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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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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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A fast multi-objective evolutionary algorithm based on a tree structure

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APPLIED SOFT COMPUTING 2010年第2期10卷 468-480页

作者： Shi, Chuan Yan, Zhenyu Shi, Zhongzhi Zhang, Lei Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Univ Virginia Dept Syst & Informat Engn Charlottesville VA 22903 USA Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China

This paper proposes a fast evolutionary algorithm based on a tree structure for multi-objective optimization. The tree structure, named dominating tree (DT), is able to preserve the necessary Pareto dominance relations among individuals effectively, contains the density information implicitly, and reduces the number of comparisons among individuals significantly. The evolutionary algorithm based on dominating tree (DTEA) integrates the convergence strategy and diversity strategy into the DT and employs a DT-based eliminating strategy that realizes elitism and preserves population diversity without extra time and space costs. Numerical experiments show that DTEA is much faster than SPEA2, NSGA-II and an improved version of NSGA-II, while its solution quality is competitive with those of SPEA2 and NSGA-II. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Pareto dominance Fitness assignment Eliminating strategy

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A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification

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INFORMATION PROCESSING & MANAGEMENT 2017年第4期53卷 814-833页

作者： Onan, Aytug Korukoglu, Serdar Bulut, Hasan Celel Bayar Univ Dept Comp Engn TR-45140 Muradiye Manisa Turkey Ege Univ Dept Comp Engn TR-35100 Izmir Turkey

Sentiment analysis is a critical task of extracting subjective information from online text documents. Ensemble learning can be employed to obtain more robust classification schemes. However, most approaches in the field incorporated feature engineering to build efficient sentiment classifiers. The purpose of our research is to establish an effective sentiment classification scheme by pursuing the paradigm of ensemble pruning. Ensemble pruning is a crucial method to build classifier ensembles with high predictive accuracy and efficiency. Previous studies employed exponential search, randomized search, sequential search, ranking based pruning and clustering based pruning. However, there are tradeoffs in selecting the ensemble pruning methods. In this regard, hybrid ensemble pruning schemes can be more promising. In this study, we propose a hybrid ensemble pruning scheme based on clustering and randomized search for text sentiment classification. Furthermore, a consensus clustering scheme is presented to deal with the instability of clustering results. The classifiers of the ensemble are initially clustered into groups according to their predictive characteristics. Then, two classifiers from each cluster are selected as candidate classifiers based on their pairwise diversity. The search space of candidate classifiers is explored by the elitist Pareto-based multi-objective evolutionary algorithm. For the evaluation task, the proposed scheme is tested on twelve balanced and unbalanced benchmark text classification tasks. In addition, the proposed approach is experimentally compared with three ensemble methods (AdaBoost, Bagging and Random Subspace) and three ensemble pruning algorithms (ensemble selection from libraries of models, Bagging ensemble selection and LibD3C algorithm). Results demonstrate that the consensus clustering and the elitist pareto-based multi-objective evolutionary algorithm can be effectively used in ensemble pruning. The experimental analysis with

关键词： Ensemble pruning Consensus clustering multi-objective evolutionary algorithm Sentiment classification

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Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts

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INFORMATION SCIENCES 2014年第0期262卷 62-77页

作者： Chen, Yu Zou, Xiufen Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China

