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multi-objective Five-Element Cycle optimization Algorithm Based on multi-Strategy Fusion for the Bi-objective Traveling Thief Problem

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7468页

作者： Xiang, Yue Guo, Jingjing Jiang, Chao Ma, Haibao Liu, Mandan East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Space Engn Univ Dept Aerosp Sci & Technol Beijing 101416 Peoples R China Beijing Univ Technol Beijing Artificial Intelligence Inst Fac Informat TechnolMinist EducKey Lab Computat Engn Res Ctr Digital CommunityBeijing Key Lab Com Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Intelligent Environm Protect Beijing 100124 Peoples R China Vanderlande Ind Logist Automated Syst Shanghai Co Technol Ctr Shanghai 200131 Peoples R China

In this paper, we propose a multi-objective Five-element Cycle optimization algorithm based on multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.

关键词： bi-objective traveling thief problem exploration and exploitation five-element cycle model multi-objective evolutionary optimization

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SMEM: A Subspace Merging Based evolutionary Method for High-Dimensional Feature Selection

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期9卷 1712-1727页

作者： Li, Kaixuan Jiang, Shibo Zhang, Rui Qiu, Jianfeng Zhang, Lei Yang, Lixia Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Inst Informat Mat & Intelligent Sensing Lab Anhui Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Univ Inst Informat Mat & Intelligent Sensing Lab Anhui Sch Elect & Informat Engn Hefei 230601 Peoples R China

In the past decade, evolutionary algorithms (EAs) have shown their promising performance in solving the problem of feature selection. Despite that, it is still quite challenging to design the EAs for high-dimensional feature selection (HDFS), since the increasing number of features causes the search space of EAs grows exponentially, which is known as the "curse of dimensionality". To tackle the issue, in this paper, a Subspace Merging based evolutionary Method, termed SMEM is suggested. In SMEM, to avoid directly optimizing the large search space of HDFS, the original feature space of HDFS is firstly divided into several independent low-dimensional subspaces. In each subspace, a subpopulation is evolved to obtain the latent good feature subsets quickly. Then, to avoid some features being missed, these low-dimensional subspaces merge in pairs, and the further search is carried on the merged subspaces. During the evolving of each merged subspace, the good feature subsets obtained from previous subspace pair are fully utilized. The above subspace merging procedure repeats, and the performance of SMEM is improved gradually, until in the end, all the subspaces are merged into one final space. At that time, the final space is also the original feature space in HDFS, which ensures all the features in the data is considered. Experimental results on different high-dimensional datasets demonstrate the effectiveness and the efficiency of the proposed SMEM, when compared with the state-of-the-arts.

关键词： Feature extraction Search problems Merging optimization Sorting History Fans High-dimensional feature selection multi-objective evolutionary optimization subspace division pairwise subspace merging

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Interpretable and accurate medical data classification - a multi-objective genetic-fuzzy optimization approach

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 71卷 26-39页

作者： Gorzalczany, Marian B. Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000 Lecia PP 7 PL-25314 Kielce Poland

In medical decision support systems, both the accuracy (i.e., the ability to adequately represent the decision making processes) as well as the transparency and interpretability (i.e., the ability to provide a domain user with compact and understandable explanation and justification of the proposed decisions) play essential roles. This paper presents an approach for automatic design of fuzzy rule-based classification systems (FRBCSs) from medical data using multi-objective evolutionary optimization algorithms (MOEOAs). Our approach generates, in a single run, a collection of solutions (medical FRBCSs) characterized by various levels of accuracy-interpretability trade-off. We propose a new complexity-related interpretability measure and we address the semantics-related interpretability issue by means of efficient implementation of the so-called strong fuzzy partitions of attribute domains. We also introduce a special coding-free representation of the rule base and original genetic operators for its processing as well as we implement our ideas in the context of well-known and one of the presently most advanced MOE0A5, i.e., Non-dominated Sorting Genetic Algorithm II (NSGA-II). An important part of the paper is devoted to a broad comparative analysis of our approach and as many as 26 alternative techniques arranged in 32 experimental set-ups and applied to three well-known benchmark medical data sets (Breast Cancer Wisconsin (Original), Pima Indians Diabetes, and Heart Disease (Cleveland)) available from the UCI repository of machine learning databases (http://***/ml). A number of useful in medical applications performance measures including accuracy, sensitivity, specificity, and several interpretability measures are employed. The results of such a broad comparative analysis demonstrate that our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained FRBCSs while remaining either competitive or superi

