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Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization Research

Wan-ying Ruan, Hai-bin Duan,wyruan@buaa.edu.cn,hbduan@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 5,   Pages 649-808 doi: 10.1631/FITEE.2000066

Abstract: We propose multi-objective social learning (MSLPIO) and apply it to for formation. In the algorithm, each pigeon learns from the better pigeon but not necessarily the global best one in the update process. A social learning factor is added to the map and compass operator and the landmark operator. In addition, a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting. We simulate the flight process of five UAVs in a complex obstacle environment. Results verify the effectiveness of the proposed method. MSLPIO has better convergence performance compared with the improved multi-objective and the improved non-dominated sorting genetic algorithm.

Keywords： 无人机；避障；鸽群优化；多目标社会学习鸽群优化    

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A review of the multiobjective tradeoff research of construction projects based on intelligent optimization algorithm

Zhang Lianying,Xu Chang,Wu Qiong

Strategic Study of CAE 2012, Volume 14, Issue 11,   Pages 107-112

Abstract:

The optimal equilibrium between the multiple objectives of construction projects is a significant aspect of project management research, which has seen rapid development in recent years, gaining a bunch of fruitful achievements. In this paper, a review is provided for the multiobjective tradeoff research of construction projects based on literature review. Models under deterministic conditions and nondeterministic conditions are investigated and summarized. Some suggestions on the possible direction for future research are included considering the algorithms adopted in the problem solution. This paper aims at providing a review of the achievements in this area so far and keeping track of the ongoing research topics so as to give certain indications for research that follows.

Keywords： construction project     project management     multi-objective optimization     intelligent optimization algorithm    

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Evolutionary Algorithms for Multi-objective Optimization and Decision-Making Problems

Xie Tao Chen Huowang

Strategic Study of CAE 2002, Volume 4, Issue 2,   Pages 59-68

Abstract:

Multi-objective optimization (MOO) and decision-making (DM) has become an important research area of evolutionary computations in recent years. The researches on multi-objective evolutionary algorithms (MOEA) focus mainly on the Pareto-based comparison and ordering of individuals, fitness assignment and Riching techniques, etc., so that the population can converge and uniformly distribute in the Pareto front. This paper presents an introduction to the history and classification of multi-objective optimization and decision-making techniques, analyzes both the Pareto-based and non-Pareto-based evolutionary algorithms, and,particularly,the five well-known MOEAs. Some problems related to the researches on MOEAs are addressed in details, such as the characteristics of Pareto front, the test suite and performance evaluation of MOEAs, the MOEA convergence analysis, the MOEA parallelization, and the disposal of real world MOO problems.

Keywords： evolutionary algorithms     multi-objective optimization and decision-making     Pareto optimal    

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Network Planning Multi-objective Optimization Based on Differential Evolution

Li Gaoyang,Wu Yuhua,Liu Mingguang

Strategic Study of CAE 2006, Volume 8, Issue 6,   Pages 60-63

Abstract:

Based on the synthetic study of costing, quality and construction period, the multi-objective optimal model considering the maximal pure value and quality of construction project is made in order to improve economic benefit of construction enterprise. Then, a new method called differential evolution is introduced into the multi-objective optimization. The model and validity of this algorithm are tested through project case.

Keywords： network planning     multi-objective optimization     differential evolution     net profit     quality    

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Fuzzy Optimum Selection Dynamic Programming Methodology for Multi-objective Optimization of Multi-stage Systems

Xiong Deqi,Yin Peihai

Strategic Study of CAE 2000, Volume 2, Issue 9,   Pages 65-69

Abstract:

Based on the concepts of the fuzzy weighted distance and membership degree, the fuzzy optimum selection dynamic programming technique that can be used for the optimization of multi-objective and multi-stage systems are developed by means of the combination of fuzzy optimum selection theory with dynamic programming technique. This is a new methodology for solving the multi-objective optimization problems of multi-stage systems. Finally, an application to the optimization of a multiple reactor system is given as an example.

Keywords： multi-stage     multi-objective optimization     fuzzy optimum selection     dynamic programming     membership degree    

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Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization Research Article

Jian DONG, Xia YUAN, Meng WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9,   Pages 1390-1406 doi: 10.1631/FITEE.2100420

Abstract: We propose a competitive binary (CBMOGWO) to reduce the heavy computational burden of conventional multi-objective problems. This method introduces a population competition mechanism to reduce the burden of electromagnetic (EM) simulation and achieve appropriate fitness values. Furthermore, we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary (BMOGWO) to achieve a good balance between exploration and exploitation. Then, the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems (MOTPs) and four multi-objective knapsack problems (MOKPs) by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization (BMOPSO). Finally, the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output (MIMO) antenna with high-dimensional mixed design variables and multiple objectives. The experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods, which indicates that our method is highly efficient for complex problems. It provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms (MOEAs) in a flexible and efficient manner.

