Hai-Feng Ling

Population Classification in Fire Evacuation: A Multiobjective Particle Swarm Optimization Approach

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.

Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach

Emergency transportation is the most important part of disaster relief supply chain operations, and its planning problem always involves multiple objectives, complex constraints, and inherent uncertainty. Based on the analysis of several natural disaster that occurred in China since 2007, we propose a multi-objective fuzzy optimization problem of emergency transportation planning in disaster relief supply chains, which takes into consideration three transportation modes: air, rail, and road. To cope with the uncertainty, we employ three correlated fuzzy ranking criteria, and define the β dominance relation for evaluating the solutions of the problem. To efficiently solve the problem, we develop a cooperative optimization method that divides the integrated problem into a set of subcomponents, evolves the sub-solutions concurrently, and brings together the sub-solutions to construct complete solutions. The proposed method is effective, scalable, and robust, and thus contributes greatly to the performance of emergency transportation planning in disaster management.

A hybrid fireworks optimization method with differential evolution operators

Fireworks algorithm (FA) is a relatively new swarm-based metaheuristic for global optimization. The algorithm is inspired by the phenomenon of fireworks display and has a promising performance on a number of benchmark functions. However, in the sense of swarm intelligence, the individuals including fireworks and sparks are not well-informed by the whole swarm. In this paper we develop an improved version of the FA by combining with differential evolution (DE) operators: mutation, crossover, and selection. At each iteration of the algorithm, most of the newly generated solutions are updated under the guidance of two different vectors that are randomly selected from highly ranked solutions, which increases the information sharing among the individual solutions to a great extent. Experimental results show that the DE operators can improve diversity and avoid prematurity effectively, and the hybrid method outperforms both the FA and the DE on the selected benchmark functions.

Evolutionary optimization for disaster relief operations

Graphical abstractDisplay Omitted HighlightsWe provide an overview of evolutionary algorithms for disaster relief operations.We show major strengths and shortcomings of the state-of-the-arts.We discuss potential directions for future research. Effective planning and scheduling of relief operations play a key role in saving lives and reducing damage in disasters. These emergency operations involve a variety of challenging optimization problems, for which evolutionary computation methods are well suited. In this paper we survey the research advances in evolutionary algorithms (EAs) applied to disaster relief operations. The operational problems are classified into five typical categories, and representative works on EAs for solving the problems are summarized, in order to give readers a general overview of the state-of-the-arts and facilitate them to find suitable methods in practical applications. Several state-of-art methods are compared on a set of real-world emergency transportation problem instances, and some lessons are drawn from the experimental analysis. Finally, the strengths, limitations and future directions in the area are discussed.

Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations

Abstract Biogeography-based optimization (BBO) is a bio-inspired metaheuristic based on the mathematics of island biogeography. The paper proposes a new variation of BBO, named ecogeography-based optimization (EBO), which regards the population of islands (solutions) as an ecological system with a local topology. Two novel migration operators are designed to perform effective exploration and exploitation in the solution space, mimicking the species dispersal under ecogeographic barriers and differentiations. Experimental results show that the EBO outperforms the basic BBO and several other popular evolutionary algorithms (EAs) on a set of well-known benchmark problems. We also present a real-world application of the proposed EBO to an emergency airlift problem in the 2013 Ya׳an–Lushan Earthquake, China.

Emergency railway wagon scheduling by hybrid biogeography-based optimization

Railway transportation plays an important role in many disaster relief and other emergency supply chains. Based on the analysis of several recent disaster rescue operations in China, the paper proposes a mathematical model for emergency railway wagon scheduling, which considers multiple target stations requiring relief supplies, source stations for providing supplies, and central stations for allocating railway wagons. Under the emergency environment, the aim of the problem is to minimize the weighted time for delivering all the required supplies to the targets. For efficiently solving the problem, we develop a new hybrid biogeography-based optimization (BBO) algorithm, which uses a local ring topology of population to avoid premature convergence, includes the differential evolution (DE) mutation operator to perform effective exploration, and takes some problem-specific mechanisms for fine-tuning the search process and handling the constraints. Computational experiments show that our algorithm is robust and scalable, and outperforms some state-of-the-art heuristic algorithms on a set of problem instances.

Localized biogeography-based optimization

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.

Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

Particle swarm optimizationPSOis a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizerCLPSO� , which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.

Efficient multi-objective tabu search for emergency equipment maintenance scheduling in disaster rescue

The paper describes a mathematical model of the emergency equipment maintenance scheduling problem particularly in disaster rescue operations, which aims to achieve a good balance between operational capability achieved by maintenance, cost-effectiveness, maintenance risks, and reserved maintenance capability for sustainable operations. We design a compact solution encoding that greatly facilitates the search process, and develop an efficient multi-objective tabu search algorithm that evolves a set of solutions towards the Pareto optimal frontier, using a weighted function based on the decision-maker’s preference to guide the search procedures. Simulation experiments and real-world application results demonstrate the effectiveness of our approach.

Emergency scheduling of engineering rescue tasks in disaster relief operations and its application in China

Disaster relief operations typically involve a large number of engineering rescue tasks, the efficient completion of which is vital to the success of the operations. The paper establishes a model of emergency scheduling of engineering rescue tasks in disaster relief operations, which involves multiple rescue teams and tasks, different and, perhaps fuzzy, processing times, as well as different importance weights of the tasks. We then propose a method based on biogeography-based optimization, develop effective migration and mutation operators, and employ a multiobjective optimization approach for providing a set of candidate solutions for decision support. Computational experiments demonstrate that our approach exhibits competitive performance on a set of test problems. The proposed model and method have been successfully applied to the recent 2013 Dingxi earthquake that occurred in China.