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Decomposition-based multi-objective optimization approach for PPI network alignment

KNOWLEDGE-BASED SYSTEMS 2022年 243卷 108527-108527页

作者： Menor-Flores, Manuel Vega-Rodriguez, Miguel A. Univ Extremadura Escuela Politecn Campus Univ S-N Caceres 10003 Spain

The alignment of protein-protein interaction (PPI) networks is in the spotlight of the research in bioinformatics. The main goal is to find structural or functional complexes that are evolutionarily conserved between species. Recent works in the area struggle to produce alignments of both good topological and functional quality, since those two objectives conflict more than expected. To this end, we introduce a decomposition-based multi-objective algorithm with two new problem-aware mutation operators, being each of them focused on the improvement of one of both objectives. The experiments have been performed over 10 scenarios of PPI network alignment with real data from five different species. The results have been evaluated with five quality metrics. The performed experiments have confirmed that the solutions resulting from our proposal obtain statistically significant improvements and are of higher quality than the solutions from related works.(c) 2022 The Author(s). Published by Elsevier B.V.& nbsp

关键词： multi-objective optimization multi-objective evolutionary algorithm based on decomposition Protein-protein interaction Network alignment Gene ontology consistency Symmetric substructure score

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A stopping criterion for decomposition-based multi-objective evolutionary algorithms

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SOFT COMPUTING 2018年第1期22卷 253-272页

作者： Kadhar, K. Mohaideen Abdul Baskar, S. Dr Mahalingam Coll Engn & Technol ECE Dept Pollachi Tamil Nadu India Thiagarajar Coll Engn EEE Dept Madurai Tamil Nadu India

This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms (MOEA/Ds) to reduce the unnecessary usage of computational resource. In MOEA/D, a multi-objective problem is decomposed into a number of single-objective subproblems using a Tchebycheff decomposition approach. Then, optimal Pareto front (PF) is obtained by optimizing the Tchebycheff objective of all the subproblems. The proposed stopping criterion monitors the variations of Tchebycheff objective at every generation using maximum Tchebycheff objective error (MTOE) of all the subproblems and stops the algorithm, when there is no significant improvement in MTOE. test is used for statistically verifying the significant changes of MTOE for every generations. The proposed stopping criterion is implemented in a recently constrained MOEA/D variant, namely CMOEA/D-CDP, and a simulation study is conducted with the constrained test instances for choosing a suitable tolerance value for the MTOE stopping criterion. A comparison with the recent stopping methods demonstrates that the proposed MTOE stopping criterion is simple and has minimum computational complexity. Moreover, the MTOE stopping criterion is tested on real-world application, namely multi-objective loop shaping PID controller design. Simulation results revealed that the MTOE stopping criterion reduces the unnecessary usage of computational resource significantly when solving the constrained test instances and multi-objective loop shaping PID controller design problems.

关键词： multi-objective evolutionary algorithm Stopping criterion multi-objective optimization H-infinity loop shaping PID controller design MOEA/D Differential evolution

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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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A loss matrix-based alternating optimization method for sparse PU learning

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SWARM AND evolutionary COMPUTATION 2022年第0期75卷

作者： Qiu, Jianfeng Cai, Xiaoqiang Zhang, Lei Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230039 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230039 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China Anhui Univ Key Lab Intelligent Comp Signal Proc Minist Educ Hefei 230039 Peoples R China

Positive and unlabeled (PU) learning is an important research topic in machine learning area, whose aim is to learn a good classifier from PU data. Due to its wide applications, a variety of PU algorithms with promising performance have been proposed. However, few of them consider PU learning from high-dimensional PU data. To fill the gap, in this work, we focus on designing the sparse PU classifier. However, it is difficult to achieve it, since the labels of U samples are uncertain. To this end, a loss matrix-based alternating optimization method, named LMAO-PU is proposed, where a two-stage alternating optimization idea is suggested to solve the difficulty in sparse PU learning. Firstly, a loss matrix is designed to measure the classification performance of the PU classifier on U samples. Then, a two-stage alternating optimization under the guide of loss matrix is developed. Specifically, the first stage is to optimize the PU model, aiming to address the challenge of labels uncertainty of U samples, where two objectives, TPR (True Positive Rate) and a suggested LRU (Loss Rate on Unlabeled), are optimized simultaneously. The second stage is to perform the sparse model optimization, where Sparsity and ErrorRate are adopted as the objectives to obtain the sparse model. The two-stage optimization procedure mentioned above is carried alternately and the sparse PU classifier with high quality is finally achieved. Experiments on 10 high-dimensional datasets demonstrate the superiority of proposed method over six state-of-the-art baselines in terms of sparsity, accuracy and area under ROC curve.