关键词： Accuracy and interpretability of medical classification systems Medical decision support multi-objective evolutionary optimization Fuzzy rule-based systems Genetic computations

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Statistical learning and optimization of the helical milling of the biocompatible titanium Ti-6Al-7Nb alloy

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期125卷 1789-1813页

作者： da Costa, Tomas Barbosa Dutra Pereira, Robson Bruno Lauro, Carlos Henrique Brandao, Lincoln Cardoso Davim, J. Paulo Univ Fed Sao Joao del Rei Ctr Innovat Sustainable Mfg CIMS Dept Mech & Ind Engn 170 Frei Orlando Sq BR-36880000 Sao Joao Del Rei MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Modelling & Optimizat Syst CIMOS Dept Mech & Ind Engn 170 Frei Orlando Sq BR-36880000 Sao Joao Del Rei MG Brazil Univ Aveiro Dept Mech Engn Aveiro Portugal

Helical milling has been applied for hole-making in titanium alloys, especially in the Ti-6Al-4V alloy, considering the aims of the aeronautic, automobile, and other sectors. When considering hole-making in Ti-alloys for biomedical applications, few studies have been carried out. Besides, intelligent approaches for modeling and optimization of this process in these special alloys are demanded to achieve the best results in terms of hole surface quality, and productivity. This work presents an approach for modeling and optimization of helical milling for hole-making of Ti-6Al-7Nb biocompatible titanium alloy. The surface roughness of the holes was measured to quantify the hole quality. Principal component analysis was performed for dimensionality reduction of the roughness outputs. For modeling, a learning procedure was proposed considering polynomial response surface regression, tree-based methods, and support vector regression. Cross-validation is used for learning and model selection. The results pointed out that the support vector regression model was the best one. multi-objective evolutionary optimization was performed considering the support vector regression model and the deterministic model of the material removal rate. The Pareto set and the Pareto frontier were plotted and discussed concerning practical aspects of the helical milling process. The proposed learning and optimization approach enabled the achievement of the best results of the helical milling in the biocompatible Ti-6Al-7Nb alloy and can be applied to other intelligent manufacturing applications.

关键词： Helical milling Biocompatible titanium alloy Statistical learning Machine learning Support vector regression multi-objective evolutionary optimization

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A fairness-aware multi-stakeholder recommender system

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2021年第6期24卷 1995-2018页

作者： Ranjbar Kermany, Naime Zhao, Weiliang Yang, Jian Wu, Jia Pizzato, Luiz Macquarie Univ Dept Comp Sydney NSW 2019 Australia Commonwealth Bank Australia AI Labs Sydney NSW 2015 Australia

Traditional recommender systems mainly focus on the accuracy of recommendation, which lead to recommender systems reinforcing popular items and ignoring lesser-known items. There is increasing evidence that providing good recommendations of surprising items can lead to better user satisfaction. Users may be delightfully surprised if long-tail items are brought to them. Marketplaces need to keep providers satisfied by making sure that their items get enough exposure. In this work, we propose a fairness-aware multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail inclusion, personalized diversity, and recommendation accuracy. Experimental results against real-world datasets show that the proposed method significantly improves the diversity of recommended items in a personalized matter and the coverage of providers with no or minor loss of accuracy.

