Skip to main content

Yutao Qi

Xidian University, School of Computer, Associate Professor

Followers

9

Following

0

Co-authors

3

Public Views

Yutao Qi
Ph. D. Associated Professor
School of Computer Science and Technology
Xi’dian University
Xi’an, Shaanxi Province 710072, P.R. China
Email: [email protected]

less

University of Southampton

University of Southampton

Jadavpur University

NIT Durgapur

Lincoln University,Oakland, California

Shengxiang Yang

De Montfort University, Leicester, UK)

Shinshu University

University of Essex

Interests

Uploads

Papers by Yutao Qi

A Delaunay Triangulation Based Density Measurement for Evolutionary Multi-objective Optimization

Diversity preservation is a critical issue in evolutionary multi-objective optimization algorithm... more Diversity preservation is a critical issue in evolutionary multi-objective optimization algorithms (MOEAs), it has significant influence on the quality of final solution set. In this wok, a crowding density measurement is developed for preserving diversity in MOEAs by using the Delaunay triangulation mesh built on the population in the objective space. Base on the property of the Delaunay triangulation, the new density measurement considers both the Eucli‐ dean distance and the relative position between individuals, and thus provide a more accurate estimation of the density around a specific individual within the population. Experimental results indicate that the suggested density measurement help to improve the performance of MOEAs significantly.

MOEA/D with A Delaunay Triangulation Based Weight Adjustment

MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization which deco... more MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization which decomposes a multi-objective optimization problem (MOP) into a set of scalar sub-problems with evenly spread weight vectors. Recent studies have shown that the fixed weight vectors used in MOEA/D might not be able to cover the whole Pareto front (PF) very well. Due to this, we developed an adaptive weight adjustment method in our previous work by removing subproblems from the crowded parts of the PF and adding new ones into the sparse parts. Although it performs well, we found that the sparse measurement of a subproblem which is determined by the m-nearest (m is the dimensional of the object space) neighbors of its solution can be more appropriately defined. In this work, the neighborhood relationship between subproblems is defined by using Delaunay triangulation (DT) of the points in the population.

An immune multi-objective optimization algorithm with differential evolution inspired recombination

According to the regularity of continuous multi-objective optimization problems (MOPs), an immune... more According to the regularity of continuous multi-objective optimization problems (MOPs), an immune multi-objective optimization algorithm with differential evolution inspired recombination (IMADE) is proposed in this paper. In the proposed IMADE, the novel recombination provides two types of candidate searching directions by taking three recombination parents which distribute along the current Pareto set (PS) within a local area. One of the searching direction provides guidance for finding new points along the current PS, and the other redirects the search away from the current PS and moves towards the target PS. Under the background of the SBX (Simulated binary crossover) recombination which performs local search combined with random search near the recombination parents, the new recombination operator utilizes the regularity of continuous MOPs and the distributions of current population, which helps IMADE maintain a more uniformly distributed PF and converge much faster. Experimental results have demonstrated that IMADE outperforms or performs similarly to NSGAII, NNIA, PESAII and OWMOSaDE in terms of solution quality on most of the ten testing MOPs. IMADE converges faster than NSGAII and OWMOSaDE. The efficiency of the proposed DE recombination and the contributions of DE and SBX recombination to IMADE have also been experimentally investigated in this work.

MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved gre... more Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing C 2014 by the Massachusetts Institute of Technology Evolutionary Computation 22(2): 231–264 Y. Qi et al. efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover , an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discon-tinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, paλ-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) ... more Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RFCO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RFCO (MO-RFCO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO–EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RFCO problem. MO-PSO–EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RFCO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO–EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO–EDA is suitable for solving MO-RFCO problems.

A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algo... more Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algorithmic framework for solving multi-objective optimization problems. It decomposes a target problem into a set of scalar sub-problems and optimizes them simultaneously. Due to its simplicity and outstanding performance, MOEA/D has been widely studied and applied. However, for solving the multi-objective vehicle routing problem with time windows (MO-VRPTW), MOEA/D faces a difficulty that many sub-problems have duplicated best solutions. It is well-known that MO-VRPTW is a challenging problem and has very few Pareto optimal solutions. To address this problem, a novel selection operator is designed in this work to enhance the original MOEA/D for dealing with MO-VRPTW. Moreover, three local search methods are introduced into the enhanced algorithm. Experimental results indicate that the proposed algorithm can obtain highly competitive results on Solomon's benchmark problems. Especially for instances with long time windows, the proposed algorithm can obtain more diverse set of non-dominated solutions than the other algorithms. The effectiveness of the proposed selection operator is also demonstrated by further analysis.