Keywords： Antenna topology optimization     Multi-objective grey wolf optimizer     High-dimensional mixed variables     Fast design    

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A Multi-objective Optimization Decision-making Model for Project Time - resource Tradeoff Problem

Wang Xianjia,Wan Zhongping

Strategic Study of CAE 2005, Volume 7, Issue 2,   Pages 35-40

Abstract:

In the project scheduling and management, the time-resource tradeoff problem is to seek the objective of minimizing the project duration and the total consumed-resources cost under the requirement of the absolute due date of project, and determine an efficient project scheduling according to some precedence relationship and the renewable resource constraints. A new multi-objective optimization decision-making model with time-resource tradeoff problem is proposed, in which objective functions with conflict one another are defined as adaptive and adjustable between the project duration and the total consumed-resources cost in all period. A satisfied feasible solution can be obtained in the solution procedure by compromising and adjusting relationship between the project duration and the total consumed-resource cost. A numerical example is illustrated. In addition, some characteristics on this two-player game are given in the corresponding Lagrangian relaxation form associated with the resource constraints.

Keywords： project scheduling management     time-resource tradeoff     multiobject decision-making model     resource-constrained     project scheduling     Lagragian relaxation    

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Associative affinity network learning for multi-object tracking Research Articles

Liang Ma, Qiaoyong Zhong, Yingying Zhang, Di Xie, Shiliang Pu,maliang6@hikvision.com,zhongqiaoyong@hikvision.com,zhangyingying7@hikvision.com,xiedi@hikvision.com,pushiliang.hri@hikvision.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1194-1206 doi: 10.1631/FITEE.2000272

Abstract: We propose a joint feature and metric learning architecture, called the associative affinity network (AAN), as an affinity model for (MOT) in videos. The AAN learns the associative affinity between tracks and detections across frames in an end-to-end manner. Considering flawed detections, the AAN jointly learns bounding box regression, classification, and affinity regression via the proposed multi-task loss. Contrary to networks that are trained with ranking loss, we directly train a binary classifier to learn the associative affinity of each track-detection pair and use a matching cardinality loss to capture information among candidate pairs. The AAN learns a discriminative affinity model for data association to tackle MOT, and can also perform single-object tracking. Based on the AAN, we propose a simple multi-object tracker that achieves competitive performance on the public MOT16 and MOT17 test datasets.

Keywords： 多目标跟踪；深度神经网络；相似度学习    

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Multi-objective particle swarm cooperative optimization algorithm for state parameters

Ding Lei,Wu Min,She Jinhua,Duan Ping

Strategic Study of CAE 2010, Volume 12, Issue 2,   Pages 101-107

Abstract:

To deal with the characters with the strong nonlinear and complex computing of synthetic permeability and burn-through point in the lead-zinc sintering process, an efficient multi-objective particle swarm cooperative optimization algorithm is proposed. Firstly, the multi-objective optimization model for burn-through point and synthetic permeability is established. Secondly, an improved multi-objective particle swarm cooperative optimization algorithm is presented by improving the constraint comparison method and the way of selecting the particles' optima, and using different swarms to optimize corresponding variables respectively. Finally, the proposed multi-objective optimization algorithm is applied to optimize the synthetic permeability and the burn-through point. The simulation results show that the proposed multi-objective optimization algorithm effectively solves the optimization problem of the synthetic permeability and burn-through point.

Keywords： lead-zinc sintering process     synthetic permeability     burn-through point     multi-objective particle swarm cooperative optimization algorithm    

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Study on the project multiple-objectives coordination

Liu Xiaofeng,Chen Tong, Wu Shaoyan

Strategic Study of CAE 2010, Volume 12, Issue 3,   Pages 90-94

Abstract:

The Particle Swarm Optimization (PSO) is an evolutionary computation, which not only can search solutions randomly and fully, but also is convenient to be carried out. Hence, the article focuses on the application of PSO to the multiple-objective coordination optimization of project, looking forward to seeking best solutions easily and quickly. After introducing the basic theory of the algorithms and its several versions, the article aims at the efficiency coefficient of quality, cost, time and resource subsystem, and set up a multiple optimization coordination model. In the following part, the article introduces how to apply PSO to solve the project coordination optimization problem in detail. The numeric example followed indicates that PSO can solve the multiple-objectives coordination optimization problem of project exactly and quickly.

Keywords： Particle Swarm Optimization (PSO)     project management     efficiency coefficient of coordination     multiple optimization coordination model     numeric example    

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A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms Article

Arun Pankajakshan, Conor Waldron, Marco Quaglio, Asterios Gavriilidis, Federico Galvanin

Engineering 2019, Volume 5, Issue 6,   Pages 1049-1059 doi: 10.1016/j.eng.2019.10.003

Abstract:

Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many manufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of multiple objective functions related to process performance and cost is necessary. In this work, a multiobjective optimal experimental design framework is proposed to enhance the efficiency of online model-identification platforms. The proposed framework permits flexibility in the choice of trade-off experimental design solutions, which are calculated online—that is, during the execution of experiments. The application of this framework to improve the online identification of kinetic models in flow reactors is illustrated using a case study in which a kinetic model is identified for the esterification of benzoic acid and ethanol in a microreactor.