关键词： multi-stakeholder recommender systems Long-tail recommendation multi-objective evolutionary optimization P-fairness Personalized diversity

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An innovative multi-objective optimization approach for long-term energy planning

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APPLIED ENERGY 2017年 208卷 1487-1504页

作者： Mahbub, Md Shahriar Viesi, Diego Cattani, Sara Crema, Luigi Fdn Bruno Kessler Via Sommarive 18 I-38123 Trento Italy Univ Trento Via Sommarive 9 I-38123 Trento Italy Univ Turin Via Verdi 8 I-10124 Turin Italy

Designing future energy scenarios is an important topic to energy planners. As designing future optimized scenarios is a multi-objective optimization problem;therefore, it is required to identify trade-off scenarios (Pareto-front) in order to optimize conflicting objectives. In this study, three Pareto-fronts are identified for designing future scenarios for Val di Non (VdN) for three different time horizons. As the community has to reach different emission targets in different time horizons, it is require to select the optimized scenarios that fulfill the targets. In this regards, we propose a new approach for selecting scenarios based on maximizing decision space diversity in order to provide a diverse set of scenarios to the decision makers. The technique is tested on optimiied scenarios of VdN and three sets containing 10 diverse scenarios for different time horizons are selected. Moreover, a smooth transition (in terms of decision variables) is desirable when having a transition from a scenario from one time horizon to a consecutive time horizon. A novel method is proposed to choose scenarios from the sets for a smooth transition based on minimizing distances among the scenarios. The approach is applied on VdN where transient scenarios are identified among different possible optimized scenarios.

关键词： Energy system optimization multi-objective evolutionary optimization Long-term energy planning Energy transition

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A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability

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APPLIED SOFT COMPUTING 2016年 40卷 206-220页

作者： Gorzalczany, Marian B. Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000 Lecia PP 7 PL-25314 Kielce Poland

Credit classification is an important component of critical financial decision making tasks such as credit scoring and bankruptcy prediction. Credit classification methods are usually evaluated in terms of their accuracy, interpretability, and computational efficiency. In this paper, we propose an approach for automatic designing of fuzzy rule-based classifiers (FRBCs) from financial data using multi-objective evolutionary optimization algorithms (MOEOAs). Our method generates, in a single experiment, an optimized collection of solutions (financial FRBCs) characterized by various levels of accuracy-interpretability trade-off. In our approach we address the complexity- and semantics-related interpretability issues, we introduce original genetic operators for the classifier's rule base processing, and we implement our ideas in the context of Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the presently most advanced MOEOAs. A significant part of the paper is devoted to an extensive comparative analysis of our approach and 24 alternative methods applied to three standard financial benchmark data sets, i.e., Statlog (Australian Credit Approval), Statlog (German Credit Approval), and Credit Approval (also referred to as Japanese Credit) sets available from the UCI repository of machine learning databases (http://***, ***/ml). Several performance measures including accuracy, sensitivity, specificity, and some number of interpretability measures are employed in order to evaluate the obtained systems. Our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained financial data classifiers while remaining either competitive or superior in terms of their accuracy and the speed of decision making. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Accuracy and interpretability of credit classification systems Financial decision support multi-objective evolutionary optimization Fuzzy rule-based systems Genetic computations

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multi-objective optimization in Modeling and Control for Rotary Inverted Pendulum

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2018年第4期15卷 363-373页

作者： Huilcapi, Victor Lima, Byron Blasco, Xavier Manuel Herrero, Juan Univ Politecn Salesiana Fac Ingn Robles 107 Guayaquil Ecuador Univ Politecn Valencia Inst Univ Automat & Informat Ind Camino Vera S-N E-46022 Valencia Spain