Multi-objective immune algorithm with Baldwinian learning

By replacing the selection component, a well researched evolutionary algorithm for scalar optimiz... more By replacing the selection component, a well researched evolutionary algorithm for scalar optimization problems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators designed for SOPs might not be suitable for MOPs due to the different optima structures between them. At present, few works have been done to improve the searching efficiency of MOEAs according to the characteristic of MOPs. Based on the regularity of continues MOPs, a Baldwinian learning strategy is designed for improving the nondominated neighbor immune algorithm and a multi-objective immune algorithm with Baldwinian learning (MIAB) is proposed in this study. The Baldwinian learning strategy extracts the evolving environment of current population by building a probability distribution model and generates a predictive improving direction by combining the environment information and the evolving history of the parent individual. Experimental results based on ten representative benchmark problems indicate that, MIAB outperforms the original immune algorithm, it performs better or similarly the other two outstanding approached NSGAII and MOEA/D in solution quality on most of the eight testing MOPs. The efficiency of the proposed Baldwinian learning strategy has also been experimentally investigated in this work.

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Applied Soft Computing, 2015

A Delaunay Triangulation Based Density Measurement for Evolutionary Multi-objective Optimization

Lecture Notes in Computer Science, 2016

Label propagation based evolutionary clustering for detecting overlapping and non-overlapping communities in dynamic networks

Knowledge-Based Systems, 2015

High Performance Immune Clonal Algorithm for Solving Large Scale TSP

MOEA/D with biased weight adjustment inspired by user preference and its application on multi-objective reservoir flood control problem

Soft Computing, 2015

A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows

Computers & Operations Research, 2015

High Performance Immune Clonal Algorithm for Solving Large Scale TSP

Traveling Salesman Problem, Theory and Applications, 2010

Prediction of missing links based on multi-resolution community division

Physica A: Statistical Mechanics and its Applications, 2015

ABSTRACT The investigation of link prediction in networks is an important issue in many disciplin... more ABSTRACT The investigation of link prediction in networks is an important issue in many disciplines. The research of prediction algorithms which required short time but high accuracy is still a challenging task. Most of the existing algorithms are based on the topological information of the networks, including the local or global similarity indices. It is found that the hierarchical organization and community structure information may indeed provide insights for link prediction. In this paper, we propose a simple link prediction method, which fully explore the community structure information of the networks. Firstly, the community structure of the networks under different resolutions is extracted. Then, a simple frequency statistical model is applied to calculate how many times that a pair of nodes divided into the same community under different resolutions. Finally, the probability of the missing links is calculated. The performance of our algorithm is demonstrated by comparing with other seven well-known methods on two kinds of networks in different scales. The results indicate that our approach not only has a good performance on the accuracy, but also has a lower time complexity than any other algorithms which are based on hierarchical structure of the network.

Discrete differential evolution algorithm for the job shop scheduling problem

Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC '09, 2009

Differential Evolution (DE) Algorithm is a new evolutionary computation algorithm with rapid conv... more Differential Evolution (DE) Algorithm is a new evolutionary computation algorithm with rapid convergence rate. However, it does not perform well on dealing with job shop scheduling problems that have discrete decision variables. To remedy this, a Discrete Differential Evolution (DDE) Algorithm with special crossover and mutation operators is proposed to solve this problem. Under the skeleton of DE algorithm, The

MOEA/D with a delaunay triangulation based weight adjustment

Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 2014

ABSTRACT MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization w... more ABSTRACT MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization which decomposes a multi-objective optimization problem (MOP) into a set of scalar sub-problems with evenly spread weight vectors. Recent studies have shown that the fixed weight vectors used in MOEA/D might not be able to cover the whole Pareto front (PF) very well. Due to this, we developed an adaptive weight adjustment method in our previous work by removing subproblems from the crowded parts of the PF and adding new ones into the sparse parts. Although it performs well, we found that the sparse measurement of a subproblem which is determined by the m-nearest (m is the dimensional of the object space) neighbors of its solution can be more appropriately defined. In this work, the neighborhood relationship between subproblems is defined by using Delaunay triangulation (DT) of the points in the population.

