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Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers. [ABSTRACT FROM AUTHOR]

Cell formation plays an important role in the design of cellular manufacturing systems. Among many methods utilized to solve the cell formation problem, the similarity coefficient method is the most widely used owing to its high flexibility and low computational requirement. However, generalized similarity coefficients ignore material flow, which is closely related to operation sequence and repeated operations. Moreover, most similarity coefficient-based clustering algorithms focus on the number of inter-cell movements but disregard distinction of the movement effort. To overcome these limitations, this study improves the generalized similarity coefficient method to form part families. In addition, a new clustering algorithm is presented to assign machines to cells with minimum intensity of material inter-cell movement, which depends on the frequency, production volume and difficulty level of inter-cell movement. Experimental results demonstrate that the proposed method has superior sensitivity and effectiveness for solving the cell formation problem. [ABSTRACT FROM AUTHOR]

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation. [ABSTRACT FROM PUBLISHER]

Social networks are known for their decentralization and democracy. Each individual has a chance to participate and influence any discussion. Even with all the freedom, people's behavior falls under patterns that are observed in numerous situations. In this paper, we propose a methodology that defines and searches for common communication patterns in topical networks on Twitter. We analyze clusters according to four traits: number of nodes the cluster has, their degree and betweenness centrality values, number of node types, and whether the cluster is open or closed. We find that cluster structures can be defined as (a) fixed, meaning that they are repeated across datasets/topics following uniform rules, or (b) variable if they follow an underlying rule regardless of their size. This approach allows us to classify 90% of all conversation clusters, with the number varying by topic. An increase in cluster size often results in difficulties finding topological shape rules; however, these types of clusters tend to exhibit rules regarding their node relationships in the form of centralization. Most individuals do not enter large-scale discussions on Twitter, meaning that the simplicity of communication clusters implies repetition. In general, power laws apply for the influencer connection distribution (degree centrality) even in topical networks. [ABSTRACT FROM AUTHOR]

In this paper we focus on two scheduling problems in container terminal: (i) the Yard Truck Scheduling Problem (YTSP) which assigns a fleet of trucks to transport containers between the QCs and the storage yard to minimize the makespan, (ii) the integrated Yard Truck Scheduling Problem and Storage Allocation Problem (YTS–SAP) which extends the first problem to consider storage allocation strategy for discharging containers. Its object is to minimize the total delay for all jobs. The second model is improved to consider the truck ready time. Due to the computational intractability, two recently developed solution methods, based on swarm intelligence technique, are developed for problem solution, namely, particle swarm optimization (PSO) and bacterial colony optimization (BCO). As these two algorithms are originally designed for continuous optimization problems, we proposed a particular mapping method to implement them for YTSP and YTS–SAP, both of which are discrete optimization problems. Through comparing the PSO algorithms and BCO algorithm with GA by an experiment conducted on different scale instances, we can draw a conclusion that LPSO perform best in YTSP while BCO perform best in YTS–SAP. [ABSTRACT FROM AUTHOR]