Artificial Plant Optimization Algorithm Mixed Strategy Of Research And Application

Artificial Plant Optimization Algorithm (APOA) is a new swarm intelligence optimization algorithm, The framework of algorithm is based on the operator of photosynthesis,phototropism operator and apical dominance operator by simulation of plant growth,but because the local search of algorithm is poor,the paper cited two strategies to improve the performance of local search,in a certain range to improve the optimization efficiency of the algorithm.For the simplex algorithm is a typical local search algorithm, which integrated location information,it is use of reflection, extension, compression, etc. to carry out local search. In this paper, Artificial Plant Optimization Algorithm with Simplex algorithm use the strongest of the light intensity obtained for further optimization in order to enhance the ability of local searching algorithm and improve the convergence speed of artificial plant optimization.In order that Artificial Plant Optimization Algorithm applied in a class of optimization question with derivative of recursive information, the paper quoted Limited Storage quasi-Newton algorithm, which is use of gradient information, can improve local search performance.so, Artificial Plant Optimization Algorithm with the Limited Storage quasi-Newton algorithm is used to further strengthen the strongest light intensity of the local search performance, the new optimization algorithm using Artificial Plant Optimization Algorithm out of the local constraints, improve its ability to solve complex nonlinear problems.