关键词： Sparse PU learning Alternating optimization Loss matrix multi-objective evolutionary algorithm

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Optimal Placement of Data Concentrators and Repeaters in PLC-enabled Smart Grids

Optimal Placement of Data Concentrators and Repeaters in PLC...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Stiri, Souhaima Chaoub, Abdelaali Grilo, Antonio Bennani, Rachid Lakssir, Brahim Tamtaoui, Ahmed INPT MUSICS Team STRS Lab Rabat Morocco MAScIR D&MSD Lab Rabat Morocco Univ Lisbon IST Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781665435970

The focus of this paper is on Power Line Communication (PLC) technology which has a great potential to accommodate the huge amount of data produced by different applications and nodes in the context of Smart Grids (SGs). In particular, this paper dives deeper into the planning and placement of Data Concentrator (DC) in various territorial categories: urban, suburban and rural environments. Supplemental repeaters, usually omitted in previous stat-of-the-art, are inserted throughout the electrical grid to reach the entire network. A multi-criteria optimization approach has been followed and the popular NSGA-II algorithm has been exploited to find a good trade-off between the conflicting performance and profitability objectives. Results of extensive numerical simulation are provided confirming the effective need of repeaters especially in remote areas where Smart Meters (SMs) are sparsely distributed. Moreover and despite the destructive influence of fading and noise along with the involved investments, PLC is still able to provide a satisfactory balance between performance and cost.

关键词： Smart Metering PLC Data Concentrator and Repeater Placement multi-objective Optimization Problem multi-objective evolutionary algorithm

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Cricketer?s tournament-wise performance prediction and squad selection using machine learning and multi-objective optimization ?

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Tirtho, Devopriya Rahman, Shafin Mahbub, Shahriar Ahsanullah Univ Sci & Technol Dhaka Bangladesh

The selection of a proper squad for a competition is vital in any sport, and cricket is no different. The diversity of squad formation is vital because those in charge of squad formation must consider all characteristics of the players. The squad must be broad and diverse enough for any event that lasts a long time since the squad must be competent in every situation, regardless of whether the side is playing at home or against. We aim to make the procedure as computationally efficient as possible. Machine Learning algorithms appear to have gone a long way in anticipating a system based on the information gained from the provided data. The article attempts to forecast player performance for a future tournament while considering most important characteristics of a cricket player. The prediction appears more accurate than an approximation. Several machine learning algorithms are utilized and analyzed to select the best one to use in our proposed work. The second phase employs multi-objective optimization (MOO) techniques to perceive optimum squads from a list of players based on anticipated performance and some additional attributes. However, the problem appears to have certain conflicting aspects that may be handled by multi -objective evolutionary algorithms (MOEA). Finally, a comparison is made between well-known MOEAs, and the results suggest that NSGA-II performs better for the problem.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Machine learning algorithm Prediction of performance Squad formation multi-objective evolutionary algorithm

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multi-objective Optimization Design of Plate-type Fuel via Co-simulation Method

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ANNALS OF NUCLEAR ENERGY 2022年 169卷 108914-108914页

作者： Tan, Jiatong Li, Quan Zhao, Bo Ma, Chao Wu, Yingwei Li, Yuanming Zhang, Jing He, Yanan Ye, Tianzhou Zhang, Cheng Tian, Wenxi Su, Guanghui Qiu, Suizheng Xi An Jiao Tong Univ Sch Nucl Sci & Technol State Key Lab Multiphase Flow Power Engn Shaanxi Key Lab Adv Nucl Energy & Technol Xian 710049 Peoples R China Nucl Power Inst China 328 Huayang Changshun Ave Sect 1 Chengdu Peoples R China

Plate-type fuel element has been implemented and studied widely because of its good heat transfer capa-bility. For the actual design of fuel assembly, the design objectives usually to be in competition, and the constraints also need to be considered. Therefore, this is a challenging constrained multi-objective opti-mization problem. A co-simulation method has been developed in this study to optimize the design of fuel plate for the thermal-hydraulic performance. Moreover, due to the versatility of this co -simulation method, extra objectives and constraints could be added as needed. First, a two-dimensional thermal-fluid-solid multiphysics model of plate-type fuel assembly was established and solved by the finite element method. After that, the values of relevant physical variables were verified with the three-dimensional model. Finally, the numerical simulation of multiphysics model was incorpo-rated with the multi-objective evolutionary algorithm (NSGA-II, adaptive NSGA-III and CMOEA-MS) which could handle constraints to optimize the thickness parameters of fuel plate. Pareto optimal solu-tions were obtained separately after the co-simulation with three algorithms, and the qualitative and quantitative analyses of the Pareto fronts showed that CMOEA-MS and NSGA-II had better performance in this study. This co-simulation could provide comprehensive and reliable design information according to the design objectives and requirements. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Plate -type fuel multi-objective optimization multi-objective evolutionary algorithm multiphysics

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Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from multi-objective evolutionary algorithms

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 289-305页

作者： Carlos Fernandez, Juan Cruz-Ramirez, Manuel Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg3rd Floor E-14071 Cordoba Spain

