Bee Swarm Labor Division Method And Its Application In Task Allocation

Swarm intelligence refers to the intelligent phenomenon that emerges at the macro level when individuals follow simple rules of behavior and interact with each other in a population composed of many simple individuals.At present,the research on swarm intelligence is mostly focused on the research on swarm intelligence optimization algorithms,which is mainly reflected in the proposed new swarm intelligence optimization algorithms,such as the wolf swarm algorithm,the lion swarm algorithm,or the existing swarm intelligence optimization algorithm.Improvements,such as the improvement of classical ant colony algorithm,particle swarm optimization algorithm,genetic algorithm and other swarm intelligence optimization algorithms,to improve some performance indicators.This paper conducts related research on swarm intelligence division of labor represented by bee colony division of labor,and shifts the focus from swarm intelligence optimization algorithm to research on issues related to population coordination division of labor.Swarm intelligent division of labor is a flexible task allocation method,the purpose of which is to rationally use the limited resources in the population and give full play to the ability of each individual,thereby maximizing the benefits of the entire population.Based on the division of labor model,the population can reallocate existing resources as needed to adapt the population to different needs.In real life,there are many similar task allocation problems,and it is necessary to make full use of limited resources to obtain the best benefits.This article explores the performance of the bee swarm division of labor in the area coverage of swarm robots,multi-target search of swarm robots,and traffic signal timing in turn.The main research work is as follows:Based on the principle of activation-inhibition and combining the principle of stimulus response,a group robot area coverage algorithm is proposed.The algorithm can simultaneously consider the interaction between individual and individual,and between individual and environment.Effective information;the results of simulation experiments and discussion and analysis show that the algorithm can achieve collaboration among individuals and adjust the position distribution of robots,thereby improving regional coverage,reducing the number of repeated coverage,and improving the regional coverage efficiency of group robots.Based on the division of labor of the bee colony,a multi-objective search algorithm for swarm robots is proposed to perform fine-grained collaboration at the individual level to quickly form subgroups;coarse-grained collaboration at the subgroup level enables further collaboration between subgroups.Simulation experiment results and discussion analysis show that the algorithm can make full use of the information searched by individual robots and subgroups during the search process,and improve the efficiency of target search.Taking inhibitors as the leading role in the division of labor in the colony as a starting point,the inhibitor sharing mechanism and physiological age update process in worker bees in the colony are simulated,and a simplified activation-inhibition algorithm is proposed and applied to the task allocation problem.The simulation results show that the simplified activation-inhibition algorithm can reduce the average delay time and increase the capacity,and it has a good effect.