Indoor evacuation simulating optimization method based on potential energy driving cellular ant colony algorithm

Abstract

The invention provides an indoor evacuation simulating optimization method based on a potential energy driving cellular ant colony algorithm. The method is a personnel evacuation behavior simulating optimization method based on colony intelligence, a two-dimensional cellular automaton mathematic model in accordance with an actual scene is mainly built, personnel evacuation behaviors are simulated by using the cellular ant colony algorithm, and personnel paths are judged and selected according to a potential energy evaluation criterion of an artificial potential energy field, so that the method is more consistent to a true scene evacuation rule, the evacuation efficiency is improved, and a reasonable evacuation scheme is provided.

Description

technical field

[0001] The invention belongs to the field of intelligent system modeling and optimization, in particular to an indoor evacuation simulation optimization method based on a potential energy-driven cellular ant colony algorithm. Background technique

[0002] The evacuation problem can be abstracted into a complex large-scale dynamic network according to the evacuation scene. The simplest and direct method is to use the traditional shortest path algorithm to solve it, but this algorithm is suitable for path optimization of small-scale static road networks, while for large-scale In a dynamic network, when the network scale increases, the complexity of the algorithm will increase rapidly. This traditional shortest path algorithm cannot meet the needs of the system due to the lack of computing power. Since the calculation time of the intelligent algorithm does not increase significantly with the increase of the network size, it is more suitable for solving the path ...

Examples

Embodiment Construction

[0073] 1. Establish a two-dimensional cellular automaton model for building evacuation:

[0074] A cellular automaton can be defined as a quadruple:

[0075] C=(D 2 ,S,N,f)

[0076] where D 2 is a 2-dimensional cell space; S is a set of finite state machines, and the state of a cell located on grid position r at time t can be expressed as:

[0077] S={S 1 (r,t),S 2 (r,t),...,S k (r,t)}

[0078] where S k (r, t) represents the kth state of the cell on the grid position r at time t; N is the neighborhood with r as the center cell, N={N 1 ,N 2 ,...,N n} is D 2 A limited subset of sequences.

[0079] f is the movement rule between the central cell r and its neighbors. Here, the definition of Moore-type neighbors is used, that is, the cells in the eight directions of the central cell’s upper, lower, left, right, upper left, lower left, upper right, and lower right are Its neighbors, at this time, the neighbor radius is also r=l, and this neighbor model is usually calle...

technical field

[0001] The invention belongs to the field of intelligent system modeling and optimization, in particular to an indoor evacuation simulation optimization method based on a potential energy-driven cellular ant colony algorithm. Background technique

[0002] The evacuation problem can be abstracted into a complex large-scale dynamic network according to the evacuation scene. The simplest and direct method is to use the traditional shortest path algorithm to solve it, but this algorithm is suitable for path optimization of small-scale static road networks, while for large-scale In a dynamic network, when the network scale increases, the complexity of the algorithm will increase rapidly. This traditional shortest path algorithm cannot meet the needs of the system due to the lack of computing power. Since the calculation time of the intelligent algorithm does not increase significantly with the increase of the network size, it is more suitable for solving the path ...

Examples

Embodiment Construction

[0073] 1. Establish a two-dimensional cellular automaton model for building evacuation:

[0074] A cellular automaton can be defined as a quadruple:

[0075] C=(D 2 ,S,N,f)

[0076] where D 2 is a 2-dimensional cell space; S is a set of finite state machines, and the state of a cell located on grid position r at time t can be expressed as:

[0077] S={S 1 (r,t),S 2 (r,t),...,S k (r,t)}

[0078] where S k (r, t) represents the kth state of the cell on the grid position r at time t; N is the neighborhood with r as the center cell, N={N 1 ,N 2 ,...,N n} is D 2 A limited subset of sequences.

[0079] f is the movement rule between the central cell r and its neighbors. Here, the definition of Moore-type neighbors is used, that is, the cells in the eight directions of the central cell’s upper, lower, left, right, upper left, lower left, upper right, and lower right are Its neighbors, at this time, the neighbor radius is also r=l, and this neighbor model is usually calle...

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