Computers and Operations Research
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Forecasting TAIFEX based on fuzzy time series and particle swarm optimization
Expert Systems with Applications: An International Journal
An efficient job-shop scheduling algorithm based on particle swarm optimization
Expert Systems with Applications: An International Journal
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
MTPSO algorithm for solving planar graph coloring problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mutual funds trading strategy based on particle swarm optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach
Expert Systems with Applications: An International Journal
Combining PSO and local search to solve scheduling problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A new ant colony algorithm for makespan minimization in permutation flow shops
Computers and Industrial Engineering
Solving the bi-objective personnel assignment problem using particle swarm optimization
Applied Soft Computing
Hybrid particle swarm optimization and convergence analysis for scheduling problems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
International Journal of Bio-Inspired Computation
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In this paper, a new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP). The objective of FSSP is to find an appropriate sequence of jobs in order to minimize makespan. Makespan means the maximum completion time of a sequence of jobs running on the same machines in flow-shops. By the RK encoding scheme, we can exploit the global search ability of PSO thoroughly. By the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of FSSP based on the proposed HPSO is far better than those based on GA [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851-861.] and NPSO [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851-861.], respectively.