路灯人影和离家出走改进的黑猩猩优化算法OA北大核心CSTPCD

To improve the solving accuracy and local extremum escape ability of chimpanzee optimization algo-rithm(ChOA),this paper proposes a street lamp shadow imaging and running away from home strategy for improved chimpanzee optimization algorithm(SSR-ChOA).Firstly,the population is initialized using SPM chaotic sequences to increase the uniformity of the initial population distribution.Secondly,this paper designs a new optical improvement method based on the physical phenomenon of human shadow changes under street lights at night:street lamp shadow imaging strategy.This strategy is used to optimize the problem of low development accuracy of ChOA algorithm.This paper designs a global optimization strategy called running away from home,which enables ordinary chimpanzee individuals to have stronger proactive exploration abilities.This strategy can help individual chimpanzees to jump out of local extrema caused by leader wrong judgement,avoiding stagnation and premature convergence in population search.This paper tests 25 benchmark test functions and CEC2014 test functions.The ChOA algorithm,4 different types of improved ChOA algorithms,and particle swarm optimization algorithm are compared.This paper analyzes the effectiveness of the improvement strategy.Finally,the application scenarios of towering electric towers and signal towers in the flight path of aerial drones are studied.This paper verifies the effectiveness of SSR-ChOA.Experimental results show that SSR-ChOA has significant differences compared with ChOA and 4 improved ChOA,and SSR-ChOA has significant advantages in optimization accuracy and stability.In terms of 3D path planning for UAV,the average total cost of SSR-ChOA is 3.06%lower than that of ChOA.