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multiobjective U-Shaped Disassembly Line Balancing Problem Considering Human Fatigue Index and an Efficient Solution

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第4期10卷 2061-2073页

作者： Guo, Xiwang Wei, Tingting Wang, Jiacun Liu, Shixin Qin, Shujin Qi, Liang Liaoning Petrochem Univ Coll Informat Sci & Control Engn Fushun 113001 Peoples R China Monmouth Univ Dept Comp Sci & Software Engn West Long Branch NJ 07764 USA Northeast Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shangqiu Normal Univ Dept Logist Management Shangqiu 476000 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

The progress of science and technology speeds up the replacement of products and produces a large number of end-of-life products. Traditional incineration causes a waste of resources and pollution to the environment. Disassembling and recycling end-of-life products are the recommended way to maximize the utilization of resources and reduce environmental pollution. Disassembly performance is affected by many factors, such as the disassembly posture of the human body, the fatigue of workers on a workstation, disassembly profit, and task precedence relationship. In this article, a mixed integer linear programming mathematical model for U-shaped layout disassembly line balancing problems is developed, in which the balance of workers' fatigue indices is an optimization objective in addition to disassembly profits. An efficient solution to the problem that uses a collaborative resource allocation strategy of the multiobjective evolutionary algorithm is proposed. The linear programming solver CPLEX is used to verify the accuracy of the model and compared with the proposed algorithm. Experiments demonstrate that the algorithm is significantly superior to the CPLEX solver in handling large-scale cases. The proposed algorithm is also compared with two well-known algorithms, which further verifies its superiority.

关键词： Task analysis Workstations Fatigue Layout Indexes Mathematical models Costs Collaborative resource allocation (CRA) disassembly fatigue index linear programming multiobjective evolutionary algorithm

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A multiobjective approach based on the behavior of fireflies to generate reliable DNA sequences for molecular computing

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APPLIED MATHEMATICS AND COMPUTATION 2014年 227卷 291-308页

作者： Chaves-Gonzalez, Jose M. Vega-Rodriguez, Miguel A. Univ Extremadura Dept Comp & Commun Technol Escuela Politecn Caceres 10003 Spain

The reliability of artificially generated DNA molecules is a key factor for applications which depend on DNA-based technologies, such as DNA computing or nanotechnology. In those cases, interactions between sequences have to be controlled to avoid undesirable reactions. In the specific case of molecular computing, the design of robust sets of sequences prevent from incorrect computations because DNA sequences are designed in order to avoid potentially conflicting interactions between the DNA molecules within the artificially generated library. However, the design of reliable DNA libraries which can be used for molecular computing involves several heterogeneous and conflicting design criteria that cannot be properly modeled by using traditional optimization algorithms. In this paper, we formulate the problem as a multiobjective optimization problem and we solve it with a novel multiobjective algorithm based on the behaviour of fireflies. Specifically, our approach, multiobjective firefly algorithm (MO-FA), works with six different conflicting design criteria that measure the reliability of the generated sequences. Furthermore, in order to compare our results in multiobjective terms, we have also developed and adjusted the well-known fast non-dominated sorting genetic algorithm (NSGA-II). Results show that our proposal obtains very satisfactory results. In fact, the reliability of DNA sequences generated significantly surpasses the reliability of sequences obtained with other approaches previously published in the literature. (C) 2013 Elsevier Inc. All rights reserved.

关键词： multiobjective optimization DNA sequence design multiobjective firefly algorithm multiobjective evolutionary algorithm Molecular computing

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COMPARISON AND SELECTION OF OBJECTIVE FUNCTIONS IN multiobjective COMMUNITY DETECTION

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COMPUTATIONAL INTELLIGENCE 2014年第3期30卷 562-582页

作者： Shi, Chuan Yu, Philip S. Yan, Zhenyu Huang, Yue Wang, Bai Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Univ Illinois Chicago IL USA King Abdulaziz Univ Jeddah 21413 Saudi Arabia Adobe Syst Inc San Jose CA USA

Detecting communities of complex networks has been an effective way to identify substructures that could correspond to important functions. Conventional approaches usually consider community detection as a single-objective optimization problem, which may confine the solution to a particular community structure property. Recently, a new community detection paradigm is emerging: multiobjective optimization for community detection, which means simultaneously optimizing multiple criteria and obtaining a set of community partitions. The new paradigm has shown its advantages. However, an important issue is still open: what type of objectives should be optimized to improve the performance of multiobjective community detection? To exploit this issue, we first proposed a general multiobjective community detection solution (called NSGA-Net) and then analyzed the structural characteristics of communities identified by a variety of objective functions that have been used or can potentially be used for community detection. After that, we exploited correlation relations (i.e., positively correlated, independent, or negatively correlated) between any two objective functions. Extensive experiments on both artificial and real networks demonstrate that NSGA-Net optimizing over a pair of negatively correlated objectives usually leads to better performances compared with the single-objective algorithm optimizing over either of the original objectives, or even to other well-established community detection approaches.

