An effective heuristic for flow shop problems with total flow time as criterion
Proceedings of the 15th annual conference on Computers and industrial engineering
A heuristic algorithm for mean flowtime objective in flowshop scheduling
Computers and Operations Research
Fast probabilistic modeling for combinatorial optimization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Comparison of heuristics for flowtime minimisation in permutation flowshops
Computers and Operations Research
Expert Systems with Applications: An International Journal
The equation for response to selection and its use for prediction
Evolutionary Computation
A combinatorial particle swarm optimisation for solving permutation flowshop problems
Computers and Industrial Engineering
A discrete differential evolution algorithm for the permutation flowshop scheduling problem
Computers and Industrial Engineering
Computers and Operations Research
Computers and Operations Research
Two ant-colony algorithms for minimizing total flowtime in permutation flowshops
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering
Hybridizing VNS and path-relinking on a particle swarm framework to minimize total flowtime
Expert Systems with Applications: An International Journal
Formulation of document summarization as a 0-1 nonlinear programming problem
Computers and Industrial Engineering
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
Hi-index | 0.00 |
In this paper, an Estimation of Distribution Algorithm (EDA) is proposed for permutation flow shops to minimize total flowtime. Longest Common Subsequence (LCS) is incorporated into the probability distribution model to mine good ''genes''. Different from common EDAs, each offspring individual is produced from a seed, which is selected from the population by the roulette method. The LCS between the seed individual and the best solution found so far is regarded as good ''genes'', which are inherited by offspring with a probability less than 100% to guarantee the population diversity. An effective Variable Neighborhood Search (VNS) is integrated into the proposed EDA to further improve the performance. Experimental results show that the inheritance of good ''genes'' obtained by LCS can improve the performance of the proposed EDA. The proposed hybrid EDA outperforms other existing algorithms for the considered problem in the literature. Furthermore, the proposed hybrid EDA improved 42 out of 90 current best solutions for Taillard benchmark instances.