Aiming at the problems of the whale optimization algorithm(WOA)in the time difference of arrival location,such as the decrease of population diversity in the later iteration,which is easy to fall into local optimum and low location accuracy,a whale optimization algorithm was proposed based on immune selection and adaptive weight(IM-WOA).The maximum likelihood estimation method was used to get the target location function,and the immune mechanism was added to increase the diversity of the population,which could effectively generate new individuals and avoid the population falling into the local optimum.The adaptive inertia weight was introduced into the individual position update formula to better coordinate the global exploration and local development ability of the algorithm.It was used to solve the classical benchmark function and target location function and the experimental results showed that,compared with the WOA,AWOA,CSSOA,PIO and CASSA,IM-WOA algorithm had higher accu-racy and stability for most benchmark functions,and it had a higher location accuracy.
whale optimization algorithmtime difference of arrivalpopulation diversityimmune mech-anism
维普
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