Research On The Hybrid Algorithm Of Bee Colony And Invasive Weed Optimization And Its Application

The invasive weed optimization (IWO) algorithm, which simulates the diffusionand reproductive behavior of weeds, is one of novel swarm intelligent optimal algo-rithms. On the basis of analyzing the optimal principle and related theories of IWO,this paper presents a hybrid algorithm of bee colony and invasive weed optimization(BCIWO), which introduces the evolutionary mechanism of the artificial bee colony(ABC) algorithm into the algorithm frame of IWO, for improving the optimal perfor-mance, broadening the practical application fields of IWO, and discussing the coher-ence of IWO and other intelligent optimal algorithms. Meanwhile, the optimal per-formance and the practical application of this new algorithm are studied thoroughly.The specific contents are as follows.(i) The basic frame of IWO and the evolutionary mechanism of ABC are intro-duced in detail. This paper has made a comprehensive analysis and demonstration tothe mathematical model, algorithm process, and convergence property of IWO andprovided its core steps and basic process. The role transformation and evolutionarypattern of ABC are also deeply researched.(ii) A hybrid strategy is designed by integrating the advantages of these two al-gorithms. After competing, every individual in the weed colony is mutated by em-ployed bees’ search behavior and the global best individual is mutated by onlookers’search method in this improved algorithm. The better result of mutation is used to re-place the original individual. The employed bees’ search behavior keeps the better in-dividuals in the swarm, while the onlookers’search method improves the optimal pre-cision.(iii) The hybrid algorithm of bee colony and invasive weed optimization is pro-posed. The mathematical model, algorithm process, convergence property, parameterssetting, and optimal performance of BCIWO are studied exhaustively. By analyzingthe impact of parameters setting on algorithm performance, the suggested values ofmain parameters are given. The superiority of the improved algorithm compared with the basic algorithms in the optimal performance, stability, operation time, and con-vergent speed are all verified by several benchmark test functions.(iv) This paper has applied IWO into two agricultural practical problems: irriga-tion schedule optimization and optimal design of planet transmission mechanism inagricultural machinery. The experimental results illustrate that BCIWO has a betteroptimal performance. Those provide a new thinking and method for the optimal prob-lems in agriculture.