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A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites

APPLIED SOFT COMPUTING 2023年 133卷

作者： Wang, Zhenwu Zhang, Wenteng Guo, Yinan Han, Mengjie Wan, Benting Liang, Shangchao China Univ Min & Technol Dept Comp Sci & Technol Beijing 100083 Peoples R China China Univ Min & Technol Dept Intelligent Control & Robot Beijing 100083 Peoples R China Dalarna Univ Sch Informat & Engn S-79188 Falun Sweden Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Peoples R China

multi-objective optimization problems (MOPs) that widely exist in real world concern all optimal solutions compromised among multiple objectives. Chicken swarm optimization algorithm derived from emergent behaviors of organisms provides an effective way for handling MOPs. To speed up convergence and improve uniformity of Pareto-optimal solutions, a multi-objective chicken swarm optimization algorithm based on dual external archives and boundary learning strategy (MOCSODABL) is proposed in this paper. Dual external archives are employed to distinguish and choose two types of elite solutions, with the purpose of more effectively guiding individual evolution. A boundary learning strategy guides the chickens to learn from boundary individuals in the later stage of evolution. Moreover, fast non-dominated sorting is adopted to establish the hierarchical social structure of a chicken population, and learning strategies of roosters, hens and chicks are improved to meet the requirements of MOPs. Experimental results on 14 benchmark functions show that the proposed MOCSO-DABL outperforms other five state-of-the-art algorithms significantly.& COPY;2022 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization problem Meta-heuristic Chicken swarm optimization Pareto dominance

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multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

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ENERGY 2024年 294卷

作者： Tian, Ai-Qing Wang, Xiao-Yang Xu, Heying Pan, Jeng-Shyang Snasel, Vaclav Lv, Hong-Xia Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

This paper establishes a multi -objective optimization model for railway heavy -haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy -haul trains, introducing multi -objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon -inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi -objective transportation problems, the paper proposes a forward -learning pigeon -inspired optimization algorithm based on a surrogate -assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward -learning strategy to enhance learning from non -dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains.

关键词： Carbon emissions Transport efficiency multi-objective optimization problem Surrogate-assisted model Forward-learning strategy

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optimization Study of Line Planning for High Speed Railway Based on an Improved multi-objective Differential Evolution Algorithm

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IEEE ACCESS 2019年 7卷 137731-137743页

作者： Tian, Huixin Shuai, Minwei Li, Kun Tianjin Polytech Univ Sch Elect Engn & Automat Key Lab Adv Elect Engn & Energy Technol Tianjin 300387 Peoples R China Tianjin Polytech Univ Sch Econ & Management Tianjin 300387 Peoples R China

The combination optimization of the train operation plan is an ongoing challenge: while computing power has improved, it is difficult to obtain a complete train operation plan system. With the aim of generating system-optimal operation strategies, a new collaborative optimization method is proposed for line planning problem. Through a set of constraints, the problem is formulated as a two-objective model with the objectives of economic benefits and market effects. An optimization approach with adaptive improvement of control parameters based on the multi-objective differential evolution (MODE) algorithm is proposed to solve the model, and a heuristic algorithm is designed to get a better initial solution. Finally, computational results on benchmark multi-objective problems show that the improvements of the strategies are positive and the optimization result of the improved algorithm has better stability. Meanwhile, based on a numerical example of a practical case study involving a 397-kilometer railway corridor to demonstrate the effectiveness of the proposed model and solution. As the basis of successive decisions, this method can adjust the number of trains according to the passenger fiow demand, which greatly saves operating costs.

关键词： Line planning high-speed railway collaborative optimization multi-objective optimization problem MODE

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A multi-objective optimization approach for beam pattern synthesis of UAV virtual rectangular antenna array

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第2期37卷

作者： Mei, Fang Guo, Xinrong Kang, Hui Sun, Geng Zheng, Tingting Wen, Jianbo Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Peoples R China Jilin Univ Coll Software Changchun Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

Virtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi-objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi-objective multi-verse optimization algorithm based on the inverse S decline curve type (ISDT-MOMVO) which introduces a strategy optimization initialization solution with quasi-opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT-MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

关键词： directivity of mainlobe multi-objective optimization problem sidelobe level UAV virtual rectangular antenna array VAA thinning