MOEA/D with uniform decomposition measurement for many-objective problems

Soft Computing, 2014

Hybrid Immune Algorithm with EDA for Multi-Objective Optimization

Journal of Software, 2014

Memetic Immune Algorithm for Multiobjective Optimization

Journal of Software, 2014

A Delaunay Triangulation Based Density Measurement for Evolutionary Multi-objective Optimization

Diversity preservation is a critical issue in evolutionary multi-objective optimization algorithm... more Diversity preservation is a critical issue in evolutionary multi-objective optimization algorithms (MOEAs), it has significant influence on the quality of final solution set. In this wok, a crowding density measurement is developed for preserving diversity in MOEAs by using the Delaunay triangulation mesh built on the population in the objective space. Base on the property of the Delaunay triangulation, the new density measurement considers both the Eucli‐ dean distance and the relative position between individuals, and thus provide a more accurate estimation of the density around a specific individual within the population. Experimental results indicate that the suggested density measurement help to improve the performance of MOEAs significantly.

MOEA/D with A Delaunay Triangulation Based Weight Adjustment

MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization which deco... more MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization which decomposes a multi-objective optimization problem (MOP) into a set of scalar sub-problems with evenly spread weight vectors. Recent studies have shown that the fixed weight vectors used in MOEA/D might not be able to cover the whole Pareto front (PF) very well. Due to this, we developed an adaptive weight adjustment method in our previous work by removing subproblems from the crowded parts of the PF and adding new ones into the sparse parts. Although it performs well, we found that the sparse measurement of a subproblem which is determined by the m-nearest (m is the dimensional of the object space) neighbors of its solution can be more appropriately defined. In this work, the neighborhood relationship between subproblems is defined by using Delaunay triangulation (DT) of the points in the population.

An immune multi-objective optimization algorithm with differential evolution inspired recombination

According to the regularity of continuous multi-objective optimization problems (MOPs), an immune... more According to the regularity of continuous multi-objective optimization problems (MOPs), an immune multi-objective optimization algorithm with differential evolution inspired recombination (IMADE) is proposed in this paper. In the proposed IMADE, the novel recombination provides two types of candidate searching directions by taking three recombination parents which distribute along the current Pareto set (PS) within a local area. One of the searching direction provides guidance for finding new points along the current PS, and the other redirects the search away from the current PS and moves towards the target PS. Under the background of the SBX (Simulated binary crossover) recombination which performs local search combined with random search near the recombination parents, the new recombination operator utilizes the regularity of continuous MOPs and the distributions of current population, which helps IMADE maintain a more uniformly distributed PF and converge much faster. Experimental results have demonstrated that IMADE outperforms or performs similarly to NSGAII, NNIA, PESAII and OWMOSaDE in terms of solution quality on most of the ten testing MOPs. IMADE converges faster than NSGAII and OWMOSaDE. The efficiency of the proposed DE recombination and the contributions of DE and SBX recombination to IMADE have also been experimentally investigated in this work.

MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved gre... more Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing C 2014 by the Massachusetts Institute of Technology Evolutionary Computation 22(2): 231–264 Y. Qi et al. efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover , an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discon-tinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, paλ-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) ... more Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RFCO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RFCO (MO-RFCO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO–EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RFCO problem. MO-PSO–EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RFCO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO–EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO–EDA is suitable for solving MO-RFCO problems.

A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algo... more Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algorithmic framework for solving multi-objective optimization problems. It decomposes a target problem into a set of scalar sub-problems and optimizes them simultaneously. Due to its simplicity and outstanding performance, MOEA/D has been widely studied and applied. However, for solving the multi-objective vehicle routing problem with time windows (MO-VRPTW), MOEA/D faces a difficulty that many sub-problems have duplicated best solutions. It is well-known that MO-VRPTW is a challenging problem and has very few Pareto optimal solutions. To address this problem, a novel selection operator is designed in this work to enhance the original MOEA/D for dealing with MO-VRPTW. Moreover, three local search methods are introduced into the enhanced algorithm. Experimental results indicate that the proposed algorithm can obtain highly competitive results on Solomon's benchmark problems. Especially for instances with long time windows, the proposed algorithm can obtain more diverse set of non-dominated solutions than the other algorithms. The effectiveness of the proposed selection operator is also demonstrated by further analysis.

