Population survival dynamics optimization method under environment pollution

Abstract

The invention provides a population survival dynamics optimization method under environment pollution, namely a PSDO-EP algorithm. A population survival dynamics theory under environment pollution is used, an environment system and the solution space of an optimization problem correspond to each other, pollution phenomena exist in the environment system, a plurality of populations live in the environment system, each population corresponds to a trial solution of the optimization problem, one feature of the population corresponds to a variable in the trial solution, the populations change all the time under the effect of environment pollution, strong populations which can resist environment pollution grow, weak populations stop growing, a population survival dynamics model under environment pollution is used for constructing evolution operators and achieving information interchange between environment and the populations and among the populations, during a population evolution process, the populations convert from one growing state to another growing state, searching on the optimal solution of the optimization problem of the populations is achieved, the PSDO-EP algorithm has the advantages of being strong in searching capacity, global convergence is achieved, and a solution is provided for the complex function optimization problem.

Description

technical field [0001] The invention relates to an intelligent optimization algorithm, in particular to a population survival dynamics optimization method under environmental pollution—PSDO-EP algorithm. Background technique [0002] Consider the function optimization problem: [0003] minf(X) [0004] [0005] Where: R n is n-dimensional Euclidean space; X=(x 1 , x 2 ,...,x n ) is an n-dimensional decision vector, variable x i (i=1, 2, ..., n) is a non-negative real number; S is a non-negative search space, also known as a solution space; f(X) is an objective function; g i (X)≥0 is the i-th constraint condition, i=1, 2,..., I, I is the number of inequality constraints; h i (X)=0 is the i-th equality constraint, i=1, 2,..., E, E is the number of equality constraints. Objective function f(X) and constraints g i (X), h i (X) No special restrictions are required. Therefore, traditional mathematical optimization methods based on function continuity and derivability...

AI Technical Summary

Problems solved by technology

[0008] Using the theory of population survival dynamics under environmental pollution to construct an algorithm that can solve function optimization problems has not been reported yet.

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