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constrained multi-objective optimization of short-term crude oil scheduling with dual pipelines and charging tank maintenance requirement

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INFORMATION SCIENCES 2022年 588卷 381-404页

作者： Hou, Yan Zhang, YiXian Wu, NaiQi Zhu, QingHua Guangdong Univ Technol Sch Comp Sci Guangzhou 510006 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Taipa 999078 Macao Peoples R China

For the short-term crude oil scheduling problem, it is difficult to guarantee the feasibility of a schedule due to complicated constraints. Meanwhile, uncertainty is a very important concern in refineries, such as unexpected breakdown of charging tanks. Therefore, it is a great challenge to make a schedule feasible. Most existing works on multi-objective opti-mization of short-term crude oil scheduling are developed for refineries processing low-fusion-point oil (L-oil) only and little is done for the case with dual pipelines for processing both L-oil and high-fusion-point oil (H-oil). With five objectives and many constraints, it is challenging for a metaheuristic algorithm to find a feasible schedule. To solve this problem, in this work, constraint violation is used to describe the degree of constraint violation. Thus, an adaptive enhanced selection pressure algorithm based on NSGA-II-CDP (NSGA-II-APE) is proposed to efficiently solve the problem for processing both L-oil and H-oil. This algorithm can effectively enhance the selection pressure in the later iterations. Industrial case problems are used to test the proposed method and compare its perfor-mance with 11 state-of-the-art constrained multi-objective evolution algorithms (CMOEAs). Results show its superiority over the existing ones in terms of convergence, solution diversity, and time efficiency.(c) 2021 Elsevier Inc. All rights reserved.

关键词： Crude oil operations Short-term scheduling Heuristic strategy constrained multi-objective optimization

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constrained multi-objective optimization algorithm with an ensemble of constraint handling methods

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ENGINEERING optimization 2011年第4期43卷 403-416页

作者： Qu, B. Y. Suganthan, P. N. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Different constraint handling techniques have been used with multi-objective evolutionary algorithms (MOEA) to solve constrained multi-objective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of the parameter tuning. To overcome this selection problem, an ensemble of constraint handling methods (ECHM) is used to tackle constrained multi-objective optimization problems. The ECHM is integrated with a multi-objective differential evolution (MODE) algorithm. The performance is compared between the ECHM and the same single constraint handling methods using the same MODE (using codes available from http://***/home/EPNSugan/***). The results show that ECHM overall outperforms the single constraint handling methods.

关键词： multi-objective evolutionary algorithm multi-objective differential evolution constrained multi-objective optimization ensemble of constraint handling methods

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constrained multi-objective optimization problems: Methodologies, algorithms and applications

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KNOWLEDGE-BASED SYSTEMS 2024年 299卷

作者： Hao, Yuanyuan Zhao, Chunliang Zhang, Yiqin Cao, Yuanze Li, Zhong Fern univ Hagen Fac Math & Comp Sci D-58097 Hagen Germany Qingdao Univ Sci & Technol Sch Data Sci 99 Songling Rd Qingdao 266061 Peoples R China Minnan Normal Univ Sch Math & Stat Zhangzhou Peoples R China Beijing Jiaotong Univ Sch Syst Sci 3 Shangyuan Village Beijing 100028 Peoples R China

constrained multi -objective optimization problems (CMOPs) are widespread in practical applications such as engineering design, resource allocation, and scheduling optimization. It is high challenging for CMOPs to balance the convergence and diversity due to conflicting objectives and complex constraints. Researchers have developed a variety of constrained multi -objective optimization algorithms (CMOAs) to find a set of optimal solutions, including evolutionary algorithms and machine learning-based methods. These algorithms exhibit distinct advantages in solving different categories of CMOPs. Recently, constrained multi -objective evolutionary algorithms (CMOEAs) have emerged as a popular approach, with several literature reviews available. However, there is a lack of comprehensive-view survey on the methods of CMOAs, limiting researchers to track the cutting-edge investigations in this research direction. Therefore, this paper reviews the latest algorithms for handling CMOPs. A new classification method is proposed to divide literature, containing classical mathematical methods, evolutionary algorithms and machine learning methods. Subsequently, it reviews the modeling and algorithms of CMOPs in the context of practical applications. Lastly, the paper gives potential research directions with respect to CMOPs. This paper is able to provide guidance and inspiration for scholars studying CMOPs.