This article shows the application of multi-objective optimization techniques, both for the identification of parameters of a nonlinear model and for the adjustment of controllers. In particular, we propose a technique to identify the parameters of a first principles model of a rotational inverted pendulum (RIP) applying a methodology of multi-objective optimization and experimental data. Also the methodology extends to the tuning of PID and PI controllers for the mentioned system. For multiobjective optimization, an implementation based on evolutionary algorithms has been used, ev-MOGA (Herrero et al., 2007). For the analysis phase of the front solutions, we use the Pareto front visualization tool called Level Diagram (Blasco et al., 2017), which allows to successfully explore a set of Pareto optimal solutions and select one of them according to the preferences of the designer. The article does not try to study different control structures for the RIP system, but, given a control structure, analyze how to maximize the possibilities of the same to meet some conflicting objectives established by the designer. The advantage offered by this methodology is the easy understanding of the conflicts that appear among the design objectives, allowing to select a compromise solution according to the preferences of the designer, without losing sight of the set of optimal solutions found.

关键词： multi-objective evolutionary optimization non-linear identification Pareto front modeling PID control

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A Modern Data-Mining Approach Based on Genetically Optimized Fuzzy Systems for Interpretable and Accurate Smart-Grid Stability Prediction

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ENERGIES 2020年第10期13卷 2559-2559页

作者： Gorzalczany, Marian B. Piekoszewski, Jakub Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000 Lecia PP 7 PL-25314 Kielce Poland

The main objective and contribution of this paper was/is the application of our knowledge-based data-mining approach (a fuzzy rule-based classification system) characterized by a genetically optimized interpretability-accuracy trade-off (by means of multi-objective evolutionary optimization algorithms) for transparent and accurate prediction of decentral smart grid control (DSGC) stability. In particular, we aim at uncovering the hierarchy of influence of particular input attributes upon the DSGC stability. Moreover, we also analyze the effect of possible "overlapping" of some input attributes over the other ones from the DSGC-stability perspective. The recently published and available at the UCI Database Repository Electrical Grid Stability Simulated Data Set and its input-aggregate-based concise version were used in our experiments. A comparison with 39 alternative approaches was also performed, demonstrating the advantages of our approach in terms of: (i) interpretable and accurate fuzzy rule-based DSGC-stability prediction and (ii) uncovering the hierarchy of DSGC-system's attribute significance.

关键词： decentral smart grid control (DSGC) interpretable and accurate DSGC-stability prediction data mining computational intelligence fuzzy rule-based classifiers multi-objective evolutionary optimization

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Temporal Data Scheduling in Internet of Vehicles Using an Improved Decomposition-Based multi-objective evolutionary Algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第5期24卷 5282-5295页

作者： Li, Ya-Lun Chai, Zheng-Yi Tan, Fei Liu, Xu Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Sch Software Engn Tianjin 300387 Peoples R China

Nowadays, Internet of Vehicles plays an important role in the emerging intelligent transportation systems. In Internet of Vehicles, it is crucial to make appropriate temporal data scheduling to ensure the service quality and the high service ratio to meet the requests from vehicles. However, service quality and service ratio are two conflict goals because of the limited bandwidth and the vehicle mobility in Internet of Vehicles. In order to optimize these two conflict objectives simultaneously, we present an improved decomposition based multi-objective evolutionary algorithm for the temporal data scheduling (I-MOEA/D-TDS) in Internet of Vehicles. Based on the MOEA/D framework, we integrate a self-adaptive weight vector adjustment method based on chain segmentation to improve the performances of temporal data scheduling. For verifying the availability of presented algorithm, under the hybrid Vehicle-to-Infrastructure / Vehicle-to-Vehicle (V2I/V2V) communications and multiple Roadside Units (RSUs) scenario, we compare the proposed algorithm with several related algorithms under the effects of data valid periods, service workloads, maximum tolerated delay, and traffic workloads. Experimental results suggest that the presented algorithm can achieve better data service quality and service ratio.

关键词： Internet of Vehicles evolutionary computation Heuristic algorithms Data models Vehicle dynamics optimization Job shop scheduling Internet of Vehicles temporal data scheduling multi-objective evolutionary optimization weight vector adjustment chain segmentation

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