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An Improved multi-objective optimization for Workflow Scheduling in Cloud Platform

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JOURNAL OF INTERNET TECHNOLOGY 2017年第3期18卷 589-599页

作者： Prathibha, Soma Latha, B. Sumathi, G. Sri Sai Ram Engn Coll Dept Informat Technol Madras Tamil Nadu India Anna Univ Madras Tamil Nadu India Sri Sai Ram Engn Coll Dept Comp Sci & Engn Madras Tamil Nadu India Sri Venkateswara Coll Engn Dept Informat Technol Madras Tamil Nadu India

Cloud computing is a promising research domain used to enhance the performance of information systems and applications used at our daily life. Workflow scheduling in cloud platform is a complex optimization problem because of its heterogeneous nature, complex billing models and infinite resources requirements which has lead the users to look for an optimal or a suboptimal solution that satisfy multiple competing goals such as minimum makespan and energy efficient solutions. The focus of this paper is to schedule workflow applications, optimizing makespan and energy consumption. A high-performance method for scheduling workflow on cloud computing platform will be presented in this paper, called non-dominated sorting particle swarm optimization (NSPSO). The proposed method is evaluated on synthetic representation of real world scientific workflow applications. The simulation results shows that the proposed method can provide better workflow scheduling solution for cloud when compared with other state-of-the-art methods.

关键词： Cloud computing Workflow scheduling multi-objective optimization problem

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A multi-objective modeling approach to harvesting resource scheduling: Decision support for a more sustainable Thai sugar industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷 107694-107694页

作者： Jarumaneeroj, Pisit Dusadeerungsikul, Puwadol Oak Chotivanich, Tharin Akkerman, Renzo Chulalongkorn Univ Dept Ind Engn Bangkok 10330 Thailand Chulalongkorn Univ Reg Ctr Mfg Syst Engn Bangkok Thailand Purdue Univ PRISM Ctr W Lafayette IN 47907 USA Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Wageningen Univ Operat Res & Logist Grp Wageningen Netherlands

This paper develops a multi-objective modeling approach for the scheduling of harvesting resources in the Thai sugar industry, in which different objectives stemming from different industry stakeholders are concurrently optimized with the overall goal to create a more sustainable sugar supply chain. In addition to traditional economic objectives, the environmental impact of sugarcane farm burning is included into the model to better reflect the current harvesting practice, where sugarcane growers often resort to burning their fields due to the lack of available harvesting resources during the season. An evolutionary algorithm based on a variant of Particle Swarm optimization (PSO) is also devised to help solve the resulting multi-objective Harvesting Resource Scheduling problem (MOHRSP), which normally becomes intractable for real-life problem instances. We find that the proposed PSO framework is notably efficient as it provides diverse sets of non-dominated solutions with markedly low coefficients of variation in a reasonable amount of time. We also find that, by sacrificing a slight amount of sugar production volume, the whole sugar supply chain could be largely improved, especially for the sugarcane growers, whose profitability turns out to be sensitive in the trade-offs with other objectives.

关键词： Sugarcane Harvesting resource scheduling multi-objective optimization problem Evolutionary algorithm Particle swarm optimization Sustainability

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A shallow water mooring system design methodology combining NSGA-II with the vessel-mooring coupled model

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OCEAN ENGINEERING 2019年 190卷 106417-000页

作者： Liang, Mingxiao Wang, Xuefeng Xu, Shengwen Ding, Aibing Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China

In this paper, a mooring system design methodology combining the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with the vessel-mooring coupled model is first proposed. In the proposed methodology, the multi-objective optimization problem for mooring system design which involves several design variables, objective functions and constraints is established and solved by the combination of the NSGA-II algorithm and the vessel-mooring coupled model, and the initial feasible range of design variables is narrowed in advance by combining the NSGA-II with the quasi-static mooring system model. A case study is presented to demonstrated the proposed methodology, in which a catenary mooring system is designed from scratch for a semi-submersible very large floating structure (VLFS) module. It can be concluded from the obtained results that, owing to the combination of the NSGA-II algorithm and the vessel-mooring coupled model, the accurate dynamic responses of vessel and mooring systems can be used as the objective functions;thus, the proposed methodology is able to attain an optimal mooring system considering more practical design requirements, such as the vessel motion response, the safety factor of the mooring system, the length of mooring line lay down part and mooring system layout.

