Affiliations: School of Information Science and Technology, Dalian
Maritime University Dalian 116026, China. E-mail: lhb@dlut.edu.cn | Machine Intelligence Research Labs - MIR Labs,
http://www.mirlabs.org Auburn, Washington 98071 USA.
E-mail: ajith.abraham@ieee.org | School of Electromechanics and Materials Engineering,
Dalian Maritime University Dalian 116026, China. E-mail: wangzw@dlmu.edu.cn
Abstract: Swarm Intelligence (SI) is an innovative distributed intelligent
paradigm whereby the collective behaviors of unsophisticated individuals
interacting locally with their environment cause coherent functional global
patterns to emerge. In this paper, we model the scheduling problem for the
multi-objective Flexible Job-shop Scheduling Problems (FJSP) and attempt to
formulate and solve the problem using a Multi Particle Swarm Optimization
(MPSO) approach. MPSO consists of multi-swarms of particles, which searches for
the operation order update and machine selection. All the swarms search the
optima synergistically and maintain the balance between diversity of particles
and search space. We theoretically prove that the multi-swarm synergetic
optimization algorithm converges with a probability of 1 towards the global
optima. The details of the implementation for the multi-objective FJSP and the
corresponding computational experiments are reported. The results indicate that
the proposed algorithm is an efficient approach for the multi-objective FJSP,
especially for large scale problems.
