Quantum firefly search optimization method based on chaos self-adaption

Abstract

The invention belongs to the technical field of quantum computing, and particularly relates to a quantum firefly search optimization method based on chaos self-adaption, which comprises the following steps: taking the dimension of a solution of a problem to be solved as the dimension of the problem to be solved, taking the number of candidate solutions as the population scale, and setting quantum firefly parameters, the parameters at least comprise an objective function for judging the advantages and disadvantages of each candidate solution, an iteration termination condition, a solution allowable value range on each dimension and the number of sub-bits contained in quantum chromosomes; a quantum firefly initial population is generated based on quantum coding by using a chaotic sequence generated by chaotic mapping, and quantum firefly individuals are distributed to the whole search space; and neighborhood search and random disturbance are respectively acted on updating of the optimal individual and other individuals of the current population, and the optimal individual value is output through a loop iteration updating process. The search performance and stability of the quantum firefly algorithm can be improved, and application in actual scenes is facilitated.

Description

technical field [0001] The invention belongs to the technical field of quantum computing, in particular to a chaotic self-adaptive quantum firefly search optimization method. Background technique [0002] The firefly algorithm is a swarm intelligence optimization algorithm inspired by firefly emitting fluorescence to complete courtship, foraging and other behaviors in nature. Classic FA is inspired by the distinctive feature of fireflies: flickering light. In nature, these lights are used to attract partners and warn predators, and as the distance between two fireflies increases, the intensity of the light decreases and the attraction decreases. The firefly algorithm simulates this light-emitting mechanism and keeps moving towards more attractive individual fireflies. FA has some good characteristics, such as few parameters, simple evolution process, strong global search ability, etc., so it has been widely used in various fields and problems such as complex optimization s...

AI Technical Summary

Problems solved by technology

However, in the face of high-dimensional multi-extreme value optimization problems, QFA will show an obvious decline, mainly because the algorithm converges too fast and the diversity of the population decreases rapidly. If the current optimal value of the population is not the global optimal solution, the diversity The rapid decrease of will cause the algorithm to easily fall into the local optimum and cannot jump out, and the algorithm is premature and cannot obtain the expected results

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