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20th International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Wei, Guoshuai Wu, Quanwang Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Recommender systems have nowadays been widely used in a variety of applications such as Amazon and Ebay. Traditional recommendation techniques mainly focus on recommendation accuracy only. In reality, other metrics such as diversity and novelty also play a key role for modern recommendation systems. Although some works based on multi-objective evolutionary algorithm have been proposed for multi-objective recommendation, they are usually very time-consuming because of the large data size of the RSs and the long-term evolution iterations and hence it greatly limits their application in practice. To address these shortcomings, this paper first designs a multi-objective recommendation system, taking into account diversity and novelty as well as accuracy. Then, a novel parallel multi-objective evolutionary algorithm called CC-MOEA is proposed to optimize these conflicting metrics. CC-MOEA is devised grounded on NSGA-II and a cooperative coevolutionary island model, and a parallel global non-dominated selection method is introduced to reduce the runtime of finding the global optimal individuals. Furthermore, a new initialization method and a crossover operator are specifically designed. The experimental results reveal that CC-MOEA outperforms some state-of-the-art algorithms in terms of hypervolume and runtime.

关键词： Accuracy Cooperative coevolutionary multi-objective evolutionary algorithm Parallel Recommendation system

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Optimal reservoir flood operation using a decomposition-based multi-objective evolutionary algorithm

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ENGINEERING OPTIMIZATION 2019年第1期51卷 42-62页

作者： Zhang, Xiao Luo, Jungang Sun, Xiaomei Xie, Jiancang Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian Shaanxi Peoples R China

Reservoir flood control operation (RFCO) is a challenging optimization problem with interdependent decision variables and multiple conflicting criteria. By considering safety both upstream and downstream of the dam, a multi-objective optimization model is built for RFCO. To solve this problem, a multi-objective optimizer, the multi-objective evolutionary algorithm based on decomposition-differential evolution (MOEA/D-DE), is developed by introducing a differential evolution-inspired recombination into the algorithmic framework of the decomposition-based multi-objective optimization algorithm, which has been proven to be effective for solving complex multi-objective optimization problems. Experimental results on four typical floods at the Ankang reservoir illustrated that the suggested algorithm outperforms or performs as well as the comparison algorithms. It can significantly reduce the flood peak and also guarantee the dam's safety.

关键词： Reservoir flood control operation multi-objective evolutionary algorithm decomposition differential evolution

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An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第6期57卷 1756-1771页

作者： Jiang, En-da Wang, Ling Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

With the increasing attention on environment issues, green scheduling in manufacturing industry has been a hot research topic. As a typical scheduling problem, permutation flow shop scheduling has gained deep research, but the practical case that considers both setup and transportation times still has rare research. This paper addresses the energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time to minimise both makespan as economic objective and energy consumption as green objective. The mathematical model of the problem is formulated. To solve such a bi-objective problem effectively, an improved multi-objective evolutionary algorithm based on decomposition is proposed. With decomposition strategy, the problem is decomposed into several sub-problems. In each generation, a dynamic strategy is designed to mate the solutions corresponding to the sub-problems. After analysing the properties of the problem, two heuristics to generate new solutions with smaller total setup times are proposed for designing local intensification to improve exploitation ability. Computational tests are carried out by using the instances both from a real-world manufacturing enterprise and generated randomly with larger sizes. The comparisons show that dynamic mating strategy and local intensification are effective in improving performances and the proposed algorithm is more effective than the existing algorithms.

关键词： energy-efficient scheduling sequence-dependent setup time multi-objective evolutionary algorithm decomposition dynamic mating strategy local intensification

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An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies

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SOFT COMPUTING 2021年第18期25卷 12227-12240页

作者： Xue, Xingsi Jiang, Chao Wang, Haolin Tsai, Pei-Wei Mao, Guojun Zhu, Hai Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Fujian Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instr Guilin 541004 Guangxi Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn John St Hawthorn Vic 3122 Australia Zhoukou Normal Univ Sch Network Engn Zhoukou 466001 Henan Peoples R China

