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Optimal placement of fixed hub height wind turbines in a wind farm using twin archive guided decomposition based multi-objective evolutionary algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 130卷

作者： Raju, Sri Srinivasa M. Mohapatra, Prabhujit Dutta, Saykat Mallipeddi, Rammohan Das, Kedar Nath Natl Inst Technol Dept Math Silchar India Vellore Inst Technol Sch Adv Sci Dept Math Vellore Tamil Nadu India Acad Sinica Inst Stat Sci Taipei Taiwan Kyungpook Natl Univ Sch Elect Engn Dept Artificial Intelligence Daegu 41566 South Korea

Harnessing maximum wind energy's power output and efficiency is vital to combat environmental challenges tied to conventional fossil fuels. Wind power's cost-effectiveness and emission reduction potential underscore its significance. Efficient wind farm layout plays a pivotal role, both technically and commercially. evolutionary algorithms show their potential while solving multi-objective wind farm layout optimization problems. However, due to the large-scale nature of the problems, existing algorithms are getting trapped into local optima and fail to explore the search space. To address this, the TAG-DMOEA algorithm is upgraded with an adaptive offspring strategy (AOG) for better exploration. The proposed algorithm is employed on a wind farm layout problem with real-time data of wind speed and direction from two different locations. Unlike mixed hub heights, fixed hub heights such as 60, 67, and 78 m are adopted to conduct the case studies at two potential locations with real-time statistical data for the investigation of improved results. The results obtained by TAG-DMOEA-AOG on six cases are compared with 10 state-of-the-art algorithms. Statistical tests such as Friedman test and Wilcoxon signed rank test along with post hoc analysis (Nemenyi test) confirmed the superiority of the TAG-DMOEA-AOG on all cases of the considered multi-objective wind farm layout optimization problem.

关键词： Wind turbine multi-objective evolutionary algorithm Optimization Decomposition Weight vector

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Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

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ENERGY 2024年 311卷

作者： Ali, Aamir Bughio, Ateeq-u-Rehman Abbas, Ghulam Keerio, M. U. Mugheri, N. H. Memon, Shaina Saand, A. S. Quaid e Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67450 Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is *** scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for smallscale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary *** study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

关键词： Distribution network Distributed generation Battery energy storage system multi-objective evolutionary algorithm

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A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

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SWARM AND evolutionary COMPUTATION 2024年 87卷

作者： Hu, Yifan Zhang, Liping Wang, Qiong Zhang, Zikai Tang, Qiuhua Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Sch Math & Stat Wuhan 430074 Peoples R China

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

关键词： Flexible assembly job shop scheduling problem Cellular manufacture Matheuristic multi-objective evolutionary algorithm

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Transfer Learning Based multi-objective evolutionary algorithm for Dynamic Workflow Scheduling in the Cloud

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第4期12卷 1200-1217页

作者： Xie, Huamao Ding, Ding Zhao, Lihong Kang, Kaixuan Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Minist Educ Key Lab Big Data & Artificial Intelligence Transpo Beijing 100044 Peoples R China

Managing scientific applications in the Cloud poses many challenges in terms of workflow scheduling, especially in handling multi-objective workflow scheduling under quality of service (QoS) constraints. However, most studies address the workflow scheduling problem on the premise of the unchanged environment, without considering the high dynamics of the Cloud. In this paper, we model the constrained workflow scheduling in a dynamic Cloud environment as a dynamic multi-objective optimization problem with preferences, and propose a transfer learning based multi-objective evolutionary algorithm (TL-MOEA) to tackle the workflow scheduling problem of dynamic nature. Specifically, an elite-led transfer learning strategy is proposed to explore effective parameter adaptation for the MOEA by transferring helpful knowledge from elite solutions in the past environment to accelerate the optimization process. In addition, a multi-space diversity learning strategy is developed to maintain the diversity of the population. To satisfy various QoS constraints of workflow scheduling, a preference-based selection strategy is further designed to enable promising solutions for each iteration. Extensive experiments on five well-known scientific workflows demonstrate that TL-MOEA can achieve highly competitive performance compared to several state-of-art algorithms, and can obtain triple win solutions with optimization objectives of minimizing makespan, cost and energy consumption for dynamic workflow scheduling with user-defined constraints.