关键词： constrained multi-objective optimization problems Evolutionary algorithms Machine learning Applications

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constrained multi-objective optimization for UAV-Enabled Mobile Edge Computing: Offloading optimization and Path Planning

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第4期11卷 861-865页

作者： Peng, Chaoda Huang, Xumin Wu, Yuan Kang, Jiawen South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

An unmanned aerial vehicle (UAV) is employed to sequentially visit the specific waypoints and provide offloading services for nearby devices. Most of the current works optimized the UAV-enabled offloading according to a single criterion while neglecting necessary optimizations and constraints for flight safety of the UAV. This motivates us to study the optimization problem of the UAV from a multi-objective viewpoint by considering the UAV's flight safety. A constrained multi-objective optimization problem (CMOP) involving two objective functions about the energy-efficient offloading and safe path planning is formulated for the UAV. To solve the formulated CMOP, we present a constrained decomposition-based multi-objective evolution algorithm. To further improve the algorithm, we particularly utilize the infeasible individuals with great objective values, which provide useful information for improving the optimized objective values during the evolution process. Finally, experimental results demonstrate that compared with the existing works, our scheme is beneficial to simultaneously reduce energy consumption and ensure safe flight for the UAV.

关键词： Autonomous aerial vehicles optimization Task analysis Trajectory Three-dimensional displays Safety Linear programming Computation offloading 3D path planning constrained multi-objective optimization and evolutionary algorithm

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Even Search in a Promising Region for constrained multi-objective optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 474-486页

作者： Fei Ming Wenyin Gong Yaochu Jin School of Computer Science China University of GeosciencesWuhan 430074China IEEE Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia 33619Germany School of Engineering Westlake UniversityHangzhou 310030China

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general ***, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

关键词： constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems

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constrained multi-objective optimization evolutionary algorithm for real-world continuous mechanical design problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Ming, Fei Gong, Wenyin Zhen, Huixiang Wang, Ling Gao, Liang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

During the past two decades, evolutionary algorithms have seen great achievements in solving complex optimization problems owing to the advantages brought by their properties, especially constrained multiobjective optimization problems (CMOPs) with multiple conflicting objective functions and constraints which widely exist in industry, scientific research, and daily life. Among the real -world CMOPs, mechanical design problems (MDPs) from the industry widely exist and are important, while unfortunately, most constrained multi -objective evolutionary algorithms (CMOEAs), developed based on benchmark CMOPs, neglect the specific features and challenges of MDPs and thus cannot solve them well to provide the practitioners promising Pareto optimal solutions for decision making. To overcome this limitation, this paper analyzes the features and challenges of MDPs, including badly scaled objective space, decision space properties, and decision variable linkages. Then, we propose a new CMOEA named CMORWMDP. First, instead of the homogeneous operator in existing CMOEAs, a heterogeneous operator strategy is adopted to use the operator of Genetic Algorithm to enhance the convergence and the operator of Differential Evolution to tackle variable linkages. In addition, an improved fitness function that considers normalization is designed for environmental and mating selections. The proposed algorithm is simple, parameter -free, and easy to implement. Experiments on 21 real -world MDPs show its superiority compared to 20 state-of-the-art CMOEAs under the Friedman test and Wilcoxon test on different metrics, demonstrating the effectiveness of the heterogeneous operator and normalization -based fitness for selections for real -world MDPs. Moreover, the effectiveness of the proposed algorithm in solving other real -world CMOPs is also verified, revealing that our methods are very promising in tackling real -world problems.

关键词： constrained multi-objective optimization Evolutionary algorithm Real-world optimization problems Mechanical design Heterogeneous operator Normalization

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constrained multi-objective optimization with dual-swarm assisted competitive swarm optimizer

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Wang, Yubo Hu, Chengyu Gong, Wenyin Ming, Fei China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

Many metaheuristic methods have been proposed and have shown promising performance in solving multiobjective optimization problems (MOPs). As a representative example, the competitive swarm optimizer (CSO) exhibits excellent performance in solving large-scale MOPs. However, CSO performs poorly when used straightforwardly for constrained MOPs with complex constraints or objective spaces. In this paper, we propose a dual -swarm -based CSO framework that co -evolves two swarms with different orientations: (i) CSO is improved using genetic algorithms, namely CSO-GA to form a convergence -directed swarm without considering constraints to accelerate the convergence. (ii) CSO is further improved by differential evolution, namely CSO-DE to form a search -directed swarm by considering constraints to enhance diversity and improving the search for the constrained Pareto front. Moreover, a new constraint relaxation method is proposed that combines the particle swarm feasible rate and current evolutionary generation to dynamically adjust the factor for decreasing or increasing constraint relaxation. Extensive experiments with 37 benchmark instances and nineteen real -world constrained MOPs demonstrate that our proposed algorithm yields significantly better or at least more competitive results than ten state-of-the-art methods.

