Supply chain network design (SCND) is a complicated constrained optimization problem that plays a significant role in the business management. This article extends the SCND model to a large-scale SCND with uncertainties (LUSCND), which is more practical but also more challenging. However, it is difficult for traditional approaches to obtain the feasible solutions in the large-scale search space within the limited time. This article proposes a cooperative coevolutionary bare-bones particle swarm optimization (CCBBPSO) with function independent decomposition (FID), called CCBBPSO-FID, for a multiperiod three-echelon LUSCND problem. For the large-scale issue, binary encoding of the original model is converted to integer encoding for dimensionality reduction, and a novel FID is designed to efficiently decompose the problem. For obtaining the feasible solutions, two repair methods are designed to repair the infeasible solutions that appear frequently in the LUSCND problem. A step translation method is proposed to deal with the variables out of bounds, and a labeled reposition operator with adaptive probabilities is designed to repair the infeasible solutions that violate the constraints. Experiments are conducted on 405 instances with three different scales. The results show that CCBBPSO-FID has an evident superiority over contestant algorithms.

Supply chain management (SCM) is a significant and complex system in a smart city that requires advanced artificial intelligence (AI) and optimization techniques. The multiobjective supply chain configuration (MOSCC) in SCM is to set the optimal configurations for supply chain members to minimize both the cost of goods sold ( $CoGS$ ) and the lead time ( $LT$ ). Although some algorithms have been proposed for the MOSCC, they do not make the best use of the problem-related knowledge and cannot perform well on the large-scale instances with many members and configuration options. Therefore, this article proposes a multipopulation ant colony system with knowledge-based local searches (MPACS-KLSs). First, the multiobjective algorithm is based on the multiple populations for multiple objectives framework. Two ant colonies are used to separately minimize $CoGS$ and $LT$ , which helps to search in the biobjective space sufficiently. Second, with the considerations of the problem-related knowledge, a priority-based solution construction method, a rank-based heuristic strategy, and an objective-oriented global pheromone updating strategy are proposed. Third, to speed up the convergence, especially for large-scale MOSCC instances, two knowledge-based local searches are designed to minimize $CoGS$ and $LT$ of solutions, respectively. Exhaustive experiments are conducted on both the instances from the real life and the randomly generated instances with different problem scales. The results show that MPACS-KLS is superior to the contestant algorithms, especially on the large-scale MOSCC instances, which significantly extends the AI and optimization techniques in practical applications of the smart city.

Decomposition methods play a critical role in cooperative co-evolutionary algorithms (CCEAs) for solving large-scale optimization problems. Although some well-performing decomposition methods have been designed based on the interactions among variables (IaV), their grouping accuracy is still limited due to the poor performance on the overlapping problems and the computational roundoff errors of IaV in the implementation. To deal with these limitations, a graph-based deep decomposition (GDD) method is proposed to obtain more accurate grouping results, especially for the overlapping problems. On the one hand, the GDD mines the IaV information and obtains the minimum vertex separator of the interaction graph of variables, so as to group variables deeply and recursively. On the other hand, the GDD has the ability of fault tolerance to deal with the computational roundoff errors of IaV and can improve the grouping accuracy. For better experimental studies of overlapping problems, a novel overlapping function generator is designed with the random and complicate overlap type, and two new metrics are proposed to evaluate the grouping accuracy. Comprehensive experiments show that GDD can greatly improve the grouping accuracy and help CCEAs perform better than other existing algorithms, especially on the overlapping problems. In addition, the GDD is highly fault tolerant and can divide problems accurately even on the inaccurate IaV.

Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area.

Supply chain configuration (SCC) plays an important role in supply chain management. This paper focuses on a multi-objective SCC (MOSCC) problem for minimizing both the cost of goods sold and the lead time simultaneously. Some existing population-based methods use the evolution of a population to obtain the optimal Pareto set, but they are time-consuming. In this paper, an Efficient Local Search-based algorithm with rank (ELSrank) is designed to solve the MOSCC problem. Firstly, instead of use of population, two solutions (xA and xB) are generated by the greedy strategy, which have the minimal cost and the minimal time, respectively. They approximately locate in two sides of the Pareto front (PF). Secondly, with the consideration of the problem characteristics, a local search (LS) is proposed to find competitive solutions among the common neighborhood of two given solutions. If xA and xB are chosen to execute the proposed LS, solutions along the link path (the approximate PF) of xA and xB can be found. This way, the solutions along the whole PF can be found. The comparative experiments are conducted on six instances from the real-life MOSCC problems, and the results show that ELSrank performs better than other start-of-the-art algorithms, especially on the large scale problem instances.