Handing Wang

Handing Wang
Xidian University · Department of Artificial Intelligence

PhD

About

84
Publications
19,001
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2,096
Citations
Introduction
Handing Wang currently works at the School of Artificial Intelligence, Xidian University, China. Handing does research in Algorithms, Computing in Mathematics, Natural Science, Engineering and Medicine and Artificial Intelligence. Their current project is 'Data-driven surrogate-assisted evolutionary fluid dynamic optimization'.
Additional affiliations
September 2018 - present
Xidian University
Position
  • Professor
July 2015 - June 2018
University of Surrey
Position
  • Research Associate
March 2012 - March 2013
Position
  • Visiting Student

Publications

Publications (84)
Article
Trauma systems have been shown to reduce death and disability from injury but must be appropriately configured. A systematic approach to trauma system design can help maximize geospatial effectiveness and reassure stakeholders that the best configuration has been chosen. This article describes the GEOS [Geospatial Evaluation of Systems of Trauma Ca...
Article
Full-text available
Abstract There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced,...
Article
Full-text available
Surrogate-assisted many-objective optimization is to locate Pareto optimal solutions using a limited number of function evaluations. Most existing surrogate-assisted evolutionary algorithms are designed to embed in a specific many-objective evolutionary algorithm. The Pareto-based bi-indicator infill sampling criterion has been proven to be effecti...
Article
A number of real-world multiobjective optimization problems (MOPs) are driven by the data from experiments or computational simulations. In some cases, no new data can be sampled during the optimization process and only a certain amount of data can be sampled before optimization starts. Such problems are known as offline data-driven MOPs. Although...
Article
To solve noisy and expensive multi-objective optimization problems, there are only a few function evaluations can be used due to the limitation of time and/or money. Because of the influence of noises, the evaluations are inaccurate. It is challenging for the existing surrogate-assisted evolutionary algorithms. Due to the influence of noises, the p...
Article
Evolutionary Algorithms (EAs) are nature-inspired population-based search methods which work on Darwinian principles of natural selection. Due to their strong search capability and simplicity of implementation, EAs have been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional mathematical p...
Data
Supplementary material for the paper "A Survey of Normalization Methods in Multiobjective Evolutionary Algorithms".
Chapter
This chapter introduces the basic evolutionary algorithms, including the canonical genetic algorithms, real-coded genetic algorithms, evolution strategies, genetic programming, ant colony optimization algorithms, particle swarm optimization, and differential evolution. In addition, memetic algorithms that combine evolutionary search with local sear...
Chapter
This chapter introduces the typical machine learning problems, describes the widely used machine learning models, and presents the basic learning algorithms suited for solving various machine learning problems. Note that a machine learning model may be used for accomplishing different machine learning tasks, provided that a proper learning algorith...
Chapter
This chapter briefly introduces the most widely used traditional optimization algorithms, including the gradient based method and its variants, basic methods for constrained optimization, pattern search for non-differentiable or black-box optimization problems, and deterministic global optimization methods.
Chapter
Offline data-driven optimization does not allow to sample new data during the optimization, making it hard to verify the solution and update the surrogates. One additional challenge is to select appropriate solutions for final implementation, in particular in multi- or many-objective optimization. Nevertheless, this does not necessarily mean that n...
Chapter
Lack of training data is one major challenge in data-driven optimization, since data collection is either computationally expensive or costly in many data-driven optimization problems. To address this issue, this chapter presents three classes of knowledge transfer approaches in data-driven evolutionary optimization. The first approach is based on...
Chapter
It becomes increasingly difficult to train a high-quality surrogate model as the dimension of a problem increases, especially for expensive optimization problems where only a limited number of samples can be afforded. This chapter focuses on addressing high-dimensional expensive problems that have over 30 and up to some 200 decision variables. The...
Chapter
This chapter presents surrogate-assisted evolutionary algorithms for single-objective optimization that employ multiple surrogates. Multiple surrogates can not only improve the prediction performance and estimate the degree of prediction uncertainty, but also capture both global and local features of the fitness landscape. The multiple surrogates c...
Chapter
With the recent booming development of deep neural networks, the demand for automated design of efficient deep neural architectures has been increasing. This chapter introduces the basics of automated neural architecture search and discusses the current remaining challenges, focusing on scalability and flexibility of network architecture representa...
Chapter
Multi-objective evolutionary optimization has found increasing applications in the real world, many of which are expensive. This chapter starts with introducing three main categories of evolutionary algorithms for multi-objective optimization, namely decomposition based, Pareto dominance based and performance indicator based. This is followed by a...
Chapter
Solving many-objective optimization problems is challenging due to the increase in the number of objectives. The challenges include the increased complexity in the structure of the Pareto front, the increased number of solutions needed to represent the Pareto front, and the selection of solutions. Many-objective optimization becomes even more chall...
Chapter
This chapter introduces the fundamentals of optimization, including the mathematical formulation of an optimization problem, convexity and types of optimization problems, single- and multi-objective optimization, and other important aspects of optimization such as robust optimization and dynamic optimization. Robustness optimization over time, a re...
Article
There are some practical optimization problems that can be only optimized using historical data, which is known as offline data-driven optimization problems. Since the real function evaluations are not available in the optimization process, surrogate models must replace the real fitness evaluations to guide the search. The key issue in offline data...
Article
Recently, evolutionary algorithms have made great achievements in multi-objective optimization problems (MOPs), but there is a little research on how to deal with noisy multi-objective optimization problems (NMOPs), which are quite common in real life. The work in this paper attempts to find the commonality of noises in images/signals and NMOPs and...
Chapter
This chapter introduces the definition of and motivations behind data-driven optimization. Two basic data-driven optimization paradigms, offline and online data-driven optimization, are introduced. A variety of heuristic population and individual based surrogate management strategies for surrogate assisted evolutionary optimization are presented, a...
Article
A real-world multiobjective optimization problem (MOP) usually has differently-scaled objectives. Objective space normalization has been widely used in multiobjective optimization evolutionary algorithms (MOEAs). Without objective space normalization, most of the MOEAs may fail to obtain uniformly-distributed and well-converged solutions on MOPs wi...
Article