Multi-objective immune algorithm with Baldwinian learning

By replacing the selection component, a well researched evolutionary algorithm for scalar optimiz... more By replacing the selection component, a well researched evolutionary algorithm for scalar optimization problems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators designed for SOPs might not be suitable for MOPs due to the different optima structures between them. At present, few works have been done to improve the searching efficiency of MOEAs according to the characteristic of MOPs. Based on the regularity of continues MOPs, a Baldwinian learning strategy is designed for improving the nondominated neighbor immune algorithm and a multi-objective immune algorithm with Baldwinian learning (MIAB) is proposed in this study. The Baldwinian learning strategy extracts the evolving environment of current population by building a probability distribution model and generates a predictive improving direction by combining the environment information and the evolving history of the parent individual. Experimental results based on ten representative benchmark problems indicate that, MIAB outperforms the original immune algorithm, it performs better or similarly the other two outstanding approached NSGAII and MOEA/D in solution quality on most of the eight testing MOPs. The efficiency of the proposed Baldwinian learning strategy has also been experimentally investigated in this work.

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Applied Soft Computing, 2015

A Delaunay Triangulation Based Density Measurement for Evolutionary Multi-objective Optimization

Lecture Notes in Computer Science, 2016

Label propagation based evolutionary clustering for detecting overlapping and non-overlapping communities in dynamic networks

Knowledge-Based Systems, 2015

High Performance Immune Clonal Algorithm for Solving Large Scale TSP

MOEA/D with biased weight adjustment inspired by user preference and its application on multi-objective reservoir flood control problem

Soft Computing, 2015

A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows

Computers & Operations Research, 2015

High Performance Immune Clonal Algorithm for Solving Large Scale TSP

Traveling Salesman Problem, Theory and Applications, 2010

Prediction of missing links based on multi-resolution community division

Physica A: Statistical Mechanics and its Applications, 2015

ABSTRACT The investigation of link prediction in networks is an important issue in many disciplin... more ABSTRACT The investigation of link prediction in networks is an important issue in many disciplines. The research of prediction algorithms which required short time but high accuracy is still a challenging task. Most of the existing algorithms are based on the topological information of the networks, including the local or global similarity indices. It is found that the hierarchical organization and community structure information may indeed provide insights for link prediction. In this paper, we propose a simple link prediction method, which fully explore the community structure information of the networks. Firstly, the community structure of the networks under different resolutions is extracted. Then, a simple frequency statistical model is applied to calculate how many times that a pair of nodes divided into the same community under different resolutions. Finally, the probability of the missing links is calculated. The performance of our algorithm is demonstrated by comparing with other seven well-known methods on two kinds of networks in different scales. The results indicate that our approach not only has a good performance on the accuracy, but also has a lower time complexity than any other algorithms which are based on hierarchical structure of the network.

Discrete differential evolution algorithm for the job shop scheduling problem

Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC '09, 2009

Differential Evolution (DE) Algorithm is a new evolutionary computation algorithm with rapid conv... more Differential Evolution (DE) Algorithm is a new evolutionary computation algorithm with rapid convergence rate. However, it does not perform well on dealing with job shop scheduling problems that have discrete decision variables. To remedy this, a Discrete Differential Evolution (DDE) Algorithm with special crossover and mutation operators is proposed to solve this problem. Under the skeleton of DE algorithm, The

MOEA/D with a delaunay triangulation based weight adjustment

Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 2014

ABSTRACT MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization w... more ABSTRACT MOEA/D is a popular algorithmic framework of evolutionary multi-objective optimization which decomposes a multi-objective optimization problem (MOP) into a set of scalar sub-problems with evenly spread weight vectors. Recent studies have shown that the fixed weight vectors used in MOEA/D might not be able to cover the whole Pareto front (PF) very well. Due to this, we developed an adaptive weight adjustment method in our previous work by removing subproblems from the crowded parts of the PF and adding new ones into the sparse parts. Although it performs well, we found that the sparse measurement of a subproblem which is determined by the m-nearest (m is the dimensional of the object space) neighbors of its solution can be more appropriately defined. In this work, the neighborhood relationship between subproblems is defined by using Delaunay triangulation (DT) of the points in the population.

MOEA/D with uniform decomposition measurement for many-objective problems

Soft Computing, 2014

Hybrid Immune Algorithm with EDA for Multi-Objective Optimization

Journal of Software, 2014

Memetic Immune Algorithm for Multiobjective Optimization

Journal of Software, 2014