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A Novel Evolutionary Algorithm for Multi-constrained Path Selection

Authors: Xiaogang Qi; Lifang Liu 0001; Sanyang Liu;

A Novel Evolutionary Algorithm for Multi-constrained Path Selection

Abstract

For the problem of multi-constrained path selection, a novel evolutionary algorithm named MCP_EA is proposed. Firstly, a novel coding technology named PNNC (Preceding Natural Number Coding, PNNC) is designed, and no circle exists on the path coded by PNNC. Secondly, a novel crossover operator called DCC operator (Dispersing Connection Crossover operator, DCC operator) is designed to guarantee the validity of the crossed paths and the diversity of the population. Thirdly, a novel mutation operator named selective mutation operator is proposed. Finally, the theoretical analysis proves that the algorithm converges to the satisfactory solution with probability 1.0. Extensive simulations show that the novel evolutionary algorithm outperforms the H_MCOP in performance for the problem, and is a promising algorithm for the problem with high performance.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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