Only a small number of function evaluations can be afforded in many real-world multi-objective optimization problems where the function evaluations are economically/computationally expensive. Such problems pose great challenges to most existing multi-objective evolutionary algorithms which require a large number of function evaluations for optimiza...
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Full-text available
Many real-world combinatorial optimization problems have both expensive objective and constraint functions. Although surrogate models for the discrete decision variables can be trained to replace the expensive fitness evaluations in evolutionary algorithms, the approximation errors of the surrogate models for the constraint function easily misguide...
Conference Paper
Full-text available
Infill sampling criteria play a crucial role in saving expensive evaluations for surrogate-assisted multiobjective evolutionary algorithms. Promoting convergence and maintaining diversity in the population are the two main goals of designing a new infilling sampling criterion, which is naturally a bi-objective optimization problem. In this paper, a...
Article
In offline data-driven evolutionary optimization, no real fitness evaluations is allowed during the optimization, making it extremely challenging to build high-quality surrogates on limited amount of data. This is especially true for large-scale optimization problems where typically a large amount of data is needed for constructing reliable surroga...
Article
Full-text available
Minimax optimization is a widely-used formulation for robust design in multiple operating or environmental scenarios, where the worst-case performance among multiple scenarios is the optimization objective requiring a large number of quality assessments. Consequently, minimax optimization using evolutionary algorithms becomes prohibitive when each...
Article
A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations...
Article
Full-text available
Real-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the compu...
Book
This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021. The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algor...
Book
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background...
Article
Optimization of many real-world optimization problems relies on numerical simulations for function evaluations. In some cases, both high- and low-fidelity simulations are available, where the high fidelity evaluation is accurate but time-consuming, whereas the low-fidelity evaluation is less accurate but computationally cheap. To find an acceptable...
Chapter
Full-text available
This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is large, or both. These problems pose challenges to evolutionary algorithms themselves, constructing surrogates and surrogate manageme...
Article
Full-text available
Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high compu...
Chapter
This work presents a summary of the results obtained during the activities developed within the GARTEUR AD/AG-52 group. GARTEUR stands for “Group for Aeronautical Research and Technology in Europe” and is a multinational organization that performs high quality, collaborative, precompetitive research in the field of aeronautics to improve technologi...
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Full-text available
Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architectures are optimally constructed. The architectures of state-of-the-art CNNs are typically hand-crafted with extensive expertise in both CNNs and the inves...
Article
The papers in this special section focus on the use of computational intelligence in data driven optimization applications. Most evolutionary algorithms and other meta-heuristic search methods typically assume that there are explicit objective functions available for fitness evaluations. In the real world, such explicit objective functions may not...
Article
Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are available for evaluating candidate solutions. In this paper, we address a class of expensive data-driven constrained multiobjective combinatorial optimization problems, where the objectives and constraints c...
Article
Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness ev...
Article
Full-text available
Cooperative coevolutionary (CC) algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes CC algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into differ...
Article
In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions. Instead, surrogate models must be built based on historical data to approximate the objective functions and no new data will be available during the optimization process. Such pr...
Article
Full-text available
Surrogate-assisted evolutionary algorithms have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing surrogate-assisted evolutionary algorithms are designed for solving low-dimensional single or multi-objective optimization problems, which are not well sui...
Article
Background: Trauma center designation in excess of need risks dilution of experience, reduction in research and training opportunities, and increased costs. The objective of this study was to evaluate the use of a novel data-driven approach (whole-system mathematical modelling of patient flow) to compare the configuration of an existing trauma sys...
Article
Full-text available
Many real-world optimization problems involve computationally intensive numerical simulations to accurately evaluate the quality of solutions. Usually the fidelity of the simulations can be controlled using certain parameters and there is a trade-off between simulation fidelity and computational cost, i.e., the higher the fidelity, the more complex...
Conference Paper
This work presents a summary of the results obtained during the activities developed within the GARTEUR AD/AG-52 group. GARTEUR stands for “Group for Aeronautical Research and Technology in Europe” and is a multinational organization that performs high quality, collaborative, precompetitive research in the field of aeronautics to improve technologi...
Article
Full-text available
Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi- and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of non-dominated sorting in m...
Article
Full-text available
Evolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. In practice, however, decision makers are usually interested in only a particular part of the Pareto front of the multi-objective optimization problem. This is particularly true when the number of objectives becomes large. Over...
Article
Full-text available
Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past...
Article
Full-text available
Nadir points play an important role in many-objective optimization problems, which describe the ranges of their Pareto fronts. Using nadir points as references, decision makers may obtain their preference information for many-objective optimization problems. As the number of objectives increases, nadir point estimation becomes a more difficult task...
Article
Full-text available
Most existing work on evolutionary optimization assumes that there are analytic functions for evaluating the objectives and constraints. In the real world, however, the objective or constraint values of many optimization problems can be evaluated solely based on data and solving such optimization problems is often known as data-driven optimization....
Article
Full-text available
Maintaining diversity is one important aim of multi-objective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multi-objective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for many-objective optimization, which is an accumulation o...
Article
Full-text available
Background The optimal geographical configuration of healthcare systems is key to maximizing accessibility while promoting the efficient use of resources. This paper reports the use of a novel approach to inform the optimal configuration of a national trauma system. Methods Prospective cohort study of all trauma patients, aged 15 years and older,...