Research Of Flow Shop Scheduling Problem Based On Hybrid Artificial Fish Swarm Algorithm

Nowadays, new intelligent optimization algorithms have been researched and applied widely in the process industry scheduling problem. These intelligent optimization algorithms have been developed by simulating certain physics and biology mechanisms, which provide new approaches to get the optimization of some complex systems. Artificial Fish Swarm Algorithm (AFSA in abbreviation) is one of the bionic group optimization algorithms simulating fish behavior in natural. It has been proved to have many advantages, such as insensitivity to the initial values and parameters varying, easy implementation, the abilities of parallel processing, and so on. However, during the actual application, some shortcomings exist in AFSA:low later convergence speed, easy to fall into local extremum, insufficient searching precision, and so on. In order to solve these problems, this paper presents a hybrid artificial fish swarm optimization algorithm based on the genetic algorithm (GA) and the simulated annealing algorithm (SA) by introducing the crossover and mutation mechanisms of GA and incorporating the local SA into the standard artificial fish algorithm, then discusses the flow shop scheduling problem of A variety conditions by this improved artificial fish optimization algorithm. In this paper, the main research contents include the following:(1) Based on the retained basic behavior of standard artificial fish algorithm, this paper introduces the mutation and crossover operators in GA and incorporates local SA. Improved artificial fish swarm improves the individual property, maintains the diversity of population, optimizes the group and improves the global convergence speed. At the same time it guarantees the global searching ability. The effectiveness of the algorithm is proved by the tests to functions and practical problem.(2) The improved artificial fish swarm optimization algorithms is applied in no waiting Flow Shop scheduling problem. Based on the mathematical model of no waiting Flow Shop scheduling problem, optimization strategy is presented based on the hybrid artificial fish swarm optimization algorithm with a detailed description of the solving steps. Finally, simulation test shows that the algorithm improves the efficiency and quality of the solution and proves the feasibility and effectiveness of the algorithm.(3) The improved artificial fish swarm optimization algorithms is applied in permutation Flow Shop scheduling problem. Based on the mathematical model of permutation Flow Shop scheduling problem, optimization strategy is presented based on the hybrid artificial fish swarm optimization algorithm with a detailed description of the solving steps. Finally, the feasibility and effectiveness of the algorithm is proved by the simulation test.