Considerable growth in container transportation has been witnessed over the past two decades with an indispensable need for optimization. Several studies tackling problems related to container operations have been reported by the academic world since then. These studies mainly focus on operations performed either onboard container ships or at the port side. Container operations onboard ships aim to optimize the proper assignment of containers to the corresponding storage areas on container ships to gain improvement in cost, time, and stability. In literature, this problem is defined as Container Stowage Planning (CSP) problem. This problem is studied either by being decomposed into sub-problems or by being approached holistically. This paper provides a detailed survey of studies that focus on addressing the CSP problem. Specifically, we cover the two main sub-problems of CSP, namely the bay planning problem and the slot planning problem. Additionally, we discuss the current state-of-the-art solution approaches that aim to achieve efficient and effective container placement.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data. To alleviate the difficulties, we propose a co-evolutionary transfer learning (CETL) method for predicting the demands of a set of medical materials, which is important in COVID-19 prevention and control. CETL reuses material demand knowledge not only from other epidemics, such as severe acute respiratory syndrome (SARS) and bird flu but also from natural and manmade disasters. The knowledge or data of these related tasks can also be relatively few and imbalanced. In CETL, each prediction task is implemented by a fuzzy deep contractive autoencoder (CAE), and all prediction networks are cooperatively evolved, simultaneously using intrapopulation evolution to learn task-specific knowledge in each domain and using interpopulation evolution to learn common knowledge shared across the domains. Experimental results show that CETL achieves high prediction accuracies compared to selected state-of-the-art transfer learning and multitask learning models on datasets during two stages of COVID-19 spreading in China.

Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance; after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions; when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.

Clustering, as an important part of data mining, is inherently a challenging problem. This article proposes a differential evolution algorithm with adaptive niching and k -means operation (denoted as DE_ANS_AKO) for partitional data clustering. Within the proposed algorithm, an adaptive niching scheme, which can dynamically adjust the size of each niche in the population, is devised and integrated to prevent premature convergence of evolutionary search, thus appropriately searching the space to identify the optimal or near-optimal solution. Furthermore, to improve the search efficiency, an adaptive k -means operation has been designed and employed at the niche level of population. The performance of the proposed algorithm has been evaluated on synthetic as well as real datasets and compared with related methods. The experimental results reveal that the proposed algorithm is able to reliably and efficiently deliver high quality clustering solutions and generally outperforms related methods implemented for comparisons.

The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.

The use of unmanned aerial vehicles (UAVs) is becoming more commonplace in search-and-rescue tasks, but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate search modes. In this paper, we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive memetic algorithm that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics, non-adaptive memetic algorithms, and adaptive memetic algorithms on a wide set of problem instances.

Power transmission networks are vulnerable to natural or man-made disasters, and it is of critical importance to efficiently restore damaged power supply in disaster-affected areas. A large-scale damaged transmission network can contain many faults that are initially uninspected/unlocated. Using unmanned aerial vehicles (UAVs) to inspect these faults can significantly improve the efficiency of subsequent restoration performed by human operators. Such a cooperative human-UAV scheduling problem is highly complex due to the correlation between UAV schedules and human-team schedules. In this article, we propose a cooperative evolutionary algorithm that simultaneously evolves two populations, one of UAV scheduling solutions (U-solutions) and the other of human-team scheduling solutions (H-solutions), which cooperate by determining a best matching U-solution for each H-solution and evaluating U-solutions based on a surrogate objective function that is iteratively improved by feedback from H-solutions. Our algorithm exhibits significant performance advantages over the state-of-the-arts on various test instances and an application to transmission network restoration in the 2017 Jiuzhaigou earthquake.

Our previous study has constructed a deep learning model for predicting gastrointestinal infection morbidity based on environmental pollutant indicators in some regions in central China. This article aims to adapt the prediction model for three purposes: 1) predicting the morbidity of a different disease in the same region; 2) predicting the morbidity of the same disease in a different region; and 3) predicting the morbidity of a different disease in a different region. We propose a tridirectional transfer learning approach, which achieves the abovementioned three purposes by: 1) developing a combined univariate regression and multivariate Gaussian model for establishing the relationship between the morbidity of the target disease and that of the source disease together with the high-level pollutant features in the current source region; 2) using mapping-based deep transfer learning to extend the current model to predict the morbidity of the source disease in both source and target regions; and 3) applying the pattern of the combined model in the source region to the extended model to derive a new combined model for predicting the morbidity of the target disease in the target region. We select gastric cancer as the target disease and use the proposed transfer learning approach to predict its morbidity in the source region and three target regions. The results show that, given only a limited number of labeled samples, our approach achieves an average prediction accuracy of over 80% in the source region and up to 78% in the target regions, which can contribute considerably to improving medical preparedness and response.