To address the heterogeneity problem of sensor data, it is necessary to conduct the Sensor Ontology Matching (SOM) process to find the mappings among diverse sensor data with the same semantics connotation. Currently, many multi-objective evolutionary algorithms (MOEAs) have been used to match the ontologies, which aim at finding a set of solutions called Pareto Set (PS) in the Pareto Front (PF) to represent a set of trade-off proposals for different Decision Makers (DMs). Being inspired by the success of MOEA with Inverse Model (IM-MOEA) in solving complicated optimization problems, in this work, an Improved IM-MOEA (I-IM-MOEA)-based matching technique is further proposed to enhance the algorithm's matching efficiency as well as the alignment's quality. To overcome the drawback of IM-MOEA that has poor performance on irregular PF, an adjusted selection mechanism is employed to avert the massive reduction in non-domination solutions on irregular PF, a dynamic Reference Vectors (RVs) is used to decrease the computational resources and boost the efficiency of the algorithm, and a local search strategy is introduced to promote the results' quality. The experiment employs the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) and three sensor ontologies to assess the performance of I-IM-MOEA, and the experimental results show that I-IM-MOEA is both effective and efficient.

关键词： Sensor ontology matching multi-objective evolutionary algorithm Inverse modeling

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A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization

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MEMETIC COMPUTING 2019年第3期11卷 285-296页

作者： Qiu, Jianfeng Liu, Minghui Zhang, Lei Li, Wei Cheng, Fan Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230039 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China

The area under receiver operating characteristic curve (AUC) is one of the widely used metrics for measuring imbalanced data classification results. Designing multi-objective evolutionary algorithms for AUC maximization problem has attracted much attention of researchers recently. However, most of these methods either search the Pareto front directly, or perform tailored convex hull search for AUC maximization. None of them take the advantage of multi-level knee points found in the process of evolution for AUC maximization. To this end, this paper proposes a multi-level knee point based multi-objective evolutionary algorithm (named MKnEA-AUC) for AUC maximization on the basis of a recently developed knee point driven evolutionary algorithm for multi/many-objective optimization. In MKnEA-AUC, an adaptive clustering strategy is proposed for automatically determining the knee points on the current population. By utilizing the preference of found knee points, the evolution of the population can converge quickly. We verify the effectiveness of the proposed algorithm MKnEA-AUC on 13 widely used benchmark data sets and the experimental results demonstrate that MKnEA-AUC is superior over the state-of-the-art algorithms for AUC maximization.

关键词： AUC maximization multi-objective evolutionary algorithm Knee points Adaptive clustering strategy

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A Hybrid multi-objective evolutionary algorithm With Heuristic Adjustment Strategies and Variable Neighbor-Hood Search for Flexible Job-Shop Scheduling Problem Considering Flexible Rest Time

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IEEE ACCESS 2019年 7卷 157003-157018页

作者： Sun, Xiang Guo, Shunsheng Guo, Jun Du, Baigang Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China

With the development of society, people have a higher demand for the work environment. It has aroused extensive attention of enterprises. One of the most important demands for workers is to have enough rest time to regain strength and energy during the working day. In this paper, a comprehensive mathematical model is established for the multi-objective flexible job-shop scheduling problem with flexible rest time (MOFJSP-FRT). Then a hybrid multi-objective evolutionary algorithm (MOEA) with heuristic adjustment strategies and variable neighborhood search (VNS), named HMOEAV, is proposed to solve the MOFJSP-FRT with the objectives to minimize the makespan and the machines loads balancing simultaneously. In the proposed hybrid algorithm, the machine-based encoding scheme is designed to improve search effectiveness by reducing computational complexity. Two heuristic adjustment strategies considering both the problem characteristics and the objective features are employed to initialize a high-quality population. To adequately emphasize the local exploitation ability of MOEA, VNS is incorporated into it. The non-dominated solutions got by MOEA are the initial solutions for VNS, in which three types of neighborhood structures according to problem structures are designed. The practical case in a steel structure enterprise is carried out to demonstrate the effectiveness of the proposed model and hybrid algorithm. The influences of rest time length on the MOFJSP-FRT are analyzed to give enterprises new insights to improve the scheduling efficiency while ensuring that employees have enough time to rest.

