Copy number variation (CNV) is a remarkable manifestation of genomic structural variations that affect human health. However, CNV detection in low coverage and low purity data is one of the challenging issues. To fill this gap, a hybrid algorithm combining an improved whale optimization algorithm (IWOA) and backpropagation (BP) neural networks (hereafter called IWOABP) is developed for CNV detection. First, to enhance the precision of detection, the detectable categories for the gain and loss are respectively expanded to two types, where gain is divided into tand_gain and inte_gain, and loss is divided into hemi_loss and homo_loss. Then, IWOA is introduced to tune the weights and bias values of BP neural network, which can improve the BP neural network abilities to jump out of the local optimums. Next, to ensure the population diversity and the uniform distribution of solutions, a pooling mechanism and a migration search strategy are designed. In addition, to balance the exploitation and exploration abilities, three position update strategies based on an adaptive inertia-weight are used. Finally, to evaluate the detection performance of IWOABP, seven state-of-the-art detection methods are chosen to make detailed comparisons with the proposed algorithm. The results show that IWOABP has outstanding performance in sensitivity, precision, and Fl-score using both simulated and real data.

The distributed hybrid flow shop scheduling problem (DHFSP), which integrates distributed manufacturing models with parallel machines, has gained significant attention. However, in actual scheduling, some adjacent machines do not have buffers between them, resulting in blocking. This paper focuses on addressing the DHFSP with blocking constraints (DBHFSP) based on the actual production conditions. To solve DBHFSP, we construct a mixed integer linear programming (MILP) model for DBHFSP and validate its correctness using the Gurobi solver. Then, an advanced iterated greedy (AIG) algorithm is designed to minimize the makespan, in which we modify the Nawaz, Enscore, and Ham (NEH) heuristic to solve blocking constraints. To balance the global and local search capabilities of AIG, two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed. Additionally, each factory is mutually independent, and the movement within one factory does not affect the others. In view of this, we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective. Finally, two shaking strategies are incorporated into the algorithm to mitigate premature convergence. Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances, and experimental results illustrate that the makespan and the relative percentage increase (RPI) obtained by AIG are 1.0% and 86.1%, respectively, better than the comparative algorithms.

To meet the multi-cooperation production demand of enterprises, the distributed permutation flow shop scheduling problem (DPFSP) has become the frontier research in the field of manufacturing systems. In this paper, we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup times. To solve DPFSPs, significant developments of some metaheuristic algorithms are necessary. In this context, a simple and effective improved iterated greedy (NIG) algorithm is proposed to minimize makespan in DPFSPs. According to the features of DPFSPs, a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed algorithm. We compare the proposed algorithm with state-of-the-art algorithms, including the iterative greedy algorithm (2019), iterative greedy proposed by Ruiz and Pan (2019), discrete differential evolution algorithm (2018), discrete artificial bee colony (2018), and artificial chemical reaction optimization (2017). Simulation results show that NIG outperforms the compared algorithms.