Lower bounds and reduction procedures for the bin packing problem
Discrete Applied Mathematics - Combinatorial Optimization
BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem
Computers and Operations Research
On Approximation Methods for the Assignment Problem
Journal of the ACM (JACM)
Grouping genetic algorithms: an efficient method to solve the cell formation problem
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
CPGEA: a grouping genetic algorithm for material cutting plan generation
Computers and Industrial Engineering
Modular product design with grouping genetic algorithm: a case study
Computers and Industrial Engineering
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
Particle swarm optimization for integer programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A hybrid grouping genetic algorithm for the registration area planning problem
Computer Communications
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
A new grouping genetic algorithm approach to the multiple traveling salesperson problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A discrete particle swarm optimization algorithm for scheduling parallel machines
Computers and Industrial Engineering
League Championship Algorithm: A New Algorithm for Numerical Function Optimization
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A grouping genetic algorithm for the microcell sectorization problem
Engineering Applications of Artificial Intelligence
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
A new grouping genetic algorithm for the quadratic multiple knapsack problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Linear linkage encoding in grouping problems: applications on graph coloring and timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A hybrid grouping genetic algorithm for reviewer group construction problem
Expert Systems with Applications: An International Journal
Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm
Expert Systems with Applications: An International Journal
Finding Feasible Timetables Using Group-Based Operators
IEEE Transactions on Evolutionary Computation
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Many combinatorial optimization problems comprise a grouping phase (the grouping problem) in which the task is to partition a set of items into disjoint sets. Introduced in 1994, grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we adapt the structure of the well-known particle swarm optimization algorithm (PSO) for grouping problems. To propose the grouping version of the PSO algorithm, which is called GPSO algorithm, we develop new particle position and velocity updating equations which preserve the major characteristics of the original equations and are respondent to the structure of grouping problems. The new updating equations work with groups of items rather than items isolatedly. One of the main characteristics of the new equations is that they work in continuous space but their outcome is used in discrete space through a two phase procedure. Applications of GPSO algorithm are made to the single batch-machine scheduling problem and bin packing problem, and results are compared with the results reported by GGA. Computational results testify that our algorithm is efficient and can be regarded as a new solver for the wide class of grouping problems.