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Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

INFORMATION SCIENCES 2014年 258卷 160-181页

作者： Basgalupp, Marcio P. Barros, Rodrigo C. de Carvalho, Andre C. P. L. F. Freitas, Alex A. Univ Fed Sao Paulo BR-12231280 Sao Jose Dos Campos SP Brazil Univ Sao Paulo BR-13560970 Sao Carlos SP Brazil Univ Kent Canterbury CT2 7NF Kent England

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Decision tree Lexicographic optimization Machine learning multi-objective evolutionary algorithm

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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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Nondominated Maneuver Strategy Set With Tactical Requirements for a Fighter Against Missiles in a Dogfight

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IEEE ACCESS 2020年 8卷 117298-117312页

作者： Yang, Zhen Zhou, Deyun Kong, Weiren Piao, Haiyin Zhang, Kai Zhao, Yiyang Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

Dogfight is often a continuous and multi-round process with missile attacks. If the fighter only considers the security when evading the incoming missile, it will easily lose the superiority in subsequent air combat. Therefore, it is necessary to maintain as much tactical superiority as possible while ensuring a successful evasion. The amalgamative tactical requirements of achieving multiple evasive objectives in a dogfight are taken into account in this paper. A method of generating a nondominated maneuver strategy set for evading missiles with tactical requirements is proposed. The tactical requirements include higher miss distance, less energy consumption, and higher terminal superiority. Then the evasion problem is defined and reformulated into a multi-objective optimization problem, which is solved by a redesigned multi-objective evolutionary algorithm based on decomposition (MOEA/D). Simulations are used to demonstrate the feasibility and effectiveness of the approach. A set of approximate Pareto-optimal solutions satisfying the tactical requirements are obtained. These solutions can not only guide the fighter to avoid being hit but also achieve the goal of relatively reducing energy consumption and improving terminal superiority.

关键词： Dogfight decision-making evasive maneuvers multi-objective evolutionary algorithm tactical requirements

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Self-adaptive polynomial mutation in NSGA-II

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SOFT COMPUTING 2023年第23期27卷 17711-17727页

作者： Carles-Bou, Jose L. Galan, Severino F. Univ Nacl Educ Distancia UNED Escuela Int Doctorado EIDUNED Madrid Spain UNED Dept Artificial Intelligence Madrid Spain

evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed and implemented by the scientific community, the popular Non-Dominated Sorting Genetic algorithm (NSGA-II) remains as a widely used baseline for algorithm performance comparison purposes and applied to different engineering problems. Since every evolutionary algorithm needs several parameters to be set up in order to operate, parameter control constitutes a crucial task for obtaining an effective and efficient performance in its execution. However, despite the advancements in parameter control for evolutionary algorithms, NSGA-II has been mainly used in the literature with fine-tuned static parameters. This paper introduces a novel and computationally lightweight self-adaptation mechanism for controlling the distribution index parameter of the polynomial mutation operator usually employed by NSGA-II in particular and by multi-objective evolutionary algorithms in general. Additionally, the classical NSGA-II using polynomial mutation with a static distribution index is compared with this new version utilizing a self-adapted parameter. The experiments carried out over twenty-five benchmark problems show that the proposed modified NSGA-II with a self-adaptive mutator outperforms its static counterpart in more than 75% of the problems using three quality metrics (hypervolume, generalized spread, and modified inverted generational distance).

关键词： multi-objective evolutionary algorithm NSGA-II Polynomial mutation Distribution index self-adaptation

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Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2013年第1期141卷 112-126页

作者： Xiong, Jian Xing, Li-ning Chen, Ying-wu Natl Univ Def Technol Dept Management Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China Univ New S Wales Australian Def Force Acad Sch Engn & Informat Technol Canberra ACT 2600 Australia

This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives - makespan and robustness - are simultaneously considered. Robustness is indicated by the expected value of the relative difference between the deterministic and actual makespan. Utilizing the available information about machine breakdowns, two surrogate measures for robustness are developed. Specifically, the first suggested surrogate measure considers the probability of machine breakdowns, while the second surrogate measure considers the location of float times and machine breakdowns. To address this problem, a multi-objective evolutionary algorithm is presented in this paper. The experimental results indicate that, compared with several other existing surrogate measures, the first suggested surrogate measure performs better for small cases, while the second surrogate measure performs better for both small and relatively large cases. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Flexible job-shop schedule problem Machine breakdowns Robust scheduling Surrogate measure multi-objective evolutionary algorithm

