Research on optimization of area coverage strategy based on improved particle swarm algorithm
TL;DR
An improved particle swarm algorithm integrating greedy thought is proposed, aiming at the problems such as multi-constraints, multi-variables, and difficult high-dimensional solutions commonly existing in the optimization of area coverage strategy.
Abstract
Aiming at the problems such as multi-constraints, multi-variables, and difficult high-dimensional solutions commonly existing in the optimization of area coverage strategy, an improved particle swarm algorithm integrating greedy thought is proposed. Based on the standard particle swarm algorithm, aiming at its defects such as redundant iteration dimension, high computational complexity, and insufficient convergence efficiency in complex high-dimensional optimization scenarios, improvements are made from three aspects: objective function decomposition, common parameter fixation, and special case correction. First, using the greedy thought, the original high-dimensional objective function is split into multiple low-dimensional sub-problems to reduce the complexity of the search space; second, the common parameters in the strategy optimization are extracted and fixed to reduce repeated iteration and improve the solution efficiency; finally, aiming at the situation that the constraint equation may have no solution under special working conditions, a correction mechanism is designed to ensure the stability and robustness of the algorithm solution.