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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Zhang, Kenneth Rodriguez-Fernandez, Angel E. Shang, Ke Ishibuchi, Hisao Schutze, Oliver Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China CINVESTAV Dept Comp Sci Mexico City DF Mexico Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

ISBN: (纸本)9783031700842;9783031700859

multi-objective evolutionary algorithms (MOEAs) are powerful optimizers that are capable of solving black-box multi-objective optimization problems. Due to their stochastic nature, local search methods, including directed search algorithms, have been proposed to guide search directions in the decision variable space. In particular, recent studies have shown that the inclusion of local hypervolume-based gradient methods can lead to better convergence rates. In this paper, a set-based method of estimating hypervolume gradients without additional function evaluations or Jacobian information is proposed and integrated with SMS-EMOA to form a steady-state MOEA. The proposed algorithm is compared to some widely-used MOEAs on two- and three-objective benchmark suites, outperforming all other algorithms on all 6/6 twoobjective problems and 12/17 three-objective problems.

关键词： multi-objective optimization multi-objective evolutionary algorithm Hypervolume indicator Hypervolume gradients

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multi-objective robust mathematical modeling of emergency relief in disaster under uncertainty

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SCIENTIA IRANICA 2022年第5期29卷 2670-2695页

作者： Eshghi, A. A. Tavakkoli-Moghaddam, R. Ebrahimnejad, S. Ghezavati, V. R. Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Islamic Azad Univ Dept Ind Engn Karaj Branch Karaj Iran

This paper presents a robust location-allocation planning problem for emergency relief in a disaster situation. It is a multi-objective, multi-commodity, multi-vehicle, and multi-level logistics model that considers injury variety through service prioritizing for injuries. Furthermore, it incorporates unmet demand for particular item types in various damaged areas. Public donation of different relief goods through capacitated medical centers and emergency centers is also addressed with regard to damage type, capacitated relief distribution centers, and disaster management centers. The model is a non-linear mixed-integer programming that simultaneously optimizes three objectives, namely maximizing service fairness to damaged areas, maximizing fair commodity disaster management, and minimizing the total logistics cost. To solve such a hard problem, a Non-dominated Sorting Genetic algorithm-II (NSGA-II) was developed and the Taguchi method was employed to adjust its parameters. The "-constraint method was used for the evaluation of the performance of the proposed algorithm. For more accurate validation, three comparison metrics including diversification, spacing, and mean ideal distance were adopted. The results verified the effectiveness of the algorithm in a reasonable computational time. Eventually, to examine the applicability of the presented model and the proposed algorithm, a case study was analyzed in an area located in the north of Iran, known for historical earthquake records and aggregated active faults. (c) 2022 Sharif University of Technology. All rights reserved.

关键词： Location-allocation planning Robust optimization Emergency relief Disaster multi-objective evolutionary algorithm

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Rule-based credit risk assessment model using multi-objective evolutionary algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 126卷 144-157页

作者： Soui, Makram Gasmi, Ines Smiti, Salima Ghedira, Khaled Higher Inst Management Gabes Tunisia Natl Sch Comp Sci Manouba Tunisia Honoris United Univ Interdisciplinary Res Ctr Tunis Tunisia

Credit risk assessment is considered as one of the vital topics in financial institutions. The existing credit risk evaluation methods are based on black box models or transparent models. The black box models cannot adequately reveal information hidden in the data and the credit risk evaluation remains difficult. In addition, there exist relatively few transparent models that take into consideration interpretability and comprehensibility. To address this problem, we aim to build a reliable credit risk evaluation model which generates a set of classification rules. In fact, we consider the credit risk evaluation as a search-based optimization problem where the goal is to minimize the complexity of the generated solution, to maximize the accuracy, and also to maximize weight which represents rules importance. We conducted a comparative study of four multi-objective evolutionary algorithms in terms of their performance. The obtained results confirm the efficiency of the SMOPSO algorithm regarding generating classification rules for credit risk assessment. The proposed credit risk evaluation model revealed an attractive trade-off between accuracy and comprehensibility. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Credit risk multi-objective evolutionary algorithm Classification rules