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A clustering-based archive handling method and multi-objective optimization of the optimal power flow problem

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APPLIED INTELLIGENCE 2024年第22期54卷 11603-11648页

作者： Akbel, Mustafa Kahraman, Hamdi Tolga Duman, Serhat Temel, Seyithan Pusula Iletisim Bilisim Internet Sanayi Ve Ticaret Istanbul Turkiye Karadeniz Tech Univ Software Engn Technol Fac Trabzon Turkiye Bandirma Onyedi Eylul Univ Elect Engn Engn & Nat Sci Fac Bandirma Turkiye TEKNOPAR Ankara Turkiye

The main challenge in finding the optimal Pareto Front (PF) and Pareto Set (PS) sets for multimodal multi-objective optimization problems (MMOPs) with conflicting objective functions is to exhibit a balanced and sustainable diversity and exploitation capability in both the objective and decision spaces. This paper introduces dynamic reference spaces based clustering (DRSC) as a new archive handling method to overcome this challenge. DRSC incorporates a niche method called dynamically switched reference spaces and adapts the K-means-based method for clustering non-dominant vectors and handling the archive. The performance of the proposed DRSC is tested on twenty-four benchmark problems. According to the results of non-parametric statistical analysis using data from four different performance metrics, DRSC-MOAGDE designed using the proposed archiving mechanism managed to achieve the best Friedman rank among thirty different competitors. According to the stability analysis results, the average success rates and average computation times of the three best performing algorithms DRSC-MOAGDE, MMODE-ICD and SSMOPSO are (88.69%, 3.01 s), (66.87%, 9.47 s) and (64.29%, 1372.49 s), respectively. It is also observed that the proposed DRSC-MOAGDE outperforms the best cost optimization values in the literature with a minimum of 0.1838 $/h and a maximum of 30.9157 $/h for the multi-objective OPF real-world problem.

关键词： Archive handling Dynamic reference spaces based clustering multi-objective evolutionary algorithm multimodal multi- objective optimization Real-world constrained optimal power flow problem

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Uncertainty-Based multi-objective Decision Making with Hierarchical Reliability Analysis Under Water Resources and Environmental Constraints

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WATER RESOURCES MANAGEMENT 2016年第2期30卷 805-822页

作者： Dong, Feifei Liu, Yong Su, Han Liang, Zhongyao Zou, Rui Guo, Huaicheng Peking Univ Coll Environm Sci & Engn Key Lab Water & Sediment Sci MOE Beijing 100871 Peoples R China Tetra Tech Inc 10306 Eaton PlSte 340 Fairfax VA 22030 USA Yunnan Key Lab Pollut Proc & Management Plateau L Kunming 650034 Peoples R China

Rapid urbanization and population growth have resulted in worldwide serious water shortage and environmental deterioration. It is then essential for efficient and feasible allocation of scarce water and environment resources to the competing users. Due to inherent uncertainties, decision making for resources allocation is vulnerable to failure. The scheme feasibility can be evaluated by reliability, representing the failure probability. A progressive reliability oriented multi-objective (PROMO) optimal decision-making procedure is proposed in this study to deal with problems with numerous reliability objectives. Dimensionality of the objectives is reduced by a top-down hierarchical reliability analysis (HRA) process combining optimization with evaluation. Pareto solutions of the reformulated model, representing alternative schemes non-dominated with each other, are generated by a metalmodel-based optimization algorithm. Evaluation and identification of Pareto solutions are conducted by multi-criteria decision analysis (MCDA). The PROMO procedure is demonstrated for a case study on industrial structure transformation under strict constraints of water resources and total environmental emissions amounts in Guangzhou City, South China. The Pareto front reveals tradeoffs between economic returns of the industries and system reliability. For different reliability preference scenarios, the Pareto solutions are ranked and the top-rated one was recommended for implementation. The model results indicate that the PROMO procedure is effective for model solving and scheme selection of uncertainty-based multi-objective decision making.

