Two-dimensional space multi-route planning method based on niche particle swarms

Abstract

The invention discloses a two-dimensional space multi-route planning method based on niche particle swarms. The method is applicable to ship route planning systems and includes firstly, modeling the environment under a rectangular coordinate system, coding particles by using a method that a planning region is equidistantly partitioned, and evaluating the particles in consideration of two factors of safety and economy; then, creating a new sub-swarm by using a niche identification technology and subjecting sub-swarm particles to intra-regional local optimization by using an isolation mechanism based niche particle swarm algorithm; and finally, performing regional partition to a main swarm, subjecting the main swarm to regional search by using a particle swarm algorithm of a cognitive pattern, performing interlacing operation to the center of the sub-swarm, and adding produced individualities to the main swarm. The two-dimensional space multi-route planning method based on the niche particle swarms is high in regional partitioning and local optimization capacities and capable of correctly and effectively planning multiple routes.

Description

technical field [0001] The invention belongs to the technical field of computational intelligence, and specifically relates to a two-dimensional space multi-path planning method based on niche particle swarms. Background technique [0002] Path planning is one of the key technologies to realize the intelligent navigation of ships. Its main task is to plan a safe navigation route and efficiently reach the predetermined position when the environment is known, partially known or unknown. In traditional path planning methods, such as artificial potential field method, visual graph method, and genetic algorithm, the evaluation of the quality of the path depends entirely on the given fitness value function to generate a path with optimal fitness. The performance of the optimal path is closely related to the evaluation function. However, a single optimal path is often not fully applicable in practical applications. [0003] In the actual path planning process, due to the complexi...

AI Technical Summary

Problems solved by technology

However, in practice, a single optimal path is often not fully applicable

[0003] In the actual path planning process, due to the complexity of the path planning problem itself, the complexity of the ship motion and the complexity of the external environment, any single evaluation function is not perfect, and it is difficult to use a unified fitness value function to All factors are taken into consideration; for a given fitness value function, in some cases there may be some paths with the same fitness, the traditional path planning algorithm needs to make a choice between two paths, and the preset choice strategy Difficult to reflect actual mission needs

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