Sequencing in an assembly line with blocking to minimize cycle time
Operations Research
Computers and Operations Research
Flowshop scheduling with limited temporary storage
Journal of the ACM (JACM)
Minimizing Cycle Time in a Blocking Flowshop
Operations Research
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
The landscape adaptive particle swarm optimizer
Applied Soft Computing
Particle swarm optimization technique based short-term hydrothermal scheduling
Applied Soft Computing
Solving shortest path problem using particle swarm optimization
Applied Soft Computing
An improved particle swarm optimization algorithm for flowshop scheduling problem
Information Processing Letters
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
Scatter Search for the Point-Matching Problem in 3D Image Registration
INFORMS Journal on Computing
Discrete cooperative particle swarm optimization for FPGA placement
Applied Soft Computing
A novel particle swarm optimizer hybridized with extremal optimization
Applied Soft Computing
Chaotic maps based on binary particle swarm optimization for feature selection
Applied Soft Computing
Two-layer particle swarm optimization for unconstrained optimization problems
Applied Soft Computing
Application of particle swarm optimization to association rule mining
Applied Soft Computing
Microprocessors & Microsystems
A real-integer-discrete-coded particle swarm optimization for design problems
Applied Soft Computing
A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers
Applied Soft Computing
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A hybrid meta-heuristic algorithm for optimization of crew scheduling
Applied Soft Computing
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This paper proposes a discrete particle swarm optimization (DPSO) algorithm for the m-machine permutation flowshop scheduling problem with blocking to minimize the makespan, which has a strong industrial background, e.g., many production processes of chemicals and pharmaceuticals in chemical industry can be reduced to this problem. To prevent the DPSO from premature convergence, a self-adaptive diversity control strategy is adopted to diversify the population when necessary by adding a random perturbation to the velocity of each particle according to a probability controlled by the diversity of the current population. In addition, a stochastic variable neighborhood search is used as the local search to improve the search intensification. Computational results using benchmark problems show that the proposed DPSO algorithm outperforms previous algorithms proposed in the literature and that it can obtain 111 new best known upper bounds for the 120 benchmark problems.