  Multi-mode resource-constrained project scheduling problems (MRCPSPs) are
classified as NP-hard problems, in which a task has different execution modes
characterized by different resource requirements. Estimation of distribution
algorithm (EDA) has shown an effective performance for solving such real-world
optimization problems but it fails to find the desired optima. This paper
integrates a novel hybrid local search technique with EDA to enhance their
local search ability. The new local search is based on delete-then-insert
operator and a random walk (DIRW) to enhance exploitation abilities of EDA in
the neighborhoods of the search space. The proposed algorithm is capable to
explore and exploit the search mechanism in the search space through its outer
and inner loops. The proposed algorithm is tested and evaluated using benchmark
test problems of the project scheduling problem library PSPLIB. Simulation
results of the proposed algorithm are compared with the classical EDA
algorithm. The obtained results showed that the effectiveness of the proposed
algorithm and outperformed the compared EDA algorithm.
