Abstract User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information for recommendation tasks. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a neural inference method (NIGraphNet) by mining user social interest for item recommendation. It can unearth user latent topics combined with user relation learning. Specifically, we exploit a neural variational inference approach to learn the distributions between user interests and hidden topics. (We denote it as interest-topic distributions in the following.) Then, we adopt a unified graph-based training loss that jointly learns the hidden topics and user relations for item recommendation. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations is conducive to the user’s automatic language understanding.

The evolution of online shopping started when big players like Amazon began selling all types of merchandise. Customers understood the ease of shopping online, so the trend grew even stronger. It is therefore essential to conduct a study of online shopping usage and the perception of customers during COVID-19, especially in the grocery sector. In this study, approximately 28 respondents from 50 specifically targeted groups were surveyed, and data collection was undertaken through a structured questionnaire. The regression method was conducted to analyze the collected data. Additionally, 5 interviews were conducted to validate and support the findings. Customers definitely preferred online grocery shopping (OGS) services during COVID-19 due to safety, convenience, and government restrictions. The influential factors were very important in this case, like delivery times, good discounts, and the quality of products. Secondly, OGS services were more stable and alert during the pandemic situation, following the government's rules and restrictions. Customers were extremely satisfied with the safety precautions during COVID-19, the assistance provided through helplines for support, and the increased customer reach to make groceries as accessible as other reputable online departments.

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

The performance of differential evolution (DE) largely depends on its mutation strategy and control parameters. In this paper, we propose an adaptive DE (ADE) algorithm with a new mutation strategy DE/lbest/1 and a two-level adaptive parameter control scheme. The DE/lbest/1 strategy is a variant of the greedy DE/best/1 strategy. However, the population is mutated under the guide of multiple locally best individuals in DE/lbest/1 instead of one globally best individual in DE/best/1. This strategy is beneficial to the balance between fast convergence and population diversity. The two-level adaptive parameter control scheme is implemented mainly in two steps. In the first step, the population-level parameters Fp and CRp for the whole population are adaptively controlled according to the optimization states, namely, the exploration state and the exploitation state in each generation. These optimization states are estimated by measuring the population distribution. Then, the individual-level parameters Fi and CRi for each individual are generated by adjusting the population-level parameters. The adjustment is based on considering the individual's fitness value and its distance from the globally best individual. This way, the parameters can be adapted to not only the overall state of the population but also the characteristics of different individuals. The performance of the proposed ADE is evaluated on a suite of benchmark functions. Experimental results show that ADE generally outperforms four state-of-the-art DE variants on different kinds of optimization problems. The effects of ADE components, parameter properties of ADE, search behavior of ADE, and parameter sensitivity of ADE are also studied. Finally, we investigate the capability of ADE for solving three real-world optimization problems.

Vehicle routing problem with time windows (VRPTW) is a well-known NP-hard combinatorial optimization problem that is crucial for transportation and logistics systems. Even though the particle swarm optimization (PSO) algorithm is originally designed to solve continuous optimization problems, in this paper, we propose a set-based PSO to solve the discrete combinatorial optimization problem VRPTW (S-PSO-VRPTW). The general method of the S-PSO-VRPTW is to select an optimal subset out of the universal set by the use of the PSO framework. As the VRPTW can be defined as selecting an optimal subgraph out of the complete graph, the problem can be naturally solved by the proposed algorithm. The proposed S-PSO-VRPTW treats the discrete search space as an arc set of the complete graph that is defined by the nodes in the VRPTW and regards the candidate solution as a subset of arcs. Accordingly, the operators in the algorithm are defined on the set instead of the arithmetic operators in the original PSO algorithm. Besides, the process of position updating in the algorithm is constructive, during which the constraints of the VRPTW are considered and a time-oriented, nearest neighbor heuristic is used. A normalization method is introduced to handle the primary and secondary objectives of the VRPTW. The proposed S-PSO-VRPTW is tested on Solomon's benchmarks. Simulation results and comparisons illustrate the effectiveness and efficiency of the algorithm.

Research project selection is an important task for government and private research funding agencies. When a large number of research proposals are received, it is common to group them according to their similarities in research disciplines. The grouped proposals are then assigned to the appropriate experts for peer review. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the proposals cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore, rich information in the proposals' full text can be used effectively. Text-mining methods have been proposed to solve the problem by automatically classifying text documents, mainly in English. However, these methods have limitations when dealing with non-English language texts, e.g., Chinese research proposals. This paper presents a novel ontology-based text-mining approach to cluster research proposals based on their similarities in research areas. The method is efficient and effective for clustering research proposals with both English and Chinese texts. The method also includes an optimization model that considers applicants' characteristics for balancing proposals by geographical regions. The proposed method is tested and validated based on the selection process at the National Natural Science Foundation of China. The results can also be used to improve the efficiency and effectiveness of research project selection processes in other government and private research funding agencies.

