Mixed heuristic graph coloring method

Abstract

The invention discloses a hybrid heuristic graph coloring method, which integrates a probability learning strategy and a hybrid evolution algorithm, and performs hybrid evolution based on two coloring grouping schemes. And before hybrid purification, according to a conditional grouping strategy, a probability learning strategy is introduced according to a coloring distribution similarity degree of two coloring grouping schemes to carry out coloring dynamic adjustment, so that better balance exists between rapid convergence and population diversity maintenance, and while the relatively high solving efficiency of the image coloring grouping scheme is maintained, the image quality is improved. And the resolving precision is further improved, so that the resolving precision and the resolving efficiency of the graph coloring problem are better balanced and considered.

Description

technical field [0001] The invention relates to the technical field of graph data processing, in particular to a hybrid heuristic graph coloring method. Background technique [0002] As an important branch of computer science, graph (Graph, G) is one of the frameworks in data structure and algorithm. Graphs can be used to represent almost any type of structure or system, such as transportation networks, communication networks, circuit board wiring, path planning, chess playing, games, optimal processes, task assignment, and human interaction networks, among others. In the field of academic or engineering practice, a graph is generally regarded as an abstract network composed of "vertex" (Vertex), each vertex in the network can be connected to each other through "edge", which means that two vertices have associated. Therefore, a random unrighted and undirected Figure 1 Generally, it can be abbreviated as G=(V,E), where V={ν 1 ,ν 2 ,...ν n} is the set of vertices, is a...

AI Technical Summary

Problems solved by technology

The advantage of HEAD is that it can quickly converge the population; the disadvantage is that there are only two individuals so that the diversity of the population is insufficient, and it is difficult to jump out of the local optimum.

Although HEAD introduces elite individuals in the iterative process, for sparse graphs such as le450_15c and le450_15d, the algorithm can easily reach the convergence threshold during the iterative process, but the solution accuracy is insufficient, resulting in a suboptimal graph coloring grouping scheme solution, leading to a deadlock

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