关键词： Dynamic scheduling Cloud computing Processor scheduling Heuristic algorithms Quality of service Costs Optimization Dynamic workflow scheduling multi-objective evolutionary algorithm transfer learning quality of service constraints preference optimization

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An active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第11期20卷 19839-19857页

作者： Guo, Xiaofang Wang, Yuping Zhang, Haonan Xian Technol Univ Sch Sci Xian 710000 Peoples R China

The inverse model based multi-objective evolutionary algorithm (IM-MOEA) generates offspring by establishing probabilistic models and sampling by the model, which is a new computing schema to replace crossover in MOEAs. In this paper, an active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy (ALGM-MOEA) is proposed. To properly cope with multi-objective problems with different shapes of Pareto front (PF), a novel population guided weight vector evolution strategy is proposed to dynamically adjust search directions according to the distribution of generated PF. Moreover, in order to enhance the search efficiency and prediction accuracy, an active learning based training sample selection method is designed to build Gaussian process based inverse models, which chooses individuals with the maximum amount of information to effectively enhance the prediction accuracy of the inverse model. The experimental results demonstrate the competitiveness of the proposed ALGM-MOEA on benchmark problems with various shapes of Pareto front.

关键词： active learning multi-objective evolutionary algorithm weight vector adjustment Gaussian regression model Pareto front

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A multi-objective evolutionary algorithm With Hierarchical Clustering-Based Selection

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IEEE ACCESS 2023年 11卷 2557-2569页

作者： Zhou, Shenghao Chen, Ze Li, Qi Gu, Mengjun Bao, Zhoucheng He, Wenda Sheng, Weiguo Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Hangzhou Normal Univ Engn Res Ctr Mobile Hlth Management Syst Hangzhou 311121 Peoples R China China Telecom Corp Ltd Zhejiang Branch Hangzhou 310022 Peoples R China

This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objective optimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental and mating selections, named local coverage selection and local area selection, respectively, for multi-objective evolutionary algorithm. The local coverage selection strategy aims to preserve well-distributed individuals with good convergence. While, the local area selection strategy is devised to deliver a balanced evolutionary search. This is achieved by encouraging individuals for exploration or exploitation according to the I-epsilon+ indicator. In both strategies, a hierarchical clustering method is employed to discover the population structure. Based on the clustering results, in local coverage selection, the individuals of different clusters will be retained according to their coverage areas and crowding distances, such that distributing as evenly as possible in the Pareto front. In local area selection, the individual(s) with the best value of I-epsilon+ indicator in each cluster will be selected to perform mating, with the purpose of achieving a balanced exploration and exploitation. The proposed algorithm has been evaluated on 26 bench-mark problems and compared with related methods. The results clearly show the significance of the proposed method.

关键词： multi-objective evolutionary algorithm multi-objective optimization problem hierarchical clustering

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Dual population multi-objective evolutionary algorithm for dynamic co-transformations

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evolutionary INTELLIGENCE 2024年第5-6期17卷 3269-3289页

作者： Yang, Yongkuan Yang, Yanxiang Liao, Binrong Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & In Xiamen 361024 Peoples R China

Solving constrained multi-objective optimization problems is a challenging task and existing algorithms have been struggling to balance constrained convergence and population diversity. To address this problem, this paper proposes a dual-population cooperative algorithm (DPDCA), which maintains two populations and an archive, and can change the search strategy depending on the evolutionary stage. At the early stage of evolution, the two populations are responsible for different tasks, promote rapid convergence of the populations by exchanging solutions, and save promising solutions in the archive. In the later stages of evolution, different local search strategies are emphasized according to the diversity of the main populations and their convergence to obtain a feasible solution set with better convergence. In order to prevent the algorithm from falling into local optimality at a later stage, we introduce the archive to enhance the diversity of the populations, and finally the main populations are explored in depth to obtain the set of solutions located in the PF. To test the superiority of the algorithm, we tested it against five state-of-the-art algorithms on 28 benchmark test problems and 3 real-world problems, proving that the altered algorithm has good competitiveness in dealing with constrained multi-objective optimization problems.

