Harris Hawks optimization algorithm based on multigroup and collaborative quantization
Harris Hawks optimization(HHO)algorithm has some advantages,but it still has some problems,such as insufficient balance between exploration and development abilities,which leads to slow convergence speed,low optimization accuracy and easy to fall into local optimization.Therefore,multi population is introduced and a multi energy strategy is proposed to simulate the escape process of two prey with different physical abilities,so that the two populations evolve in different directions to improve the searching ability of the algorithm.In addition,a cooperative quantization strategy is also proposed to avoid the algorithm from falling into local extremum in the early stage,and to improve optimization accuracy at the later stage of iteration.Finally,based on the optimization results,compared with some other classical or latest algorithms,the improved algorithm greatly improves the convergence speed and optimization accuracy,and has stronger ability to jump out of the local extremum.
Harris Hawks optimizationmulti populationmulti energy strategyquantizationcooperationswarm optimization
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