The use of unmanned aerial vehicles (UAVs) for target searching in complex environments has increased considerably in recent years. The numerous studies on UAV search methods have been reported, but few have been conducted on collaborative human-UAV search which is common in many applications. In this paper, we present a problem of collaborative human-UAV search for escaped criminals, the aim of which is to minimize the expected time of capture rather than detection. We show that our problem is much more complex than the problem of pure UAV search. The difficulty of our problem is further increased by the fact that criminals will attempt to avoid detection and capture. To solve the problem, we propose a hybrid evolutionary algorithm (EA) that uses three evolutionary operators, namely, comprehensive learning, variable mutation, and local search, to efficiently explore the solution space. The experimental results demonstrate that the proposed method outperforms some well-known EAs and other popular UAV search methods on test instances. An application of our method to a real-world operation took 311 min to capture a criminal who had escaped for over three days, validating its practicability and performance advantage. This paper provides a good basis for promoting the application of EAs to a wider class of man-machine collaboration scheduling problems.

Due to the uncertainty of diseases, traditional approaches of drug procurement planning in hospitals often cause drug overstocking or understocking, which can have strong negative effects on healthcare services. This paper proposes a big-data driven approach, which uses a deep neural network to predict morbidities of acute gastrointestinal infections based on a huge amount of environmental data, and then constructs an optimization problem of drug procurement planning for maximizing the expected therapeutic effect on the predicted cases. The problem is solved by an efficient heuristic optimization algorithm. Computational experiments demonstrate the performance advantages of both the deep learning model and the heuristic algorithm over existing ones, and two real case studies in Central China show that the average prediction error of our approach is only 8% and the estimated recovery rate reaches 99%, much better than the currently used method. Our approach can also be extended for many other medical resource planning problems.

As a relatively new metaheuristic in swarm intelligence, fireworks algorithm (FWA) has exhibited promising performance on a wide range of optimization problems. This paper aims to improve FWA by enhancing fireworks interaction in three aspects: 1) Developing a new Gaussian mutation operator to make sparks learn from more exemplars; 2) Integrating the regular explosion operator of FWA with the migration operator of biogeography-based optimization (BBO) to increase information sharing; 3) Adopting a new population selection strategy that enables high-quality solutions to have high probabilities of entering the next generation without incurring high computational cost. The combination of the three strategies can significantly enhance fireworks interaction and thus improve solution diversity and suppress premature convergence. Numerical experiments on the CEC 2015 single-objective optimization test problems show the effectiveness of the proposed algorithm. The application to a high-speed train scheduling problem also demonstrates its feasibility in real-world optimization problems.

Early warning is crucial for preventing industrial accidents and mitigating damage, but current methods are often time-consuming, error-prone, and incompetent to deal with uncertainty. This paper presents a fuzzy deep neural network for early warning of industrial accidents, which equips the classical deep denoising autoencoder (DDAE) model with Pythagorean-type fuzzy parameters in order to enhance the model's representation ability and robustness. To efficiently train the fuzzy deep model, we propose a hybrid algorithm combining Hessian-free optimization and biogeography-based optimization metaheuristic to balance global search and local search. Experiments on datasets from several industrial zones in China show that the proposed Pythagorean-type fuzzy DDAE (PFDDAE) can achieve much higher accuracy of accident risk classification than the classical DDAE and the fuzzy DDAE using regular fuzzy parameters, and the proposed hybrid learning algorithm exhibits significant performance advantage over some other learning algorithms in training PFDDAE. In particular, a test on the 2014 Kunshan aluminum dust explosion accident shows that the deep learning model would be very likely to prevent the accident if it was adopted in advance.

Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

Automatic data clustering, whose goal is to recover the proper number of clusters as well as appropriate partitioning of data sets, is a fundamental yet challenging problem in unsupervised learning. In this paper, adaptive multisubpopulation competition (AMC) and multiniche crowding are proposed and incorporated into a memetic algorithm to tackle the problem. The AMC mechanism is developed to ensure a diverse search over solution subspaces corresponding to different numbers of clusters while allowing more promising subspaces to be more intensively searched. In this mechanism, the amount of individuals to be migrated between subpopulations is adaptively controlled according to the performance of subpopulations as well as the diversity of cluster numbers in population. Further, the migration is restricted to occur between subpopulations with relatively similar performances. Additionally, subpopulations with different performances are devised to search their corresponding subspaces with different exploration powers. The adaptive multiniche crowding scheme is designed to promote a diverse search of the subspace while allowing an efficient convergence of the corresponding subpopulation. This is achieved by dynamically adjusting parameter values of a multiniche crowding method to form and maintain diverged niches of high fitness within the subpopulation. The performance of proposed algorithm has been demonstrated through a series of experiments on both artificial and real data, and compared with existing methods. The results reveal that our proposed algorithm can achieve superior clustering performance and outperform related methods.

During disaster events, timely and targeted information provision and exchange could provide great help to the stricken population in difficult and complicated environments. This paper reports a service-oriented system, called Rescue Wings, for providing emergency support to sufferers and rescuers in disasters. The system utilizes mobile services to acquire real-time information about the users and environment, and constructs service agents (servants) to provide active services for mobile users. To perform their functions, the servants frequently invoke a set of intelligent services of Rescue Wings, which can further access a number of public services from government and other public organizations. We identify the most frequent request sequence patterns (FRSP) of Rescue Wings, and develop a new bio-inspired algorithm for efficiently scheduling the requests to minimize the response delay. The system has been tested in several disaster rescue drills, and has been successfully applied to the 2013 Ya'an Earthquake in Southeast China.

Ecogeography-based optimization (EBO) is an extension of biogeography-based optimization (BBO) that evolves a population of solutions by continually migrating features among them, mimicking the principle of immigration and emigration of species from one habitat to another in biogeographical distribution. The original EBO uses a linear maturity model for balancing exploration and exploitation. In this paper we generalize the maturity model by introducing a single control parameter, and then tune the parameter for each test problem of the CEC 2015 learning-based benchmark problem suite in order to find the most effective model for solving the problem. We design a binary search method for conveniently and effectively tuning the model. The computational experiments show that the tuned algorithm can improve the solution quality on different problems of the benchmark suite significantly.

Borrowing ideas from shallow water wave theory, water wave optimization (WWO) is a new evolutionary algorithm that uses three wave-inspired operators including propagation, refraction, and breaking for effectively exploring a high-dimensional solution space of an optimization problem. In this paper we develop a simplified version of WWO (SimWWO) by leaving out the refraction operator and introducing a population size reduction strategy to better balance exploration and exploitation, meanwhile partially compensate the weeding-out effect of the refraction operator. SimWWO is easier to implement, and computational experiments show that the overall performance of SimWWO is better than the original WWO on the CEC 2015 single-objective optimization test problems.

Timely and accurate identification and classification of victims in earthquakes is crucial for improving rescue efficiency, but available information about victims and their surrounding environment is often vague and imprecise. Rescue wings is a web-based intelligent system that monitors and analyzes the statuses of identified victims to support decision making in earthquake rescue operations. A key component of the system is a Takagi-Sugeno (T-S)-type neuro-fuzzy network for disaster-stricken population classification, and one important input of the network is the output of another T-S-type recurrent neuro-fuzzy network for recognizing the movement patterns from the users' temporal location data. A novel differential biogeography-based optimization (DBBO) algorithm is developed for parameter optimization of both the main network and the subnetwork. Experimental results have shown that the hybrid neuro-fuzzy network exhibits good classification performance in comparison with some other typical neuro-fuzzy networks, and the proposed DBBO outperforms some state-of-the-art evolutionary algorithms in network learning. The solution approach has also been successfully applied to the 2013 Ya'an Earthquake in Sichuan province, China.

The railway has played a significant role in disaster relief transportation in China. This paper presents an emergency railway transportation problem, which is to use the limited transport capability to meet the urgent relief transportation requirements. We have applied several state-of-the-art evolutionary algorithms to a variety of problem instances, but have found that none of them can obtain satisfactory solutions in all cases. To overcome this obstacle, we integrate a set of individual heuristic operators into a hyperheuristic framework, which performs a stochastic search on the low-level heuristics by using feedback on their performance in the process of problem solving, thus yielding a high overall performance on different instances. Computational experiments show that the hyperheuristic exhibits significant advantages over the individual heuristics. The problem model and the hyperheuristic solution approach have also been successfully applied to the emergency railway transportation during the 2013 Dingxi earthquake, in China.

