Abstracts:In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this work, we make a first attempt to incorporate the structure constraints into the whole solution set. Specifically, we propose to model such a multiobjective optimization problem as a set optimization problem with structure constraints. The structure constraints define some patterns that all the solutions are required to share. Such patterns can be fixed components shared by all solutions, specific relations among decision variables, and the required shape of the Pareto set. In addition, we develop a simple yet efficient evolutionary stochastic optimization method to learn the set model, which only requires a low computational budget similar to classic MOEAs. With our proposed method, the decision-makers can easily tradeoff the Pareto optimality with preferred structures, which is not supported by other MOEAs. A set of experiments on benchmark test suites and real-world application problems demonstrates that our proposed method is effective.

Abstracts:Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20 000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data.

Abstracts:The distributed hybrid flowshop scheduling problems (DHFSPs) widely exist in various industrial production processes, and thus have received widespread attention. However, the existing research mainly focuses on interfactory and intermachine collaboration, but ignores collaborative processing between jobs. Therefore, this article considers rescheduling DHFSP with job merging and reworking (DHFRPJM) and establishes a mixed-integer linear programming model. The objective is to minimize the makespan. Based on problem-specific knowledge, a decoding heuristic and initialization strategy considering job merging are designed. An acceleration strategy based on critical path is adopted to save the computational effort of the iterated greedy algorithm. A local search strategy based on a deep reinforcement learning algorithm further improves the performance of the algorithm. Experimental results based on actual production data show that the proposed algorithm outperforms other algorithms in closely related literature.

Abstracts:Multirobot task allocation (MRTA) is a challenging bi-level problem in the multirobot cooperative systems (MRCSs) and offers an effective method for addressing complex tasks. However, dynamic /uncertain environments can easily invalidate original schemes in practical MRTA decision-makings. Further, a nested structure in MRTA problems makes computational expensive. Therefore, the two main tasks are 1) finding a sufficient number of equivalent schemes for MRTA problems to adapt to task environments and 2) improving algorithm search efficiency in bi-level optimization problems. In this study, a multimodal multiobjective evolutionary algorithm (MMOEA) based on deep reinforcement learning (DRL) and large neighborhood search (LNS), called MMOEA-DL, is proposed to solve MRTA problems. In the MMOEA-DL, the task allocation problem, which is considered as the upper-level optimization problem, is solved using an improved MMOEA. The traveling salesman problem (TSP) regarded as the lower-level optimization problem is addressed via end-to-end method (i.e., DRL) and LNS. By leveraging the end-to-end method to obtain the results of the lower-level optimization, the bi-level optimization problem is effectively transformed into a single-level optimization problem. To demonstrate the performance of the proposed algorithm, 16 MRTA simulation scenarios and two actual MRTA scenarios with evenly and unevenly distributed task points are introduced in the present study. The simulation results verify that the MMOEA-DL not only provides decision-makers with expanded equivalent optimal schemes to address dynamic environments or unforeseen circumstances, but also offers a novel approach to solve the multimodal multiobjective bi-level optimization problem while saving computational costs.