关键词： Water resources allocation Stochastic programming Tradeoff analysis multi-criteria decision analysis multi-objective evolutionary algorithm Optimization

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Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world

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CLIMATIC CHANGE 2015年第3期132卷 373-386页

作者： Kwakkel, Jan H. Haasnoot, Marjolijn Walker, Warren E. Delft Univ Technol Fac Technol Policy & Management NL-2600 GA Delft Netherlands Deltares NL-2600 MH Delft Netherlands Delft Univ Technol Dept Policy Anal Fac Technol Policy & Management NL-2600 GA Delft Netherlands Delft Univ Technol Dept Air Transport & Operat Fac Aerosp Engn NL-2600 GA Delft Netherlands

Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper presents a model-driven approach supporting the development of promising adaptation pathways, and illustrates the approach using a hypothetical case. We use robust optimization over uncertainties related to climate change, land use, cause-effect relations, and policy efficacy, to identify the most promising pathways. For this purpose, we generate an ensemble of possible futures and evaluate candidate pathways over this ensemble using an Integrated Assessment Meta Model. We understand 'most promising' in terms of the robustness of the performance of the candidate pathways on multiple objectives, and use a multi-objective evolutionary algorithm to find the set of most promising pathways. This results in an adaptation map showing the set of most promising adaptation pathways and options for transferring from one pathway to another. Given the pathways and signposts, decision-makers can make an informed decision on a dynamic adaptive plan in a changing environment that is able to achieve their intended objectives despite the myriad of uncertainties.

关键词： Uncertainty multi-objective evolutionary algorithm dynamic Policies Water Management computer-assisted Self tuning Robustness Pathway meta-models

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Energy and spectral efficiency tradeoff in massive MIMO systems with multi-objective adaptive genetic algorithm

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SOFT COMPUTING 2019年第16期23卷 7163-7179页

作者： Hei, Yongqiang Zhang, Cong Song, Weiting Kou, Yanchun Xidian Univ State Key Lab ISN Xian Shaanxi Peoples R China

Spectral efficiency (SE) and energy efficiency (EE) are both important metrics in massive multiple-input multiple-output (MIMO) systems. However, maximizing EE and SE is always conflicting with each other, and they can hardly be achieved simultaneously. In this paper, we focus on the tradeoff optimization between EE and SE in multiuser massive MIMO systems in terms of the number of transmit antennas and the transmit power. Different from the previous EE-oriented or SE-oriented method, the EE-SE tradeoff problem is formulated into a multi-objective optimization problem. To efficiently attain the Pareto optimal front (POF) of EE-SE tradeoff, a multi-objective adaptive genetic algorithm, inspired by the non-dominated sorting genetic algorithm (NSGA-II), is proposed to improve the convergence speed. Experimental comparisons against several well-known multi-objective algorithms show that the proposed algorithm can quickly adapt to the true POF of EE-SE tradeoff and maintain good performance on benchmark functions in terms of the adopted performance metrics.

关键词： EE-SE tradeoff Massive MIMO multi-objective evolutionary algorithm

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Design and Investigation of a New Torque-Enhanced Flux Modulated Permanent Magnet Arc Motor With Flux-Concentrated Stator PMs

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷 1页

作者： Chen, Nan Fang, Shuhua Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

This article proposes an innovative flux modulated permanent magnet (PM) arc motor enhanced with flux-concentrated stator PMs (FCS-FMPMAM). With the primary goal of increasing the average torque output, the FCS-FMPMAM incorporates a set of flux-focused stator PMs. This article initially introduces the configuration and performance of the FCS-FMPMAM. The combination of the flux-switching PMs, consequent-pole PMs and PMs in the stator yoke is beneficial to generate abundant harmonics and bolster the triple flux-modulation effect. Then, the optimal rotor pole number of the proposed machine is analyzed and selected. The sensitivity analysis is performed on the design parameters of FCS-FMPMAM for the consequent optimization of the torque performance of FCS-FMPMAM. This facilitates the subsequent torque performance optimization of FSC-FMPMAM. Based on the multi-objective evolutionary algorithm, the torque capabilities of FCS-FMPMAM are optimized to obtain the maximized average torque output and the minimized torque ripple. The optimized machine is analyzed under different PM excitation conditions to reveal the superiority of the proposed PM arrangement. Finally, the proposed machine is compared with an existing flux-modulated stator-PM arc motor in electromagnetic performances with finite element analysis method to exhibit the superiority of FCS-FMPMAM.

关键词： Arc motor flux-concentrated stator PMs multi-objective evolutionary algorithm sensitivity analysis triple flux-modulation effect torque

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