关键词： Dual-population multi-objective evolutionary algorithm Archives Dynamic synergy strategy

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A phenotype-based multi-objective evolutionary algorithm for maximizing lifetime in wireless sensor networks with bounded hop

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SOFT COMPUTING 2023年第15-16期28卷 8681页

作者： Ngoc, Bui Hong Tam, Nguyen Thi Binh, Huynh Thi Thanh Vinh, Le Trong Hanoi Univ Sci & Technol Hanoi Vietnam Vietnam Natl Univ Univ Sci Hanoi Vietnam

Relay node placement with a hop count bound is a crucial problem in enhancing connectivity, lifetime, and reliability in multi-hop wireless sensor networks. However, existing approaches focus solely on minimizing the number of used relay nodes without considering the energy consumption among nodes. This work investigates a relay node placement problem in multi-hop wireless sensor networks with two objectives: minimize the number of used relay nodes, and minimize the maximum node energy consumption to prolong the network's lifetime while still ensuring the network's connectivity. In particular, we consider a hop count bound as a delay constraint to elevate the network's reliability. We propose a multi-objective evolutionary algorithm called GPrim to solve our problem. The algorithm is a combination of edge-set encoding and NSGA-II framework. Leveraging problem-specific properties, we introduce objective-oriented heuristics incorporated into initialization, crossover, and mutation operators to improve the algorithm's convergence. Simulation results on 3D datasets show that the proposed algorithm performs significantly better than existing algorithms on all measured metrics.

关键词： multi-hop wireless sensor networks Network lifetime multi-objective evolutionary algorithm

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An improved multi-objective evolutionary algorithm for multiple-target asynchronous parallel selective disassembly sequence planning

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第10期237卷 1553-1569页

作者： Sun, Xiang Guo, Shunsheng Guo, Jun Du, Baigang Tang, Hongtao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China Hubei Digital Mfg Key Lab Wuhan Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn 122 Luoshi Rd Wuhan 430070 Peoples R China

The product's service life has been shortened rapidly along with the development of modern technologies and human esthetic evolutions, resulting in a great number of end-of-life (EOL) products. Disassembling renewable parts from EOL products for remanufacturing is of great significance to save resources and protect the environment. The existing studies on selective disassembly sequence planning (SDSP) largely focus on the sequential disassembly planning, which is inefficient especially for complex products because it is a linear process where only one component can be removed at a time. Thus, this work focuses on parallel SDSP and further proposes a multiple-target asynchronous parallel selective DSP (APSDSP) with the objectives of minimizing disassembly time and maximizing disassembly profit simultaneously. In APSDSP, operators can remove multiple components simultaneously as long as disassembly constraints are not violated, and without synchronization requirement between operators. The feasible disassembly sequence and disassembly direction sequence are generated by a space interference matrix method (SIMM) to meet the actual disassembly environment. Based on SIMM, an improved multi-objective evolutionary algorithm based on multiple neighborhood search strategy is developed to create the pareto frontier of the problem. Finally, two cases study are presented to validate the effectiveness of the proposed methodology. It gives enterprises new insights to reduce disassembly time and improve disassembly profit.

关键词： Selective disassembly sequencing planning asynchronous parallel disassembly multi-objective evolutionary algorithm pareto frontier remanufacturing

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A Novel multi-objective evolutionary algorithm Hybrid Simulated Annealing Concept for Recommendation Systems 1

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23rd International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Du, Yu Bao, Haijia Li, Ya Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (数字)9789819708598

ISBN: (纸本)9789819708581;9789819708598

Nowadays, recommendation systems have been widely used in information systems and internet applications. In order to solve the problem that most traditional recommendation algorithms mainly focus on accuracy and neglect other requirements such as diversity and novelty, multi-objective evolutionary algorithm has been introduced into recommendation system. But there is still much room for improvement in their performance. In this paper, we design a novel multi-objective evolutionary algorithm for recommendation system with accuracy, diversity, and novelty as its objective functions. In the proposed model, an efficient uniform distribution initialization and a mutation operator that integrates the concept in simulated annealing algorithm has been designed to improve the probability of producing more high-quality offspring. And an adaptive hybrid selection strategy is designed to select more valuable or promising individuals from offspring. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of accuracy, diversity, novelty, and hypervolume compared to some state-of-the-art recommendation algorithms.

关键词： multi-objective evolutionary algorithm Recommendation system Genetic operator

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