Routing and scheduling in a flexible job shop by tabu search
Annals of Operations Research - Special issue on Tabu search
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
A fast taboo search algorithm for the job shop problem
Management Science
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Swarm intelligence
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Scheduling Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Approximation Algorithms for Flexible Job Shop Problems
LATIN '00 Proceedings of the 4th Latin American Symposium on Theoretical Informatics
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
Breeding swarms: a GA/PSO hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An effective hybrid genetic algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
Chaotic dynamic characteristics in swarm intelligence
Applied Soft Computing
Computers and Operations Research
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
Pattern Recognition Letters
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
Applying the clonal selection principle to find flexible job-shop schedules
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Multiagent scheduling method with earliness and tardiness objectives in flexible job shops
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarms for Linearly Constrained Optimisation
Fundamenta Informaticae
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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.