Route planning method based on multi-target glowworm swarm algorithm

Abstract

The invention provides a route planning method based on a multi-target glowworm swarm algorithm and belongs to the technical field of route planning. The method includes: modeling a route planning problem, initializing the multi-target glowworm swarm algorithm, updating the glowworm position, determining a non-inferior solution set, updating an external archived file, judging whether a preset maximum iteration number is achieved or not, and determining a Pareto optimal route. The basic glowworm swarm algorithm is improved based on the Pareto dominant conception, and global searching and parallel computing capacities of the glowworm swarm algorithm are well used. Multiple route performance indexes are considered simultaneously during planning, a group of Pareto optimal solution sets can be obtained by planning once, and high flexibility is achieved. Further, the route panning method is different from traditional route planning methods for a single target and route planning methods of using a weighing method to convert multiple targets into the single target, and can better meet practical requirements of route planning.

Description

technical field [0001] The invention belongs to the technical field of path planning, and in particular relates to a path planning method based on a multi-objective firefly algorithm. Background technique [0002] Path planning is one of the core technologies for robots to realize autonomous navigation. Mobile robot path planning is to seek a movement trajectory of a mobile robot that connects the starting point to the end point and can avoid obstacles in the environment according to certain task requirements (the shortest path, the least consumption or the shortest use time, etc.), that is, optimal or suboptimal valid path. Traditional path planning usually only considers the optimization of a single measurement criterion, and finds an optimal path to the target state in the decision space according to an optimization criterion. However, in practical applications, it is usually necessary to simultaneously optimize multiple factors, such as path length, smoothness, robot e...

AI Technical Summary

Problems solved by technology

Different from the path planning problem under single-objective conditions, since each objective usually has conflicts, a solution is better for a certain objective, and may be worse for other objectives, thus resulting in multi-objective Path planning problems generally do not have a uniquely determined optimal path, but a set of optimal paths that cannot be compared

The successful application of FA in single-objective optimization problems illustrates the effectiveness of FA, but FA cannot be directly applied to multi-objective optimization problems

Theofanis et al. used FA to solve the multi-objective optimization problem of environmental and economic load dispatch in power systems (Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem, 2010). The optimization problem is transformed into a single-objective optimization problem, and then FA is used to optimize a single objective function, but this method can only get one solution at a time, and there is a problem of weight selection, which requires a strong prior knowledge of the problem itself , when the preference of the decision maker changes, the value of the weight should be changed accordingly, which is not in line with the essence of the multi-objective optimization problem

The invention patent with the application number 201110257951.3 designed a ship path planning method based on the firefly algorithm, but this method only considers the optimization of the total length of the path, and does not consider other performance indicators in the path planning problem

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