Research On The Application Of Improved Wolf Pack Algorithm In Ceramic Firing Combinatorial Optimization Problem

The combinatorial optimization problem refers to the selection of a number of objects that satisfy the constraints from a set of objects,while optimizing a certain objective function,and finding the optimal solution that best meets the constraints of the original problem from all feasible solutions of the objective function.Typical combinatorial optimization problems include traveling salesman problem,backpack problem,boxing problem,and the common solution methods include branch-andbound method,dynamic programming method,etc.Wolf pack algorithm is a group intelligence optimization algorithm that uses the cooperative search of multiple wolf packs to solve the target problem by simulating the behavior of wolves tracking prey in nature.The algorithm has the advantages of strong global search capability,fast convergence,strong adaptability and good scalability,and has good successful applications in solving the backpack problem,UAV trajectory planning,workshop scheduling and many other fields.However,due to the shortcomings of wolf pack algorithm,the search process is easy to fall into the local optimal region,which reduces the optimization accuracy of the algorithm.In this paper,we analyze the behavioral characteristics of wolf pack predation process,improve its position updating mechanism and population initialization method,and test the performance of the improved wolf pack algorithm through simulation experiments.Meanwhile,this paper applies the improved wolf pack algorithm to the kiln use problem in the firing process of ceramic products,and verifies the feasibility and effectiveness of the proposed algorithm by two selected test cases of ceramic products.Compared with the traditional wolf pack algorithm and the other two optimization algorithms,the improved wolf pack algorithm has more obvious advantages in terms of solution accuracy and algorithm robustness.This paper contains the following main aspects of work:1.This paper first introduces the optimization problem and the basis of the wolf pack algorithm,and reviews its development history and current research status,and analyses the shortcomings of the wolf pack algorithm itself and its parameters.2.The problems in the behavioral mechanism of wolf packs during predation are analyzed,and an improved wolf pack algorithm based on an adaptive position update mechanism is proposed.By introducing incentive functions to construct dynamic wandering and running steps,the collective behavior of the wolf pack predation process is made to have adaptive position updating behavior,which is conducive to better solution exploration by the wolf pack in a wide range of problem spaces,thus improving the merit-seeking ability and speed of the wolf pack algorithm.Simulation tests on a set of standard test functions show that the proposed adaptive wolf pack algorithm has better convergence speed and better finding effect than other heuristic algorithms.3.In this paper,the combinatorial optimization problem in ceramic firing is considered as a multi-dimensional backpack problem and the corresponding mathematical model for combinatorial optimization in ceramic firing is developed and solved using the binary adaptive wolf pack algorithm.The proposed improved algorithm initializes the wolf pack population by introducing a backward learning strategy,which enhances the population diversity while also improving the global search capability of the algorithm.The experimental results,tested by three sets of simulation test cases,show that the binary adaptive wolf pack algorithm proposed in this paper is able to open up the solution space better than other heuristic algorithms,is not easily trapped in the local optimum region,and has better global search capability than other heuristic search algorithms.