Jinyun Xue

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.

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.

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.

A Hybrid Neuro-Fuzzy Network Based on Differential Biogeography-Based Optimization for Online Population Classification in Earthquakes

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 Yaan Earthquake in Sichuan province, China.

A problem reduction based approach to discrete optimization algorithm design

The paper presents a novel approach to formal algorithm design for a typical class of discrete optimization problems. Using a concise set of program calculation rules, our approach reduces a problem into subproblems with less complexity based on function decompositions, constructs the problem reduction graph that describes the recurrence relations between the problem and subproblems, from which a provably correct algorithm can be mechanically derived. Our approach covers a large variety of algorithms and bridges the relationship between conventional methods for designing efficient algorithms (including dynamic programming and greedy) and some effective methods for coping with intractability (including approximation and parameterization).

A High Performance Solution for Automated Computer Examination Systems

In this paper ire present an effective and practical approach for developing high-performance, scalable, and manageable automated computer examination systems (ACES). The proposed solution employs the XML technology) to naturally represent and efficiently manipulate the semi-structured data of test libraries, performs the most complex computational tasks via SQLCLR objects that reside in the server-side processes, and therefore reduces the network traffic and improves system performance vastly. Our approach has been successfully applied in several real-world examination systems, and has demonstrated its significant contribution to the effectiveness and efficiency of computer-based test delivery and management.

An a-team based architecture for constraint programming

The paper proposes an agent-based constraint programming architecture that we have successfully applied to solve large, particularly combinatorial, operations problems. The architecture is based on the asynchronous team (A-Team) in which multiple problem solving agents cooperate with each other by exchanging results to produce a set of non-dominated solutions. We extend the A-Team by introducing CSP-specific agents, explicitly defining solution states, and enabling solution decomposition/composition, and thereby improve the performance, reliability, and automation of constraint programming significantly.

A Pythagorean-Type Fuzzy Deep Denoising Autoencoder for Industrial Accident Early Warning

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 models 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.

A-TEAM BASED SUPPLY CHAIN MANAGEMENT AGENT ARCHITECTURE

Todays supply chains increasingly involve complex sets of processes, objectives and constraints, and therefore agent-based architectures for supply chain management (SCM) become much more difficul...

Object-Oriented specification composition and refinement via category theoretic computations

Most of the existing formal object-oriented methods use classes or objects as the basic unit of design, and therefore lack a precise semantics for specifying high-granularity components. The paper presents a framework of categorical models that focus concern on the interactive relationships between objects, and that explicitly support specification composition and refinement at different levels of abstraction and granularity in object-oriented design. A case study of implementing templated design patterns demonstrates the ability of category theoretic computations to mechanize software development.