关键词： Flexible job shop scheduling flexible rest time multi-objective evolutionary algorithm heuristic adjustment strategies variable neighborhood search

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Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第5期30卷 2257-2272页

作者： Fu, Yaping Wang, Hongfeng Tian, Guangdong Li, Zhiwu Hu, Hesuan Qingdao Univ Inst Complex Sci Qingdao 266071 Shandong Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Jilin Univ Coll Transportat Changchun 130022 Jilin Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Shaanxi Peoples R China

multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.

关键词： Flow shop scheduling Deteriorating scheduling multi-objective multi-agent scheduling multi-objective evolutionary algorithm multipopulation

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A new history-guided multi-objective evolutionary algorithm based on decomposition for batching scheduling

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 141卷 112920-000页

作者： Jia, Zhao-hong Gao, Le-yang Zhang, Xing-yi Minist Educ Key Lab Intelligent Comp & Signal Proc Beijing Peoples R China Anhui Univ Hefei Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China

In this paper, a multi-objective scheduling problem on parallel batching machines is investigated with three objectives, the minimization of the makespan, the total weighted earliness/tardiness penalty and the total energy consumption, simultaneously. It is known that the batch scheduling problem is a type of NP-hard problems and the solutions to this problem have quite valuable structural features that are difficult to be formulated. One of the main issues is to make full use of the structural features of the existing solutions. Aiming at this issue, two effective strategies, local competition and internal replacement, are designed. Firstly, the local competition searches for the competitive neighboring solutions to accelerate convergence, through adjusting job positions based on two structural indicators. Secondly, the internal replacement uniformly retains half of the population as elites by elitist preservation based on decomposition. Thereafter, the other half of the population is replaced by the new solutions generated under the guidance of historical information. Moreover, the historical information is updated with the structural features extracted from the elites. As a result, a history-guided evolutionary algorithm based on decomposition with the above two strategies is proposed. To verify the performance of the proposed algorithm, extensive experiments are conducted on 18 groups of instances, in comparison with four state-of-the-art multi-objective optimization algorithms. Experimental results demonstrate that the proposed algorithm shows considerable competitiveness in addressing the studied multi-objective scheduling problems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithm Constrained scheduling problem Local competition Historical information Elitist preservation

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Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning

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SWARM AND evolutionary COMPUTATION 2019年 44卷 480-495页

作者： Ramirez Atencia, Cristian Del Ser, Javier Camacho, David Univ Autonoma Madrid Dept Ingn Informat C Francisco Tomas & Valiente 11 E-28049 Madrid Spain TECNALIA OPTIMA Area E-48160 Zamudio Spain Univ Basque Country UPV EHU Dept Commun Engn E-48013 Bilbao Spain BCAM E-48009 Bilbao Spain

Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naive MOEA approach.

关键词： Unmanned air vehicle Mission planning Constraint satisfaction problem multi-objective evolutionary algorithm Weighted strategies

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An Efficient Design for a multi-objective evolutionary algorithm to Generate DNA Libraries Suitable for Computation

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2019年第3期11卷 542-558页

作者： Chaves-Gonzalez, Jose M. Martinez-Gil, Jorge Univ Extremadura Dept Comp Sci Escuela Politecn Caceres 10003 Spain Software Competence Ctr Hagenberg Softwarepk 21 A-4232 Hagenberg Im Muhlkreis Austria

The design of reliable DNA libraries that can be used for bio-molecular computing involves several heterogeneous conflicting design criteria that traditional optimization approaches do not fit properly. As it is well known, evolutionary algorithms are very appropriate for solving complex NP-hard optimization problems. However, these approaches take significant computational resources when large instances of complex problems are managed. This is the case for the design of DNA libraries suitable for computation, which involves a set of conflicting design criteria that have to be simultaneously optimized. The problem tackled in this paper involves four objectives and two constraints which are managed at the same time by a tested multi-objective evolutionary algorithm (MOEA) with thousands of individuals in the population. In this context, every computational approach would take several hours of execution time to generate high-quality DNA strands. In this paper, we present an analysis of the parallel MOEA which has been efficiently parallelized with the aim of generating reliable sets of DNA sequences. The results obtained in the study presented here show that the parallel approach is computationally very efficient and that the DNA libraries are highly reliable for computation.

关键词： DNA library multi-objective evolutionary algorithm Parallel metaheuristics DNA sequence design

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