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Pareto based differential evolution with homeostasis based mutation

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第5期32卷 3245-3257页

作者： Singh, Shailendra Pratap Kumar, Anoj Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Allahabad Uttar Pradesh India

Differential evolution algorithm is a very popular nature inspired algorithm and it is used for solving single objective optimization problems. Due to its high convergence rate and ability to produce diverse solutions, it has been extended to solve multiobjective optimization problems. Many versions of DE were proposed however multiobjective versions, where concept of Pareto optimality is used, are most popular. Pareto Based Differential Evolution ( PBDE) is one of them. Although performance of this algorithm is very good, yet its convergence rate can be further improved by minimizing the time complexity of nondominated sorting and by improving the diversity among solutions. This has been implemented by using efficient nondominated algorithm whose time complexity is better than the previous one and a new mutation scheme is implemented in DE which can provide more diversity among solutions. The proposed variant adds one more vector named as Homeostasis mutation vector in the existing mutation vectors to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escaping the situation of stagnation. Performance of proposed algorithm is evaluated on twelve benchmark test functions (bi-objective and tri-objective) on Pareto-optimal front. Performance of proposed algorithm is compared with other state-of-the-art algorithms on five multiobjective evolutionary algorithms (MOEAs).The result verifies that our proposed Homeostasis mutation strategy performs better than other state-of-the-art variants.

关键词： Adaptation optimization multi-objective evolutionary algorithm

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Incorporating domain knowledge into the optimization of energy systems

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APPLIED SOFT COMPUTING 2016年第0期47卷 483-493页

作者： Mahbub, Md Shahriar Wagner, Markus Crema, Luigi Fdn Bruno Kessler Via Sommarive 18 I-38123 Povo Trento Italy Univ Trento Via Sommarive 9 I-38123 Povo Trento Italy Univ Adelaide Adelaide SA 5005 Australia

Energy plays a key factor in the advancement of humanity. As energy demands are mostly met by fossil fuels, the world-wide consciousness grows about their negative impact on the environment. Therefore, it becomes necessary to design sustainable energy systems by introducing renewable energies. Because of the intermittent availability of different renewable resources, the designing of a sustainable energy system should find an optimal mix of different resources. However, the optimization of this combination has to deal with a number of possibly contradictory objectives. multi-objective evolutionary algorithms (MOEA) are widely used to solve this kind of problems. As optimizing an energy system by using a MOEA is computationally costly, it is necessary to solve the problem efficiently. For this purpose, we propose the incorporation of domain knowledge related to energy systems into different phases (i.e., initialization and mutation) of a MOEA run. The proposed approaches are implemented for two widely used MOEAs and evaluated on the Danish Aalborg test problem. The experimental results show that each approach individually achieves significant improvements of the energy systems, which is expressed in better trade-off sets. Moreover, a state-of-the-art stopping criterion is adapted to detect the convergence in order to save computational resources. Finally, all proposed techniques are merged within two MOEAs with the result that our combined approaches yield significantly better results in less time than generic approaches. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Domain knowledge Energy system optimization Initialization multi-objective evolutionary algorithm

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Optimal Charge/Discharge Scheduling for Batteries in Energy Router-Based Microgrids of Prosumers via Peer-to-Peer Trading

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2022年第3期13卷 1315-1328页

作者： Gu, Bo Mao, Chengxiong Liu, Bin Wang, Dan Fan, Hua Zhu, Jinli Sang, Zixia Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China State Grid Hubei Elect Power Co Econ & Technol Res Inst Jingzhou 434000 Peoples R China

The concept of a microgrid of prosumers is demanded for the efficient utilization of renewable energy and the flexible energy trading between prosumers. This paper proposes a novel approach to optimize the charging/discharging schedule of battery energy storage systems in the microgrids of prosumers based on the energy router-based energy sharing structure. The proposed model aims to improve each prosumer's revenue with the arbitrage scheme in the peer-to-peer energy trading market. A novel internal pricing mechanism is presented according to the sharing energy in the prosumer community. A multi-objective evolutionary algorithm is implemented to solve the optimal solutions, and the fuzzy-decision-making method is used to select the schedule from the Pareto frontiers. The simulation results show that each prosumer's profit has been increased under different scenarios compared with the optimal profit of independent operation in the utility grid, with the reduction of the energy interchanged with the grid and the increase of the energy storage system life time. The experimental platform validates the feasibility of the energy router-based energy sharing structure for microgrids of prosumers.