Keywords： Multi-objective optimization     Optimal design of experiments     Online    

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A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization Research Articles

Ming-gang Dong, Bao Liu, Chao Jing,jingchao@glut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8,   Pages 1119-1266 doi: 10.1631/FITEE.1900321

Abstract: The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design, overlapping community detection, power dispatch, and unmanned aerial vehicle formation. To address such issues, current approaches focus mainly on problems with regular Pareto front rather than solving the . Considering this situation, we propose a many-objective evolutionary algorithm based on decomposition with (MaOEA/D-DRA) for irregular optimization. The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front. An evolutionary population and an are used in the search process, and information extracted from the is used to guide the evolutionary population to different search regions. The evolutionary population evolves with the to decompose a problem into several subproblems, and all the subproblems are optimized in a collaborative manner. The is updated with the method of . The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms using a variety of test problems with . Experimental results show that the proposed algorithm outperforms these five algorithms with respect to convergence speed and diversity of population members. By comparison with the weighted-sum approach and penalty-based boundary intersection approach, there is an improvement in performance after integration of the into the proposed algorithm.

Keywords： Many-objective optimization problems     Irregular Pareto front     External archive     Dynamic resource allocation     Shift-based density estimation     Tchebycheff approach    

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Multiobjective Decision Making Theory and Model for Floodcontrol Operation

Chen Shouyu

Strategic Study of CAE 2000, Volume 2, Issue 2,   Pages 47-52

Abstract:

Analyzing the status of floodcontrol operation in china, this paper sumarized decision making problems of floodcontrol and proposed multiobjective decision making theory, model and methods. These include some new achievements such as multiobjective fuzzy optimum seeking theory and model for multilayer system ,definition of objective weight etc. some of the theory and model were applied to reservoirs floodcontrol operations and satisfactory results were achieved.

Keywords： Floodcontrol operation     decision making     multiobjective     objective weight    

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Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting Article

Jia Shi, Jinchun Song, Bin Song, Wen F. Lu

Engineering 2019, Volume 5, Issue 3,   Pages 586-593 doi: 10.1016/j.eng.2018.12.009

Abstract:

Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its highthroughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs; and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multisubgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s-1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms.

Keywords： Drop-on-demand printing     Inkjet printing     Gradient descent multi-objective optimization     Fully connected neural networks    

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An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

Xin LI,Jin SUN,Fu XIAO,Jiang-shan TIAN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 2,   Pages 160-172 doi: 10.1631/FITEE.1500168

Abstract:

With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method.Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.

Keywords： Parameter variations     Parametric yield     Multi-objective optimization     Chebyshev affine     Adaptive weighted sum    

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Title Author Date Type Operation

Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization

Wan-ying Ruan, Hai-bin Duan,wyruan@buaa.edu.cn,hbduan@buaa.edu.cn

Journal Article

A review of the multiobjective tradeoff research of construction projects based on intelligent optimization algorithm

Zhang Lianying,Xu Chang,Wu Qiong

Journal Article

Evolutionary Algorithms for Multi-objective Optimization and Decision-Making Problems

Xie Tao Chen Huowang

Journal Article

Network Planning Multi-objective Optimization Based on Differential Evolution

Li Gaoyang,Wu Yuhua,Liu Mingguang

Journal Article

Fuzzy Optimum Selection Dynamic Programming Methodology for Multi-objective Optimization of Multi-stage Systems

Xiong Deqi,Yin Peihai

Journal Article

Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization

Jian DONG, Xia YUAN, Meng WANG

Journal Article

A Multi-objective Optimization Decision-making Model for Project Time - resource Tradeoff Problem

Wang Xianjia,Wan Zhongping

Journal Article

Associative affinity network learning for multi-object tracking

Liang Ma, Qiaoyong Zhong, Yingying Zhang, Di Xie, Shiliang Pu,maliang6@hikvision.com,zhongqiaoyong@hikvision.com,zhangyingying7@hikvision.com,xiedi@hikvision.com,pushiliang.hri@hikvision.com

Journal Article

Multi-objective particle swarm cooperative optimization algorithm for state parameters

Ding Lei,Wu Min,She Jinhua,Duan Ping

Journal Article

Study on the project multiple-objectives coordination

Liu Xiaofeng,Chen Tong, Wu Shaoyan

Journal Article

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms

Arun Pankajakshan, Conor Waldron, Marco Quaglio, Asterios Gavriilidis, Federico Galvanin

Journal Article

A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

Ming-gang Dong, Bao Liu, Chao Jing,jingchao@glut.edu.cn

Journal Article

Multiobjective Decision Making Theory and Model for Floodcontrol Operation

Chen Shouyu

Journal Article

Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting

Jia Shi, Jinchun Song, Bin Song, Wen F. Lu

Journal Article

An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

Xin LI,Jin SUN,Fu XIAO,Jiang-shan TIAN

Journal Article