Hybrid Optimization Algorithm Of Ant Colony And Particle Swarm With Application

Due to machine constraint, flexible job shop scheduling is much more complex than traditional job shop scheduling and even more difficult to be solved in view of optimization. In this dissertation, a hybrid of ant colony and particle swarm optimization algorithms is used to solve flexible scheduling problems such as flexible job shop scheduling. In this dissertation, the main work and innovations are as follows:1. Flexible job shop scheduling problems with single objective are studied using the hybrid of ant colony and particle swarm optimization algorithms. First, A hybrid of ant colony and particle swarm optimization algorithms with master-slave structure is proposed based on the characteristic of flexible job shop scheduling problems to be solved. Then, an ant solution construction graph is presented and the transfer probability of ant between machines which can be selected to process job based on the extract graph of job processing machines for the ant colony algorithm at the master level. While, at the slave level, a decoding method is designed for particle based on the sequence of priority number in particle position matrix. Finally, the value of primary parameters in the hybrid algorithm is analyzed by experiments.2. Capacity constrained and multi-objective flexible job shop scheduling problems are studied using the hybrid of ant colony and particle swarm optimization algorithm. The computing and updating method of local heuristic information in ant transfer probability is redesigned based on the characteristics of the problems above-mentioned. Therefore, not only the capacity constraints can be dealt with, but also the minimum of total machine load and bottle-neck machine load can be realized by the ant colony optimization algorithm at master level.3. The mutli-mode resource-constrained project scheduling problems are studied using the hybrid of ant colony and particle swarm optimization algorithm. First, A hybrid of ant colony and particle swarm optimization algorithm with master-slave structure is proposed based on the characteristic of mutli-mode resource-constrained project scheduling problems to be solved. Then, the transfer probabilities of ant between tasks and between task-modes which can be selected to perform task based on the extract graph of task performing models for the ant colony algorithm at the master level. While, at the slave level, a decoding method including task-selecting probability is designed for particle based on the sequence of priority number in particle position vector . Finally, the value of primary parameters in the hybrid algorithm is analyzed by experiments based on the examples of project scheduling problem library (PSPLIB).