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Bi-objective green in-house transportation scheduling and fleet size determination in mixed-model assembly lines with mobile robots

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ENGINEERING COMPUTATIONS 2022年第7期39卷 2630-2654页

作者： Zhou, Binghai Zhang, Jihua Fei, Qianran Tongji Univ Inst Ind Engn Sch Mech Engn Shanghai Peoples R China

Purpose Facing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well. Design/methodology/approach This research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a multi-objective Discrete Differential Evolution algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem. Findings The performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem. Originality/value This study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

关键词： Mixed-model assembly line Energy efficiency Optimization multi-objective evolutionary algorithm In-house transportation

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multi-objective LSTM ensemble model for household short-term load forecasting

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MEMETIC COMPUTING 2022年第1期14卷 115-132页

作者： Fan, Chaodong Li, Yunfan Yi, Lingzhi Xiao, Leyi Qu, Xilong Ai, Zhaoyang Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Xiangtan Univ Foshan Green Intelligent Mfg Res Inst Foshan 528000 Peoples R China Hunan Univ Finance & Econ Sch Informat Technol & Management Changsha 410205 Peoples R China Hunan Univ Coll Foreign Languages Interdisciplinary Res Ctr Language Intelligence & Changsha 410082 Peoples R China Quanzhou Normal Univ Fujian Prov Key Lab Data Intens Comp Quanzhou 362000 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China

With the development of smart grid, household load forecasting played an important role in power system operations. However, the household load forecasting is often inefficient due to its high volatility and uncertainty. Consequently, a multi-objective LSTM ensemble model based on the DBN combination strategy, is proposed in this paper. This method first builds a deep learning framework based on the LSTM network in order to generate several sub-models. With the diversity and accuracy of the sub-models as the objective functions, the improved MOEA/D algorithm is then used to optimize the parameters, in order to enhance the overall performance of the sub-models and ensure their differences. Finally, a DBN-based combination strategy is used to combine the single forecasts in order to form the ensemble forecast, and reduce the adverse effects of model uncertainty and data noise on the prediction accuracy. The experimental results show that the proposed method has several advantages in prediction accuracy and generalization capacity, compared with several current intelligent prediction methods.

关键词： Short-term load forecasting LSTM network Ensemble learning multi-objective evolutionary algorithm Household load forecasting

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Minable Data Publication Based on Sensitive Association Rule Hiding

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第5期6卷 1247-1257页

作者： Yang, Fan Lei, Xinyu Le, Junqing Mu, Nankun Liao, Xiaofeng Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Minable data publication can promote data sharing among commercial companies and further facilitate the development of data-driven services. However, these commercial companies are often reluctant to publish their data due to security concerns. The published data may contain some sensitive information that is minable by malicious entities, leading to data privacy leakage. Therefore, it is highly demanded to develop the technologies supporting minable data publication with privacy protection. In this paper, we propose a privacy-preserved minable data publication scheme (PMDP ). PMDP enables selective sensitive association rules hiding while supporting the association rule mining. In PMDP, how to balance the trade-off between data privacy and data utility is the major problem, which can be formulated as a multi-objective optimization problem. To tackle this multi-objective optimization problem, we develop a customized multi-objective evolutionary algorithm (MOEA). In the customized MOEA, the local optimum trapping issue and slow convergence speed issue are hard to be addressed. First, to avoid being trapped into the local optimum, we carefully design a novel mutation method to guarantee the diversity of solutions. Second, to accelerate the convergence speed, we present a preprocessing method before the evolution process of the MOEA. In addition, we introduce the elite learning strategy into the MOEA, so the convergence speed can be further accelerated. At last, experiments are conducted over several datasets to demonstrate the effectiveness of PMDP.