关键词： Microgrids Batteries Pricing Real-time systems Energy storage Peer-to-peer computing Costs Energy router pricing energy storage system multi-objective evolutionary algorithm peer-to-peer prosumer

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A hybrid heuristic approach to discrete multi-objective optimization of credit portfolios

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2004年第2期47卷 373-399页

作者： Schlottmann, F Seese, D Univ Karlsruhe TH Dept Econ Inst AIFB D-76128 Karlsruhe Germany

A hybrid heuristic approach combining multi-objective evolutionary and problem-specific local search methods is proposed to support the risk-return analysis of credit portfolios. Its goal is to compute approximations of discrete sets of Pareto-efficient portfolio structures concerning both the respective portfolio return and the respective portfolio risk using the non-linear, non-convex Credit-Value-at-Risk downside risk measure which is relevant to real world credit portfolio optimization. In addition, constraints like capital budget restrictions are considered in the hybrid heuristic framework. The computational complexity of selected parts of the algorithm is analyzed. Moreover, empirical results indicate that the hybrid method is superior in convergence speed to a non-hybrid evolutionary approach and finds approximations of risk-return efficient portfolios within reasonable time. (C) 2003 Elsevier B.V. All rights reserved.

关键词： hybrid heuristic approach multi-objective evolutionary algorithm local search portfolio credit risk credit-value-at-risk constrained Pareto-efficient portfolio

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A DEA-based MOEA/D algorithm for portfolio optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 14477-14486页

作者： Zhou, Zhongbao Liu, Xianghui Xiao, Helu Wu, Shijian Liu, Yueyue Hunan Univ Sch Business Adm Changsha 410082 Hunan Peoples R China Hunan Normal Univ Sch Business Changsha 410081 Hunan Peoples R China Shandong Univ Sci & Technol Coll Econ & Management Qingdao 266590 Shandong Peoples R China Quzhou Univ Coll Entrepreneurship & Innovat Quzhou 324000 Peoples R China

In this paper, we present a multi-objective genetic algorithm DEA-MOEA/D by integrating decomposition method and DEA (Data Envelopment Analysis) approach. The initial solutions are generated by the DEA approach. Difference operators are adopted as the crossover operator of the parent. We adopt the test functions and portfolio optimization problems to compare the performance of DEA-MOEA/D, FDH-MOGA, MOEA/D and NSGA II. The results show that DEA-MOEA/D performs better than other three algorithms, not only for test functions, but for the portfolio optimization.

关键词： Portfolio optimization Data envelopment analysis multi-objective evolutionary algorithm Cardinality constraints

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Dynamic multi-objective Optimization of Grid-Connected Distributed Resources Along With Battery Energy Storage Management via Improved Bidirectional Coevolutionary algorithm

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IEEE ACCESS 2024年 12卷 58972-58992页

作者： Ali, Asif Liu, Zhizhen Ali, Aamir Abbas, Ghulam Touti, Ezzeddine Nureldeen, Waleed Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67450 Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Northern Border Univ Coll Engn Dept Elect Engn Ar Ar 73222 Saudi Arabia Univ Business & Technol Coll Engn Dept Gen Subjects Jeddah 23435 Saudi Arabia

This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems' sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm's superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems

关键词： Distribution network distributed generation battery energy storage system multi-objective evolutionary algorithm

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multi-objective Optimization for Bandwidth-Limited Federated Learning in Wireless Edge Systems

