Study On Improved Monkey Optimization Algorithm And Its Application

The swarm intelligent optimization algorithm is a bionic evolutionary algorithm to solve complex optimization problems by learning from some evolution phenomena in nature and imitating the behavior of biological groups and individuals. The monkey algorithm is a new swarm intelligent optimization algorithm based on the climbing actions of monkeys. The algorithm has the advantages of simple structure, few parameters and easy to understand, and it has been applied to solve complex optimization problems at present. However, the algorithm has the shortcomings of low precision and easy to fall into the local optimal value when solving some practical problems. Thereby, it is important that the monkey algorithm is further studied.The main works of this thesis are as follows:1. The monkey algorithm based on Sigmoid function is given and the problem of logistics center location is solved. Because the behavior of monkey's climbing process is relatively simple,the performance of the algorithm is often affected. So,first,the initial position is generated by using chaotic variables in the initialization of the algorithm, so that the position of the initial monkey can be more evenly distributed and avoid falling into the local optimal value.Next, in the process of climbing of the algorithm, the decreasing factor based on Sigmoid function is used to replace the climbing length, which improves the accuracy of the algorithm.In the simulation, using the classical test function verifies the proposed algorithm, the results show that the algorithm has good global search ability; also, it is applied to the logistics center location problem, the experimental results show that the improved method is effective.2. The monkey algorithm based on the guiding factor is given and the 0₁ knapsack problem is solved. Because the behavior of monkey has certain blindness, the speed of optimization is leaded to slow down. So, firstly, the guiding factor is introduced in the climbing process of the algorithm, the algorithm can guide the monkeys to climb to the top of the mountain along with the increasing number of iterations, which can improve the local search speed of the algorithm. Then, the climbing length is replaced with the variable factor to further improve the accuracy of the algorithm. In the simulation, the results show that the proposed algorithm has better performance than other intelligent algorithms by using the classical test function, moreover, it is used to the 0₁ knapsack problem and the experiment results show that the improved method is feasible.