This paper proposes a framework to obtain ensembles of classifiers from a multi-objective evolutionary algorithm (MOEA), improving the restrictions imposed by two non-cooperative performance measures for multiclass problems: (1) the Correct Classification Rate or Accuracy (CCR) and, (2) the Minimum Sensitivity (MS) of all classes, i.e., the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. The proposed framework is based on collecting Pareto fronts of Artificial Neural Networks models for multiclass problems by the Memetic Pareto evolutionary NSGA2 (MPENSGA2) algorithm, and it builds a new Pareto front (ensemble) from stored fronts. The ensemble built significantly improves the closeness to the optimum solutions and the diversity of the Pareto front. For verifying it, the performance of the new front obtained has been measured with the habitual use of weighting methodologies, such as Majority Voting, Simple Averaging and Winner Takes All. In addition to CCR and MS measures, three trade-off measures have been used to obtain the goodness of a Pareto front as a whole: Hyperarea, Laumanns's Hyperarea (LAUMANNS) and Zitzler's Spread (M3). The proposed framework can be adapted for any MOEA that aims to improve the compaction and diversity of its Pareto front, and whose fitness functions impose severe restrictions for multiclass problems.

关键词： Ensemble multi-objective evolutionary algorithm multiclass classification Artificial Neural Networks Minimum Sensitivity Pareto Performance measures

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multi-objective missile boat scheduling problem using an integrated approach of NSGA-II, MOEAD, and data envelopment analysis

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APPLIED SOFT COMPUTING 2022年 127卷

作者： Chiu, Chun-Chih Lai, Chyh-Ming Natl Chin Yi Univ Technol 57Sec 2Zhongshan Rd Taichung 41170 Taiwan Natl Def Univ Grad Sch Resources Management & Decis Sci Taipei 112 Taiwan

During peacetime, the missile boat squadron possesses rapidly deployable capacities for crisis response. The squadron conducts maritime reconnaissance and patrolling, safeguarding maritime safety and engaging in humanitarian assistance and disaster relief in the surrounding waters of Taiwan and the Asia Pacific region. To help the Navy efficiently schedule missile boats, this study models the problem as a multi-objective optimization and seeks to develop an integrated approach to optimizing the scheduling problem of a missile boat squadron. We applied a second-generation nondominated sorting genetic algorithm (NSGAII) and a multi-objective evolutionary algorithm based on decomposition (MOEAD) to search the approximated Pareto-optimal set for the problem efficiently. Then, a data envelopment analysis was used to identify super-efficiency solutions among these and rank them. An appropriate solution was then determined based on the relative efficiency instead of the decision -maker preferences. The results were substantiated using 20 datasets and reflected that the NSGAII can provide super-efficient solutions on the input-oriented model, and MOEAD demonstrated precedence in forming super-efficiency solutions in the output-oriented model. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Nondominated sorting genetic algorithm II (NSGAII) multi-objective evolutionary algorithm based on decomposition (MOEAD) Missile boat squadron scheduling problem (MBSSP)

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An intelligent design for Ni-based superalloy based on machine learning and multi-objective optimization

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MATERIALS & DESIGN 2022年 221卷

作者： Deng, Yuedan Zhang, Yu Gong, Xiufang Hu, Wang Wang, Yucheng Liu, Ying Lian, Lixian Sichuan Univ Coll Mat Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn 2006 Xiyuan Ave Chengdu 611731 Peoples R China State Key Lab Long Life High Temp Mat 666 Jinshajing West Rd Deyang 618000 Peoples R China Dongfang Elect Corp Dongfang Turbine Co Ltd 666 Jinshajing West Rd Deyang 618000 Peoples R China

The simultaneous consideration of multiple conflicting properties in alloy optimization design is necessary yet remains challenging. A comprehensive strategy of machine learning (ML) and multi-objective optimization based on thermodynamic simulation data was proposed to accelerate the composition design of Ni-based superalloys. The microscopic parameters were determined by Pearson correlation analysis and domain knowledge as the key affect factors of tensile strength and elongation. The multiobjectives evolutionary algorithm (MOEA) was adopted to search the well-built surrogate by ML metaheuristically for the Pareto front of three objectives and its responding Pareto optimal solution set of composition. Furthermore, nine high-performance superalloy samples selected from the obtained Pareto front were well verified by fabricating and testing in the laboratory. Specially, a new composition among the nine as-fabricated samples was the best one according to the pre-defined design preferences with the gamma' solvus temperature, gamma' volume fraction, and TCP phases content approximating to 1210 degrees C, 65%, and 0.01%, respectively. This intelligent cooperation strategy based on ML and MOEA extends the methodology for multi-composition and multi-property design materials, which can optimize multiple conflicting objectives simultaneously rather than do one by one. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Ni-based superalloys Microscopic parameters multi-objective evolutionary algorithm Machine learning Thermo-Calc

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