Improved Artificial Bee Colony Algorithm And Its Application

With the rapid development of science and technology,optimization requirements appear in many fields,such as power system scheduling,architecture design,machine learning,storage system optimization,etc.However,most optimization problems are multi-dimensional.Traditional optimization algorithms such as the conjugate gradient method,gradient descent method,grid search method,etc.can no longer meet the needs of solving complex problems.Designing efficient optimization algorithms for specific problems has become a research Hot spot.This article discusses the swarm intelligence algorithm and its application in the classification of ECG signals and automated storage systems,researches how to improve the performance of the algorithm,and creatively proposes an improved algorithm for the adaptive team collaboration mode.The research content is as follows:(1)An integrated learning ECG detection framework based on artificial bee colony algorithm tuning parameters is proposed.Grid search method is the most commonly used method of classifier tuning,but it is easy to fall into the local optimal solution.To improve the accuracy of the classifier more effectively,considering the strong exploration and development capabilities of the artificial bee colony algorithm,it is applied In the model building process of the integrated learning classifier,the highest classification accuracy is the optimization goal,and the adjustable parameters are optimized.(2)The application of multi-objective artificial bee colony algorithm in cargo location optimization is explored.To improve the efficiency and safety of the three-dimensional warehouse,a spatial distribution plan of the three-dimensional warehouse that considers path planning,different weights and the frequency of goods in and out is proposed.The optimization goal is to minimize the center of gravity of the shelf and maximize operating efficiency.Multi-objective artificial bee colony algorithm and multi-objective particle swarm algorithm are introduced to solve the problem of cargo space allocation.The results show that this strategy effectively solves the problem of warehouse storage space allocation optimization and realizes the efficient operation of the warehouse storage system.(3)To balance the search and convergence capabilities of the artificial bee colony algorithm,a new optimization framework is proposed and applied to the scheduling problem of airport cargo stations.The algorithm uses a group collaboration mechanism to guide the evolutionary direction of hire bees and follow bees in the next search.Experimental data show that the proposed method can produce the optimal solution to complex scheduling problems,improve the convergence accuracy of the solution,and prove that the performance of the improved algorithm is better than the original algorithm.