Bio-geographic optimization algorithm-oriented constraint handling method and device

Abstract

The invention provides a bio-geographic optimization algorithm-oriented constraint handling method and device. The method comprises: an improved migration step: capturing individuals selected from a target population; extracting target dimension elements of the selected individuals; based on an individual emigration rate, performing weighted fusion on the target dimension elements of the selected individuals according to a simulated binary crossover operator to generate new elements to be immigrated; an improved variation step: updating target sub populations according to the new elements to be immigrated and performing disturbance using Gaussian distribution to obtain an excellent target population; and a feasible constraint handing step: determining the excellent target population, determining optimal individuals and updating varied parameters by a differential evolution algorithm. By combining the information search capability of the differential evolution algorithm and the information utilization capability of the bio-geographic algorithm, the global optimization capability of the handling scheme is promoted and balanced.

Description

technical field [0001] The present invention relates to the field of biogeography optimization algorithms, more specifically, to a constraint processing method and device for biogeography optimization algorithms. Background technique [0002] The biogeography optimization algorithm is a new type of swarm intelligence algorithm proposed by Simon in 2008 based on the biogeography mathematical model. This algorithm simulates the migration mechanism of biological populations between different habitats to share population information and simulate habitats. The sudden phenomenon of the ground is used to explore new areas, so as to achieve the purpose of optimization. The biogeography algorithm has simple structure, few control parameters, fast convergence speed, and excellent information utilization ability, and has been applied in different fields of engineering. [0003] However, the biogeography optimization algorithm maximizes the use of the existing information in the popula...

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Problems solved by technology

[0003] However, the biogeography optimization algorithm maximizes the use of the existing information in the population through the migration operation, sacrificing the exploration of new areas; although it uses random mutation operations to obtain new elements, explore unknown spaces, and increase population diversity, Exploration is not strong enough

[0004] Therefore, the information exploration ability and information utilization ability of the biogeographical optimization algorithm are unbalanced.

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