关键词： Mooring system design methodology Vessel-mooring coupled model multi-objective optimization problem NSGA-II

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MPSO-Based Model of Train Operation Adjustment

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Procedia Engineering 2016年 137卷 114-123页

作者： Lanxia Zhang Yong Qin Xuelei Meng Li Wang Tao Zhu School of traffic and transportation Beijing Jiaotong University Beijing 100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044 China Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing Jiaotong University Beijing 100044 China School of traffic and transportation Lanzhou Jiaotong University Lanzhou 730070 China Information Technology for Chinese Railway Beijing 100844 China

Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multi-objective optimal model of train operation adjustment, whose optimization objective is to reduce the train delay time and the numbers of delay train. Since the model is established as an NP complete problem, a multi-objective particle swarm optimization algorithm (MPSO) is proposed to solve the complex problem. Considering the strategy of dispatcher’ preference, MPSO can get a set of Pareto solutions in the actual train operation adjustment problems. The actual experiment, taking Beijing-Shanghai high-speed railway as example, is conducted to validate the feasibility of the algorithm compared with the basic particle swarm optimization algorithm (PSO). Results demonstrate that the model can capture the characteristics of the practical dispatching problem. MPSO is efficient for train operation adjustment and provides better solutions than the traditional approaches.

关键词： Train operation adjustment multi-objective optimization problem MPSO

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An Improved Method for Scheduling Aircraft Ground Handling Operations From a Global Perspective

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2019年第4期36卷 1950020.1-1950020.25页

作者： Padron, Silvia Guimarans, Daniel TBS Business Sch Toulouse France Monash Univ Fac Informat Technol Data Sci & Artificial Intelligence Grp Melbourne Vic Australia

The turnaround is a critical airport process where a set of interrelated operations need to be performed to get an aircraft ready for its next flight. These activities are carried out by different vehicles, which need to be coordinated to guarantee an efficient utilization of resources. Due to the relations between operations, the order in which these resources are scheduled has a critical influence on the planning and performance of the turnaround. In this work, we present a novel methodology for solving the proposed bi-objective ground handling scheduling problem from a global perspective. This means solving a set of interconnected routing problems with restrictive time windows for each operation. We first explore the solution space using a fast heuristic, focusing then on the most promising solutions to intensify the search in the vicinity of the Pareto frontier. This two-step schema permits significantly reducing the required computational time, which, in turn, allows a more thorough exploration of solutions. Different experiments over real data from two Spanish airports have been conducted to assess the proposed methodology. Our results show that the new method not only outperforms previous approaches in terms of computational requirements, but can also improve the quality of scheduling solutions.

关键词： Air transportation multi-objective optimization problem Pareto solutions Vehicle Routing problem with Time Windows

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A multi-objective Covering Salesman problem with 2-coverage

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APPLIED SOFT COMPUTING 2021年第PartA期113卷 108024-108024页

作者： Tripathy, Siba Prasada Biswas, Amiya Pal, Tandra Natl Inst Sci & Technol Sch Comp Sci Berhampur Odisha India Durgapur Govt Coll Dept Math Durgapur 713214 India Natl Inst Technol Durgapur Dept Comp Sci & Engn Durgapur India

In a wide range of contexts including military operations, environment monitoring, surveillance in border areas, health care, public safety [1, 2], disaster management, humanitarian relief and blood supply chain, a robust solution of the Covering Salesman problem (CSP) is necessary. These applications require more than one facilities to cover a given customer (region of interest (ROI)). In this paper, we consider the coverage radius to be fixed and same for each node. Then we propose a multi-objective algorithm based on NSGA-II, in which minimization of tour length and maximization of number of nodes with 2-coverage are considered as the objectives. For implementing the algorithm, an individual chromosome is represented using a one-dimensional array with variable length, and developed a new crossover and a new mutation operator based on the problem and variable length chromosome representation. Numerical examples taken from TSP instances (TSPLIB [3]) with number of nodes ranging from 51 to 150 are solved using the algorithm. For each numerical example, the tour corresponding to the solution with 2-coverage of customer nodes is presented, which shows that the proposed algorithm is effective. Finally, a potential future research direction of this problem is discussed. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Covering Salesman problem 2-coverage NSGA-II multi-objective optimization problem

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