Improved ant colony optimization based on particle swarm optimization and its application
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摘要: 針對現有基于粒子群參數優化的改進蟻群算法耗時較大的問題,提出了一種新的解決方案.方案中采用一種全局異步與精英策略相結合的信息素更新方式,同時合理減少蟻群算法被粒子群算法調用一次所需的迭代代數.對日本旭川垃圾場巡查機器人路徑規劃問題仿真求解的結果表明,與其他算法相比,該改進算法具有比較明顯的速度優勢.Abstract: This article introduces a novel algorithm to solve the large time-consuming problem of the existing improved ant colony optimization (ACO) based on particle swarm optimization (PSO). A new pheromone update method which combines the global asynchronous feature and elitist strategy was used in the algorithm. Moreover, the iteration steps of ACO invoked by PSO were reasonably reduced. The algorithm was applied to solve the path planning problem of landfill inspection robots in Asahikawa, Japan. It is shown that the algorithm has a better performance in search speed compared with other algorithms recently reported.
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