The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO) algorithm to solve DVRPs, utilizing a greedy strategy. Both migration operation and the butterfly adjusting operator only accept the offspring of butterfly individuals that have better fitness than their parents. To improve performance, a later perturbation procedure is implemented, to maintain a balance between global diversification and local intensification. The computational results indicate that the proposed technique outperforms the existing approaches in the literature for average performance by at least 9.38%. In addition, 12 new best solutions were found. This shows that this proposed technique consistently produces high-quality solutions and outperforms other published heuristics for the DVRP.

As the number of elderly people has increased worldwide, there has been a surge of research into assistive technologies to provide them with better care by recognizing their normal and abnormal activities. However, existing abnormal activity recognition (AAR) algorithms rarely consider sub-activity relations when recognizing abnormal activities. This paper presents an application of the Hidden State Conditional Random Field (HCRF) method to detect and assess abnormal activities that often occur in elderly persons’ homes. Based on HCRF, this paper designs two AAR algorithms, and validates them by comparing them with a feature vector distance based algorithm in two experiments. The results demonstrate that the proposed algorithms favorably outperform the competitor, especially when abnormal activities have same sensor type and sensor number as normal activities.

Community detection is a fundamental task in network analysis in that it can express the characteristics of individual behaviors and the relationships between individuals in complex networks, thus revealing the functional and structural properties of various networks. However, as networks become larger and larger, how to quickly and accurately detect the community structure of large-scale networks has become a huge challenge nowadays. In this paper, an efficient algorithm, called Local Search for Community Detection (LSCD), is proposed to solve the multi-objective community detection problem. In the algorithm, an iterated local search is performed to search for non-dominated solutions in the solution space. To search for higher quality non-dominated solutions, this paper employs a learning-based strategy to select nodes in each round of the search. The strategy learns from the historical movement of the nodes and changes their selection probability according to their importance. Moreover, to search the entire solution space, this paper proposes a local search strategy that restricts one objective with a bound for a certain round, as well as an adaptive bound update mechanism. Experimental results on synthetic and real-world networks show that the proposed algorithm outperforms several state-of-the-art algorithms in terms of multi-objective optimization performance and accuracy in detecting the community structure of complex networks, since it performs best on 44 instances out of 46 in terms of Modularity (Q) and 29 out of 39 in terms of Normalized Mutual Information (NMI). In addition, the results show that the proposed algorithm has a good ability to handle large-scale networks.

Data clustering is useful in a wide range of application areas. The Animal Migration Optimization (AMO) algorithm is one of the recently introduced swarm-based algorithms, which has demonstrated good performances for solving numeric optimization problems. In this paper, we presented a modified AMO algorithm with an entropy-based heuristic strategy for data clustering. The main contribution is that we calculate the information entropy of each attribute for a given data set and propose an adaptive strategy that can automatically balance convergence speed and global search efforts according to its entropy in both migration and updating steps. A series of well-known benchmark clustering problems are employed to evaluate the performance of our approach. We compare experimental results with k-means, Artificial Bee Colony (ABC), AMO, and the state-of-the-art algorithms for clustering and show that the proposed AMO algorithm generally performs better than the compared algorithms on the considered clustering problems.