Obey the normal distribution N (4, 1).

[0058] For Example 1: Population size: 30; Number of random simulations: 3000; Number of iterations: 300; Number of runs: 1. In addition, the number of honey bees and follower bees is 15, and the threshold limit is 15.

[0059] For example 2, population size: 30; number of random simulations: 5000; number of iterations: 2000; number of runs: 1. In addition, the number of honey bees is 15, the number of follower bees is 15, and the threshold limit is 15.

[0060] The main configuration of the computer is: memory: 8GB; CPU frequency: 3.0GHz; operating system: win10; VisualC++6.0 to write and run the program.

[0061] In Example 1: the hybrid intelligent algorithm is executed once, and the obtained optimal solution and ...

technical field

[0001] The invention relates to the technical field of stochastic programming, in particular to a method for solving SEVM model problems based on an artificial bee colony algorithm. Background technique

[0002] Stochastic programming has shown great vitality in the application fields of optimal control, power dispatching, logistics management, signal and image processing, etc. However, due to the injection of random parameters in the solution of such problems, the solution algorithm of this kind of problems comes from Tsinghua University No significant progress has been made since Professor Liu Baoding of the University proposed to use the genetic algorithm to solve the problem. Due to some inherent shortcomings of the genetic algorithm itself, such as many control variables, slow convergence, complex genetic operation process, poor local search ability, and large amount of calculation, etc., more efficient SEVM model problem solving algorithms are still ext...

Examples

Embodiment 1

[0051] Solve the following SEVM single-objective model:

[0052]

[0053] where the random variable ξ 1 , ξ 2 , ξ 3 They are subject to uniform distribution U(1,2), normal distribution N(3,1) and exponential distribution EXP(4) respectively.

Embodiment 2

[0055] Solve the following SEVM goal program:

[0056]

[0057] In the formula, ξ 1 Subject to normal distribution N(1, 1), ξ 2 Subject to normal distribution N(2,1), ξ 3 Subject to normal distribution N(3,1), ξ 4 Obey the normal distribution N (4, 1).

[0058] For Example 1: Population size: 30; Number of random simulations: 3000; Number of iterations: 300; Number of runs: 1. In addition, the number of honey bees and follower bees is 15, and the threshold limit is 15.

[0059] For example 2, population size: 30; number of random simulations: 5000; number of iterations: 2000; number of runs: 1. In addition, the number of honey bees is 15, the number of follower bees is 15, and the threshold limit is 15.

[0060] The main configuration of the computer is: memory: 8GB; CPU frequency: 3.0GHz; operating system: win10; VisualC++6.0 to write and run the program.

[0061] In Example 1: the hybrid intelligent algorithm is executed once, and the obtained optimal solution and ...

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