Previous theoretical analyses of evolutionary multi-objective optimization (EMO) mostly focus on obtaining epsilon-approximations of Pareto fronts. However, in practical applications, an appropriate value of epsilon is critical but sometimes, for a multi-objective optimization problem (MOP) with unknown attributes, difficult to determine. In this paper, we propose a new definition for the finite representation of the Pareto front the adaptive Pareto front, which can automatically accommodate the Pareto front. Accordingly, it is more practical to take the adaptive Pareto front, or its epsilon-approximation (termed the epsilon-adaptive Pareto front) as the goal of an EMO algorithm. We then perform a runtime analysis of a (mu + 1) multiobjective evolutionary algorithm ((mu + 1) MOEA) for three MOPs, including a discrete MOP with a polynomial Pareto front (denoted as a polynomial DMOP), a discrete MOP with an exponential Pareto front (denoted as an exponential DMOP) and a simple continuous two-objective optimization problem (SCTOP). By employing an estimator-based update strategy in the (mu + 1) MOEA, we show that (1) for the polynomial DMOP, the whole Pareto front can be obtained in the expected polynomial runtime by setting the population size mu equal to the number of Pareto vectors;(2) for the exponential DMOP, the expected polynomial runtime can be obtained by keeping mu increasing in the same order as that of the problem size n;and (3) the diversity mechanism guarantees that in the expected polynomial runtime the MOEA can obtain an epsilon-adaptive Pareto front of SCTOP for any given precision epsilon. Theoretical studies and numerical comparisons with NSGA-II demonstrate the efficiency of the proposed MOEA and should be viewed as an important step toward understanding the mechanisms of MOEAs. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Runtime analysis multi-objective evolutionary algorithm Finite approximations of Pareto fronts

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Knee based multimodal multi-objective evolutionary algorithm for decision making

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INFORMATION SCIENCES 2021年 544卷 39-55页

作者： Zhang, Kai Shen, Chaonan He, Juanjuan Yen, Gary G. Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Recently, many advanced multimodal multi-objective evolutionary algorithms (MMOEAs) have been presented to solve multimodal multi-objective optimization problems (MMOPs). However, most of these approaches struggle not only to find the entire Pareto sets and Pareto front, but also ignoring the problem of multi-Criteria Decision Making (MCDM) to be handled at a later stage. As a matter of fact, it is a very eclectic and challenging burden for decision makers to choose some trade-off optimal solutions from an enormous collection of non-dominated Pareto-optimal individuals. In this paper, we propose a knee based evolutionary algorithm, named MMO-EvoKnee, which incorporates MCDM strategy into solving MMOPs. The proposed algorithm is designed to search for a complete set of global knee solutions instead of an entire Pareto front and Pareto sets. Firstly, the EvoKnee Selection is adopted to find target global knee solutions and boundary individuals. Secondly, the Knee multimodal Solutions Selection focuses on decision space exploitation to obtain well-converged multiple Pareto-optimal knee solutions. Thirdly, the Maximum Spanning Subset Selection is designed to accurately identify a complete number of knee solutions from overcrowded Pareto regions. Fourthly, the Boundary multimodal Solutions Selection can find and maintain well-diversified and well-converged boundary solutions. As a result, MMO-EvoKnee can consistently find all knee solutions of interest quickly, and it relieves the burden for decision makers. Finally, the performance of the proposed algorithm is evaluated on thirteen benchmark MMOPs. The experiment results clearly show that the proposed MMO-EvoKnee provides a competitive edge over the chosen state-of-the-art MMOEAs. (C) 2020 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithm multimodal multi-objective optimization multi-criteria decision making multi-objective optimization multimodal optimization

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Decomposition-based 2-echelon multi-objective evolutionary algorithm with energy-efficient local search strategies for shop floor multi-crane scheduling problems

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 10719-10739页

作者： Zhou, Binghai Liao, Xiumei Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

Since the environment-friendly production has attracted extensive attention of many manufacturing enterprises, the energy consumption has become one of the core indices to evaluate production processes. Under this circumstance, the main purpose of this paper is to propose an efficient scheduling method for multi-objective shop floor multi-crane scheduling problems. In this research, the objectives of minimizing total weighted tardiness and total energy consumption are considered simultaneously. Owing to the NP-hard nature of the investigated problem, an improved decomposition-based 2-echelon multi-objective evolutionary algorithm with energy-efficient local search strategies (2-echelon iMOEA/D) is developed to solve the problem. The upper echelon optimizer of the proposed algorithm extends the advantages of computing resources allocation and adaptive neighborhood adjustment to accelerate the convergence speed, while the lower echelon optimizer utilizes energy-efficient local search strategies to realize deep optimization of the algorithm. The performance of the proposed method is compared with two other high-efficient multi-objective optimization algorithms. The computation results indicate that the proposed 2-echelon iMOEA/D achieves better performance both on solutions' quality and diversity.