关键词： Convergence Data privacy Optimization Computational intelligence Statistics Sociology Privacy Minable data publication association rule hiding multi-objective evolutionary algorithm

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multi-objective Software Effort Estimation: A Replication Study

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第8期48卷 3185-3205页

作者： Tawosi, Vali Sarro, Federica Petrozziello, Alessio Harman, Mark UCL Dept Comp Sci London WC1E 6BT England Facebook London WC1E 6BT England

Replication studies increase our confidence in previous results when the findings are similar each time, and help mature our knowledge by addressing both internal and external validity aspects. However, these studies are still rare in certain software engineering fields. In this paper, we replicate and extend a previous study, which denotes the current state-of-the-art for multi-objective software effort estimation, namely CoGEE. We investigate the original research questions with an independent implementation and the inclusion of a more robust baseline (LP4EE), carried out by the first author, who was not involved in the original study. Through this replication, we strengthen both the internal and external validity of the original study. We also answer two new research questions investigating the effectiveness of CoGEE by using four additional evolutionary algorithms (i.e., IBEA, MOCell, NSGA-III, SPEA2) and a well-known Java framework for evolutionary computation, namely JMetal (rather than the previously used R software), which allows us to strengthen the external validity of the original study. The results of our replication confirm that: (1) CoGEE outperforms both baseline and state-of-the-art benchmarks statistically significantly (p < 0.001);(2) CoGEE's multi-objective nature makes it able to reach such a good performance;(3) CoGEE's estimation errors lie within claimed industrial human-expert-based thresholds. Moreover, our new results show that the effectiveness of CoGEE is generally not limited to nor dependent on the choice of the multi-objective algorithm. Using CoGEE with either NSGA-II, NSGA-III, or MOCell produces human competitive results in less than a minute. The Java version of CoGEE has decreased the running time by over 99.8 percent with respect to its R counterpart. We have made publicly available the Java code of CoGEE to ease its adoption, as well as, the data used in this study in order to allow for future replication and extension of our wo

关键词： Software effort estimation multi-objective evolutionary algorithm confidence interval estimates uncertainty

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Micro multi-strategy multi-objective artificial bee colony algorithm for microgrid energy optimization

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 131卷 59-74页

作者： Peng, Hu Wang, Cong Han, Yupeng Xiao, Wenhui Zhou, Xinyu Wu, Zhijian Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

multi-objective evolutionary algorithm (MOEA) has become a common and effective method to solve real-world multi-objective optimization problems. However, in some practical problems, such as the microgrid energy optimization problem (MEOP), the algorithm needs to run on the micro controller to control each distributed power supply in real time. Due limitation of hardware resources on the micro controller, the MOEAs are not suitable. The emerging micro population MOEAs are suitable for this scenario. But the micro population MOEA is vulnerable to lost diversity, resulting in its performance decline. Therefore, this paper proposes a new micro multi-strategy multi-objective ABC algorithm to solve MEOP, called mu MMABC. multi-strategy ABC optimizer is used to divide the population into multiple subgroups and produce offspring in parallel to balance the exploration and exploitation. In addition, an adaptive updating mechanism is proposed to renew the population adaptively. The mechanism can adaptively select more convergent and diverse solutions at different stages to balance the exploration and exploitation of the algorithm. Furthermore, in order to improve the performance of mu MMABC on problems with irregular Pareto fronts, the reference point reconstruction with intermediate strategy is also proposed. Some benchmark test suites are used to test the performance of mu MMABC. Finally, it is used to solve the MEOP. The experimental results show that the proposed algorithm is more competitive and effective than the traditional MOEAs and other micro population MOEAs in solving the MEOP. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Micro population multi-objective evolutionary algorithm Artificial bee colony Microgrid energy optimization Adaptive updating mechanism

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evolutionary algorithm based Ensemble Extractive Summarization for Developing Smart Medical System

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2021年第2期13卷 229-259页

作者： Mallick, Chirantana Das, Asit Kumar Nayak, Janmenjoy Pelusi, Danilo Vimal, S. Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur 711103 Howrah India Aditya Inst Technol & Management AITAM Dept Comp Sci & Engn Tekkali 532201 Andhra Pradesh India Univ Teramo Dept Commun Sci Teramo Italy Natl Engn Coll Dept Informat Technol Kovilpatti 628503 Tamil Nadu India

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed meth

关键词： Bio-Medical informatics Ensemble summary Supervised extractive summarization multi-objective evolutionary algorithm Clustering coefficient Sparsity index

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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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