Traffic signal control is a challenging task for traffic systems. As fuzzy logic is proved to be well suited to control some complex systems with uncertainties and human perception, it has been widely used to control the traffic signal in recent years. This paper proposes a novel fuzzy logic controller for signalizing modern roundabouts. Different from existing fuzzy traffic-signal controllers, the proposed controller consists of two fuzzy layers each of which has its own duty. According to the current traffic condition, one layer of the controller controls the phase sequence while the other layer determines the signal timing. By the cooperation of the two layers, the proposed controller is capable of immediately responding to the current traffic condition so as to reduce the vehicle delay or the queue length of waiting vehicles, as well as smoothing the traffic flows in order to reduce the risk of traffic jams. Simulation results prove the effectiveness of the proposed controller, for it can improve the traffic efficiency of the roundabout when compared with several existing controllers.

Project scheduling under uncertainty is a challenging field of research that has attracted an increasing attention in recent years. While most existing studies only considered the classical single-mode project scheduling problem with makespan criterion under uncertainty, this paper aims to deal with a more realistic and complicated model called the stochastic multi-mode resource constrained project scheduling problem with discounted cash flows (S-MRCPSPDCF). In the model, uncertainty is sourced from activity durations and costs, which are given by random variables. The objective is to find an optimal baseline schedule so that the project's expected net present value (NPV) of cash flows is maximized. In order to solve this intractable problem, an ant colony system (ACS) algorithm is designed. The algorithm dispatches a group of ants to build baseline schedules iteratively based on pheromones and an expected discounted cost (EDC) heuristic. In addition, because it is impossible to evaluate the expected NPVs of baseline schedules directly due to the presence of random variables, the algorithm adopts Monte Carlo (MC) simulations to evaluate the performance of baseline schedules. Experimental results on 33 instances demonstrate the effectiveness of the proposed scheduling model and the ACS approach.

Decisions for admission scheduling in hospitals are a class of optimization problems constrained by many factors. Instead of scheduling the admission of patients directly, this paper proposes a genetic algorithm (GA) designed for the optimization of a long-term admission strategy for the ophthalmology department in hospitals. For the optimization of admission strategy, we devise a coding scheme of strategies and define the objective functions for two objectives: efficiency and fairness. The proposed algorithm utilizes historical data of the hospital for evaluation of chromosomes. Experiments are conducted on several cases, and the strategy optimized by the proposed GA is compared with the first come first serve (FCFS) strategy and the greedy strategy. Experimental results show that strategies optimized by the proposed algorithm outperform FCFS and the greedy strategy.

Real coded genetic algorithms (RCGAs) have been widely studied and applied to deal with continuous optimization problems for years. However, how to improve the degree of accuracy so as to produce high quality solutions is still one of the main difficulties that RCGAs face with. This paper proposes a novel mutation scheme for RCGAs. The mutation operator is defined as a linear map in the space of chromosomes (in RCGAs each chromosome is a floating point vector). It operates on a whole chromosome instead of several single genes to produce the new chromosome. The linear map is represented by a randomly generated mapping matrix which satisfies some predefined constraints. By this way, the constraints restrict the mutations of genes on a same chromosome as a whole. RCGA with the proposed mutation scheme is tested on 16 benchmark functions. Results demonstrate that the proposed scheme not only improves the solution accuracy that RCGA can obtain, but also presents a very fast convergence speed. The linear map-based mutation scheme has a bright future to improve RCGAs.

Research into maximizing the network lifetime is one of the most significant and challenging areas in wireless sensor networks (WSNs). By arranging sensors and sinks to realize target coverage and network connectivity respectively, an efficient schedule of sensors and sinks can prolong the network lifetime. However, the arrangements of sensors and sinks correlate with each other because each sensor needs to send its data to a sink, making the problem of finding the optimal schedule difficult. Instead of using a single process to optimize the entire schedule of sensors and sinks, this paper proposes a scheduling method which uses two separate processes to schedule operations of sensors and sinks respectively. The first process organizes sensors in the network into disjoint sets, with each set being able to fully cover the targets. Based on the arrangement of sensors, a novel genetic algorithm (GA) is adopted in the second process to allocate sinks to each set of sensors. When the number of full cover sets that ensure both connectivity of sensors to sinks and connectivity of the network composed of sinks is maximized, a schedule that maximizes the network lifetime can be obtained. The proposed method has been applied to a number of WSN cases. Results demonstrate that the method is effective and efficient in prolonging the lifetime of WSNs.