Storing biometric templates and/or encryption keys, as adopted in traditional biometrics-based authentication methods, has raised a matter of serious concern. To address such a concern, biometric key generation, which derives encryption keys directly from statistical features of biometric data, has emerged to be a promising approach. Existing methods of this approach, however, are generally unable to appropriately model user variations, making them difficult to produce consistent and discriminative keys of high entropy for authentication purposes. This paper develops a semisupervised clustering scheme, which is optimized through a niching memetic algorithm, to effectively and simultaneously model both intra- and interuser variations. The developed scheme is employed to model the user variations on both single features and feature subsets with the purpose of recovering a large number of consistent and discriminative feature elements for key generation. Moreover, the scheme is designed to output a large number of clusters, thus further assisting in producing long while consistent and discriminative keys. Based on this scheme, a biometric key generation method is finally proposed. The performance of the proposed method has been evaluated on the biometric modality of handwritten signatures and compared with existing methods. The results show that our method can deliver consistent and discriminative keys of high entropy, outperforming-related methods.

The paper presents a hybrid biogeography-based optimization (BBO) and fireworks algorithm (FWA) for global optimization. The key idea is to introduce the migration operator of BBO to FWA, in order to enhance information sharing among the population, and thus improve solution diversity and avoid premature convergence. A migration probability is designed to integrate the migration of BBO and the normal explosion operator of FWA, which can not only reduce the computational burden, but also achieve a better balance between solution diversification and intensification. The Gaussian explosion of the enhanced FWA (EFWA) is reserved to keep the high exploration ability of the algorithm. Experimental results on selected benchmark functions show that the hybrid BBO FWA has a significantly performance improvement in comparison with both BBO and EFWA.

Penalty functions are widely used in constrained optimization, but determining optimal penalty parameters or weights turns out to be a difficult optimization problem itself. The paper proposes a hyper-heuristic approach, which searches the optimal penalty weight setting for low-level heuristics, taking the performance of those heuristics with specialized penalty weight settings as feedback to adjust the high-level search. The proposed approach can either be used for merely improving low-level heuristics, or be combined into a common hyper-heuristic framework for constrained optimization. Experiments on a set of well-known benchmark problems show that the hyper-heuristic approach with penalty parameter adaptation is effective in both aspects.

Biogeography-based optimization (BBO) is a relatively new heuristic method, where a population of habitats (solutions) are continuously evolved and improved mainly by migrating features from high-quality solutions to low-quality ones. In this paper we equip BBO with local topologies, which limit that the migration can only occur within the neighborhood zone of each habitat. We develop three versions of localized BBO algorithms, which use three different local topologies namely the ring topology, the square topology, and the random topology respectively. Our approach is quite easy to implement, but it can effectively improve the search capability and prevent the algorithm from being trapped in local optima. We demonstrate the effectiveness of our approach on a set of well-known benchmark problems. We also introduce the local topologies to a hybrid DE/BBO method, resulting in three localized DE/BBO algorithms, and show that our approach can improve the performance of the state-of-the-art algorithm as well.

In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making; and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.

In today's business-critical environments, there is only a limited possibility that all services are to be developed from scratch. To reuse prior knowledge of existing object-oriented system design and adapt them to more flexible and scalable service-oriented systems, the paper presents a systematic approach that employs categorical models to formalize design knowledge in both kinds of software, and utilizes category theoretic computations to mechanize lifting, integration, and distribution in refactoring from object-oriented systems to service-oriented systems. Our approach provides highly abstract, modularized and effective evolution towards service-oriented computing.

The paper presents Macml, a domain-specific language (DSL) that focuses on the effective specification, implementation, and verification of information systems in the domain of machinery services. As a meta-model of the application domain, the language precisely defining elements including entities, relationships, behaviors, constraints, and workflows, based on which the users, domain experts, and software engineers can effectively communicate with each other and work together to model a variety of machinery service management systems, which are all instances of the meta-model and which can be further transformed into executable systems mechanically. As a case study, a system model of Macml is presented to illustrate the implementation of our approach.