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2023年 4卷 954-966页

作者： Zhou, Yu Liu, Xuemei Lei, Lei Cent South Univ Sch Automat Changsha 410083 Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This paper studies a bandwidth-limited federated learning (FL) system where the access point is a central server for aggregation and the energy-constrained user equipments (UEs) with limited computation capabilities (e.g., Internet of Things devices) perform local training. Limited by the bandwidth in wireless edge systems, only a part of UEs can participate in each FL training round. Selecting different UEs could affect the FL performance, and selected UEs need to allocate their computing resource effectively. In wireless edge FL systems, simultaneously accelerating FL training and reducing computing-communication energy consumption are of importance. To this end, we formulate a multi-objective optimization problem (MOP). In MOP, the model training convergence is difficult to calculate accurately. Meanwhile, MOP is a combinatorial optimization problem, with the high-dimension mix-integer variables, which is proved to be NP-hard. To address these challenges, a multi-objective evolutionary algorithm for the bandwidth-limited FL system (MOEA-FL) is proposed to obtain a Pareto optimal solution set. In MOEA-FL, an age-of-update-loss method is first proposed to transform the original global loss function into a convergence reference function. Then, MOEA-FL divides MOP into $N$ single objective subproblems by the Tchebycheff approach and optimizes the subproblems simultaneously by evolving a population. Extensive experiments have been carried out on MNIST dataset and a medical case called TissueMNIST dataset for both the i.i.d and non-i.i.d data setting. Experimental results demonstrate that MOEA-FL performs better than other algorithms and verify the robustness and scalability of MOEA-FL.

关键词： Training Convergence Energy consumption Optimization Wireless communication Computational modeling Bandwidth Federated learning multi-objective evolutionary algorithm combinatorial optimization differential evolution decomposition

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Protein multiple Conformation Prediction Using multi-objective Evolution algorithm

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2024年第3期16卷 519-531页

作者： Hou, Minghua Jin, Sirong Cui, Xinyue Peng, Chunxiang Zhao, Kailong Song, Le Zhang, Guijun Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China BioMap & MBZUAI Beijing 100038 Peoples R China

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named multiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. multiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, multiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, multiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of multiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://***/multiSFold.

关键词： Protein structure prediction multiple conformational states multi-objective evolutionary algorithm

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Active broad learning with multi-objective evolution for data stream classification

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 899-916页

作者： Cheng, Jian Zheng, Zhiji Guo, Yinan Pu, Jiayang Yang, Shengxiang China Coal Res Inst Res Inst Mine Big Data Beijing 100013 Peoples R China Tiandi Sci & Technol Co Ltd Beijing 100013 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted.

关键词： Concept drift Data stream mining Broad learning system Active learning multi-objective evolutionary algorithm

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A niched Pareto tabu search for multi-objective optimal design of groundwater remediation systems

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JOURNAL OF HYDROLOGY 2013年 490卷 56-73页

作者： Yang, Yun Wu, Jianfeng Sun, Xiaomin Wu, Jichun Zheng, Chunmiao Nanjing Univ Sch Earth Sci & Engn Dept Hydrosci Key Lab Surficial GeochemMinist Educ Nanjing 210093 Jiangsu Peoples R China Geol Survey Jiangsu Prov Key Lab Earth Fissures Geol Disaster Minist Land & Resources Nanjing 210018 Peoples R China Univ Alabama Dept Geol Sci Tuscaloosa AL 35487 USA Peking Univ PKU Ctr Water Res Beijing 100871 Peoples R China

This study presents a new multi-objective optimization method, the niched Pareto tabu search (NPTS), for optimal design of groundwater remediation systems. The proposed NPTS is then coupled with the commonly used flow and transport code, MODFLOW and MT3DMS, to search for the near Pareto-optimal tradeoffs of groundwater remediation strategies. The difference between the proposed NPTS and the existing multiple objective tabu search (MOTS) lies in the use of the niche selection strategy and fitness archiving to maintain the diversity of the optimal solutions along the Pareto front and avoid repetitive calculations of the objective functions associated with the flow and transport model. Sensitivity analysis of the NPTS parameters is evaluated through a synthetic pump-and-treat remediation application involving two conflicting objectives, minimizations of both remediation cost and contaminant mass remaining in the aquifer. Moreover, the proposed NPTS is applied to a large-scale pump-and-treat groundwater remediation system of the field site at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts, involving minimizations of both total pumping rates and contaminant mass remaining in the aquifer. Additional comparison of the results based on the NPTS with those obtained from other two methods, namely the single objective tabu search (SOTS) and the nondominated sorting genetic algorithm II (NSGA-II), further indicates that the proposed NPTS has desirable computation efficiency, stability, and robustness and is a promising tool for optimizing the multi-objective design of groundwater remediation systems. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Contaminant transport Groundwater remediation Pareto-optimal design multi-objective evolutionary algorithm Niched Pareto tabu search Massachusetts Military Reservation (MMR)

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