Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Design and Analysis of Experiments
Design and Analysis of Experiments
Computers and Operations Research
Differential evolution for solving multi-mode resource-constrained project scheduling problems
Computers and Operations Research
An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem
Applied Soft Computing
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In this paper, an estimation of distribution algorithm (EDA) is proposed to solve the multi-mode resource-constrained project scheduling problem (MRCPSP). In the EDA, the individuals are encoded based on the activity-mode list (AML) and decoded by the multi-mode serial schedule generation scheme (MSSGS), and a novel probability model and an updating mechanism are proposed for well sampling the promising searching region. To further improve the searching quality, a multi-mode forward backward iteration (MFBI) and a multi-mode permutation based local search method (MPBLS) are proposed and incorporated into the EDA based search framework to enhance the exploitation ability. Based on the design-of-experiment (DOE) test, suitable parameter combinations are determined and some guidelines are provided to set the parameters. Simulation results based on a set of benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EDA.