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficiency.

When a sensor network is deployed, it is typically required to support multiple simultaneous missions. Schemes that assign sensing resources to missions thus become necessary. In this article, we formally define the sensor-mission assignment problem and discuss some of its variants. In its most general form, this problem is NP-hard. We propose algorithms for the different variants, some of which include approximation guarantees. We also propose distributed algorithms to assign sensors to missions which we adapt to include energy-awareness to extend network lifetime. Finally, we show comprehensive simulation results comparing these solutions to an upper bound on the optimal solution.

The multimode resource-constrained project-scheduling problem with discounted cash flows (MRCPSPDCF) is important and challenging for project management. As the problem is strongly nondeterministic polynomial-time hard, only a few algorithms exist and the performance is still not satisfying. To design an effective algorithm for the MRCPSPDCF, this paper proposes an ant colony optimization (ACO) approach. ACO is promising for the MRCPSPDCF due to the following three reasons. First, MRCPSPDCF can be formulated as a graph-based search problem, which ACO has been found to be good at solving. Second, the mechanism of ACO enables the use of domain-based heuristics to accelerate the search. Furthermore, ACO has found good results for the classical single-mode scheduling problems. But the utility of ACO for the much more difficult MRCPSPDCF is still unexplored. In this paper, we first convert the precedence network of the MRCPSPDCF into a mode-on-node (MoN) graph, which becomes the construction graph for ACO. Eight domain-based heuristics are designed to consider the factors of time, cost, resources, and precedence relations. Among these heuristics, the hybrid heuristic that combines different factors together performs well. The proposed algorithm is compared with two different genetic algorithms (GAs), a simulated annealing (SA) algorithm, and a tabu search (TS) algorithm on 55 random instances with at least 13 and up to 98 activities. Experimental results show that the proposed ACO algorithm outperforms the GA, SA, and TS approaches on most cases.

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.

This paper proposes an orthogonal learning particle swarm optimization (OLPSO) by designing an orthogonal learning (OL) strategy through the orthogonal experimental design (OED) method. The OL strategy takes the dimensions of the problem as the orthogonal experimental factors. The levels of each dimension (factor) are the two choices of the personal best position and the neighborhood's best position. By orthogonally combining the two learning exemplars, the useful information can be discovered, preserved and utilized to construct an efficient exemplar to guide the particle to fly in a more promising direction towards the global optimum. The effectiveness and efficiency of the OL strategy is demonstrated on a set of benchmark functions by comparing the PSOs with and without OL strategy. The OL strategy improves the PSO algorithm in terms of higher quality solution and faster convergence speed.

Ant colony optimization (ACO) has been widely and successfully applied to NP-hard combinatorial optimization problems for its strong searching ability and robustness. Recently, several extended ACO algorithms have also been proposed to deal with continuous optimization problems. However, the ACO algorithms always have slow convergence speed and encounter premature convergence in engineering applications. This paper proposes a novel adaptive parameter control method for continuous ACO algorithms. Clustering analysis is used to judge the optimization state of the algorithm and the flexible adjustment of the parameters is based on these optimization states during the training process. As an example, the adaptive control method is used to improve the performance of the continuous orthogonal ant colony (COAC). Experimental results demonstrate that the clustering-based adaptive parameters control scheme contributes to both faster convergence speed and higher solution accuracy. The proposed adaptive control method has great practical value and bright prospect.

Computer-assisted testing systems are promising in generating tests efficiently and effectively for evaluating a person's skill. This paper develops a novel intelligent testing system for both teachers and students. Based on the browser/server structure, the proposed testing system comprises a question bank and five modules, offering the features of self-adaptation, reliability, and flexibility for generating parallel tests with identical test ability. The core of the developed system is the ant-colony-optimization-based test composition (ACO-TC) method, which aims at generating high-quality tests for examinations and satisfying multiple requirements. As an advanced computational intelligence algorithm, the proposed ACO-TC method uses a colony of ants to select appropriate questions from a question bank to construct solutions. Pheromone and heuristic information is designed for facilitating the ants' selection. The system is analyzed by composing tests in different situations. The generated tests not only match the expected total completion time, the concept proportions, the average difficulty, and the score proportions of different question types, but also have high average discrimination degrees of questions. The experimental results also show that the system can always generate high-quality tests from question banks with various sizes.