Computational intelligence PC tools
Computational intelligence PC tools
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Computing Nash equilibria through computational intelligence methods
Journal of Computational and Applied Mathematics - Special issue: Selected papers of the international conference on computational methods in sciences and engineering (ICCMSE-2003)
Multi-objective aggregate production planning with fuzzy parameters
Advances in Engineering Software
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The activity-based aggregate production planning with capacity expansion in manufacturing systems
Computers and Industrial Engineering
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A multi-objective approach to the application of real-world production scheduling
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Particle swarm optimization (PSO) originated from bird flocking models. It has become a popular research field with many successful applications. In this paper, we present a scheme of an aggregate production planning (APP) from a manufacturer of gardening equipment. It is formulated as an integer linear programming model and optimized by PSO. During the course of optimizing the problem, we discovered that PSO had limited ability and unsatisfactory performance, especially a large constrained integral APP problem with plenty of equality constraints. In order to enhance its performance and alleviate the deficiencies to the problem solving, a modified PSO (MPSO) is proposed, which introduces the idea of sub-particles, a particular coding principle, and a modified operation procedure of particles to the update rules to regulate the search processes for a particle swarm. In the computational study, some instances of the APP problems are experimented and analyzed to evaluate the performance of the MPSO with standard PSO (SPSO) and genetic algorithm (GA). The experimental results demonstrate that the MPSO variant provides particular qualities in the aspects of accuracy, reliability, and convergence speed than SPSO and GA.