关键词： multi-objective evolutionary algorithm Energy-efficient local search strategy Shop floor multi-crane scheduling Deep optimization

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Secondary population implementation in multi-objective evolutionary algorithm for scheduling of FMS

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第9-12期57卷 1143-1154页

作者： Pandian, P. Paul Sankar, S. Saravana Ponnambalam, S. G. Bathrinath, S. Monash Univ Petaling Jaya 46150 Selangor Malaysia Kalasalingam Univ Anand Nagar 626190 Krishnankoil India Sethu Inst Technol Pullor 626115 Kariapatti India

Any practical implementation of any multi-objective evolutionary algorithm (MOEA) must include a secondary population composed of all Pareto-optimal solutions found during its search process. Such an implementation with an active participation of solutions from the secondary population into the generational population of the genetic cycle is expected to improve the effectiveness of the MOEA. In this work, two kinds of secondary population, one with set of non-dominated solutions and another with a set of inferior solutions, accrued out of the generation cycles are constructed, and with different combinations of feeding of solutions from these two secondary populations, seven different implementation schemes are designed with an aim of intensifying the convergence and diversification capabilities of the genetic process of MOEA. All the schemes were implemented in a genetic algorithm-based MOEA designed to solve the scheduling problem with dual objectives for a flexible manufacturing system and tested with common experimental data. The performances of the schemes are compared, and the most appropriate implementation scheme is proposed.

关键词： multi-objective evolutionary algorithm Genetic algorithm Secondary population Scheduling optimization Flexible manufacturing system

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Spatial evolution character of multi-objective evolutionary algorithm based on self-organized criticality theory

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2012年第22期391卷 5490-5499页

作者： Li, Jun-fang Zhang, Bu-han Liu, Yi-fang Wang, Kui Wu, Xiao-shan Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

This paper analyzes the spatial evolution character of multi-objective evolutionary algorithms using self-organized criticality theory. The spatial evolution character is modeled by the statistical property of crowding distance, which displays a scale-free feature and a power-law distribution. We propose that the evolutional rule of multiobjective optimization algorithms is a self-organized state transition from an initial scalefree state to a final scale-free state. The target is to get close to a critical state representing the true Pareto-optimal front. Besides, the anti-Matthew effect is the internal incentive factor of most strategies. The final scale-free state reflects the quality of the final Pareto-optimal front. The speed of the state transition reflects the efficiency of the algorithm. We simulate the spatial evolution characters of three typical multi-objective evolutionary algorithms representing three fields, i.e., Genetic algorithm, Differential Evolution and the Artificial Immune System algorithm. The results prove that the model and the explanation are effective for analyzing the evolutional rule of multi-objective evolutionary algorithms. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： Spatial evolution character Self-organized criticality Scale-free characteristic Power-law distribution multi-objective evolutionary algorithm Matthew effect

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On Performance of a Simple multi-objective evolutionary algorithm on the Constrained Minimum Spanning Tree Problem

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-11页

作者： Lai, Xinsheng Shaoxing Univ Dept Comp Sci & Engn Beihai St Shaoxing 312000 Zhejiang Peoples R China

The constrained optimization problems can be transformed into multi-objective optimization problems, and thus can be optimized by multi-objective evolutionary algorithms. This method has been successfully used to solve the constrained optimization problems. However, little theoretical work has been done on the performance of multi-objective evolutionary algorithms for the constrained optimization problems. In this paper, we theoretically analyze the performance of a multi-objective evolutionary algorithms on the constrained minimum spanning tree problem. First, we theoretically prove that the multi-objective evolutionary algorithm is capable of finding a (2,1)-approximation solution for the constrained minimum spanning tree problem in a pseudopolynomial runtime. Then, this simple multi-objective evolutionary algorithm is shown to be efficient on a constructed instance of the problem.

关键词： Approximation algorithm multi-objective evolutionary algorithm Constrained optimization Spanning tree problem Combinatorial problem

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