关键词： constrained multi-objective optimization problems Competitive swarm optimizer Convergence-directed swarm Search-directed swarm Constraint relaxation

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constrained multi-objective optimization via two archives assisted push-pull evolutionary algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期75卷

作者： Qin, Cifeng Ming, Fei Gong, Wenyin Gu, Qiong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Peoples R China

Solving constrained multi-objective optimization problems (CMOPs) obtained more and more popularity in the last decade. Various constrained multi-objective optimization evolutionary algorithms were developed for the CMOPs. However, most of them are ineffective in dealing with CMOPs with complex infeasible regions. In this paper, the two archives assisted push-pull evolutionary algorithm (namely PPTA) is proposed to handle the CMOPs with complex infeasible regions effectively. PPTA has the following features: (i) Two archives (A and B) are used, where A is used in the push stage to promote convergence and diversity, and in the pull stage to explore undetected feasible regions;in the pull stage B is used for the promotion of convergence, diversity, and feasibility. (ii) A new push stage strategy is devised for better approximating the unconstrained Pareto front, as well as the constrained Pareto front. (iii) The aggregation function in the pull stage is improved to better explore the objective space. (iv) a new epsilon-constrained method is customized to enhance the ability to explore feasible regions. Results on 33 widely used instances and eight real-world problems verify the superiority of PPTA over 10 related methods.

关键词： constrained multi-objective optimization Evolutionary algorithm Push-pull Two archives epsilon-constrained technique

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Evolutionary multitasking With Global and Local Auxiliary Tasks for constrained multi-objective optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1951-1964页

作者： Kangjia Qiao Jing Liang Zhongyao Liu Kunjie Yu Caitong Yue Boyang Qu School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhou 450001China School of Electrical and Information Zhongyuan University of TechnologyZhengzhou 450007China

constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the *** solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

关键词： constrained multi-objective optimization evolutionary multitasking(EMT) global auxiliary task knowledge transfer local auxiliary task

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constrained multi-objective optimization OF HELIUM LIQUEFACTION CYCLE

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THERMAL SCIENCE 2024年第4期28卷 2777-2790页

作者： Shi, Min Shi, Tongqiang Shi, Lei Ouyang, Zhengrong Li, Junjie Chinese Acad Sci High Magnet Field Lab HFIPS Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Peoples R China Anhui Wanrui Cold Power Technol Co Ltd Hefei Peoples R China Shanghai Tech Univ Sch Phys Sci & Technol Shanghai Peoples R China

The helium cryo-plant is an indispensable subsystem for the application of low temperature superconductors in large-scale scientific facilities. However, it is important to note that the cryo-plant requires stable operation and consumes a substantial amount of electrical power for its operation. Additionally, the construction of the cryo-plant incurs significant economic costs. To achieve the necessary cooling capacity while reducing power consumption and ensuring stability and economic feasibility, constrained multi-objective optimization is performed using the interior point method in this work. The Collins cycle, which uses liquid nitrogen precooling, is selected as the representative helium liquefaction cycle for optimization. The discharge pressure of the compressor, flow ratio of turbines, and effectiveness of heat exchangers are taken as decision parameters. Two objective parameters, cycle exergy efficiency, eta ex,cycle , and liquefaction rate, m L , are chosen, and the wheel tip speed of turbines and UA of heat exchangers are selected as stability and economic cost constraints, , respectively. The technique for order of preference by similarity to the ideal solution (TOPSIS) is utilized to select the final optimal solution from the Pareto frontier of constrained multi-objective optimization. Compared to the constrained optimization of eta ex,cycle , the TOPSIS result increases the m L by 23.674%, but there is an 8.162% reduction in eta ex,cycle . Similarly, compared to the constrained optimization of m L , the TOPSIS result increases the eta ex,cycle by 57.333%, but a 10.821% reduction in m L is observed. This approach enables the design of helium cryo-plants with considerations for cooling capacity, exergy efficiency, economic cost, and stability. Furthermore, the wheel tip speed and UA of heat exchangers of the solutions in the Pareto frontier are also studied.

关键词： Collins cycle helium cryo-plants exergy efficiency constrained multi-objective optimization stability

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