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.

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.

This paper defines the coverage radius of Unmanned Aerial Vehicle(UAV) as its own attribute such as detection radius, overlooking radius, attack radius and so on when UAV is regarded as a fixed particle at a certain height. Generally, when UAV reconnaissance, attack or material delivery to multiple targets, its flight path planning problem is transformed into Traveling Salesman Problem(TSP), but the coverage radius of UAV is not considered, and there may be path intersection and targets covered repeatedly in the planned path. Aiming at the problem of UAV path planning when considering UAV coverage radius, Ant Colony Optimization (ACO) and Density-Based Spatial Clustering of Applications with Noise(DBSCAN) are proposed to solve UAV flight path planning problem combined with geometric judgment, which can better solve the problem of path intersection in TSP, and the problem of targets covered repeatedly without considering UAV coverage radius, to achieve the goal of no intersection path and shorter path length. Python programming simulation is carried out for the proposed method, and the experimental results show that the proposed method can effectively avoid the problems flight path intersection and targets covered repeatedly, and greatly shorten the flight path length of UAV, which will greatly improve the flight efficiency of UAV.

Regionalization is an important part of the spatial analysis process, and the solution should be contiguity-constrained in each region. In general, several objectives need to be optimized in practical regionalization, such as the homogeneity of regions and the heterogeneity among regions. Therefore, multi-objective techniques are more suitable for solving regionalization problems. In this paper, we design a multi-objective particle swarm optimization algorithm for solving regionalization problems. Towards this goal, a novel particle representation for regionalization is proposed, which can be expressed in continuous space and has flexible constraints on the number of regions. In the process of optimization, a contiguous-region method is designed that satisfies the constraints and improves the efficiency. The decision solution is selected in the Pareto set based on a trade-off between the objective functions, and the number of regions can be automatically determined. The proposed method outperforms six regionalization algorithms in terms of both the number and the quality of the solutions.

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.

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.

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.