关键词： complex network community detection multiobjective optimization multiobjective evolutionary algorithm

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A General multiobjective Clustering Approach Based on Multiple Distance Measures

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IEEE ACCESS 2018年 6卷 41706-41719页

作者： Liu, Cong Liu, Jie Peng, Dunlu Wu, Chunxue Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

Many conventional clustering methods have limitations to partition data sets with different structures. The reason is that the relationship of each pair of data objects in different structures is usually based on different distance measures while conventional clustering methods are often designed for an assumption distribution in Euclidean space. Most of current clustering methods have also been proposed for integrating different distance measures together, however, the weights for different distance measures are difficult to set. To alleviate this case and to generate reliable clustering results for data sets with different structures, in this paper, a novel multiple distance measures clustering method based on a multiobjective evolutionary algorithm is proposed to this problem. This approach takes two types of distance measures as multiple objective functions and optimizes them simultaneously by using a modified multiobjective evolutionary algorithm with some new strategies including initialization, crossover operator, mutation operator, and objective functions designing. Moreover, an updated approach was also proposed for detecting the correct cluster number automatically. The new approaches are applied to many datasets with spherical and irregular structures, and the results of eight artificial, four widely used and four real data sets will be exhibited in experiments. The comparisons with other clustering algorithms show that, no matter what shape dataset has, both of the proposed approaches can get satisfactory results in combining different distance measures and detecting the optimal cluster number in a single run.

关键词： Clustering analysis multiobjective evolutionary algorithm evolutionary clustering multiple distance measures

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Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses

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INFORMATION SCIENCES 2013年 223卷 136-152页

作者： Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Fac Engn Khon Kaen 40002 Thailand

This paper proposes a hybrid evolutionary algorithm for multiobjective optimisation of trusses using real-code population-based incremental learning (RPBIL) to solve multiobjective design problems. Differential evolution (DE) operators are integrated into the main procedure of RPBIL leading to a hybrid algorithm. The newly developed optimiser. along with some established multiobjective evolutionary algorithms (MOEAs) is implemented to solve a number of multiobjective design problems of trusses. Comparative performance based upon a hypervolume indicator shows that the new hybrid multiobjective evolutionary algorithm is superior to the other MOEAs particularly in cases involving large-scale truss design problems. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Real-code population-based incremental learning Differential evolution Hybrid method multiobjective evolutionary algorithm multiobjective design of truss

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MOEA/D with Uniform Design for Solving multiobjective Knapsack Problems

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JOURNAL OF COMPUTERS 2013年第2期8卷 302-307页

作者： Tan, Yan-yan Jiao, Yong-chang Xidian Univ Natl Key Lab Antennas & Microwave Technol Xian Shaanxi Peoples R China Xidian Univ Intelligent informat Proc Xian Shaanxi Peoples R China

The 0/1 knapsack problem is a well-known problem, which appears in many real domains with practical importance. The problem is NP-complete. The multiobjective 0/1 knapsack problem is a generalization of the 0/1 knapsack problem in which multiple knapsacks are considered. Many algorithms have been proposed in the past five decades for both single and multiobjective knapsack problems. A new version of MOEA/D with uniform design for solving multiobjective 0/1 knapsack problems is proposed in this paper. The algorithm adopts the uniform design method to generate the aggregation coefficient vectors so that the decomposed scalar optimization subproblems are uniformly scattered, and therefore the algorithm could explore uniformly the region of interest from the initial iteration. To illustrate how the algorithm works, some numerical experiments on the benchmark multiobjective knapsack problems are realized. Experimental results show that the proposed algorithm outperforms NSGA-II, SPEA2 and PESA significantly for the 2-objective, 3-objective and 4-objective knapsack problems.

关键词： multiobjective evolutionary algorithm uniform design decomposition knapsack problem

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On Neighborhood Exploration and Subproblem Exploitation in Decomposition Based multiobjective evolutionary algorithms

On Neighborhood Exploration and Subproblem Exploitation in D...

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IEEE Congress on evolutionary Computation

作者： Aimin Zhou Yuting Zhang Guixu Zhang Wenyin Gong Shanghai Key Lab. of Multidimensional East China Normal Univ. Shanghai China

ISBN: (纸本)9781479974931

The decomposition based multiobjective evolutionary algorithm, denoted as MOEA/D, is an open framework for multiobjective optimization. This paper addresses the reproduction operation in MOEA/D. Generally, the solutions from a neighborhood of a subproblem are chosen as the mating pool for offspring reproduction. Since the Pareto set of an MOP shows some kind of structure in the decision space, the newly generated solutions based on the mating pool are arguable more likely to distribute along the population structure, which is called neighborhood exploration, and less likely to push a solution forward along the subproblem, which is called subproblem exploitation. To balance neighborhood exploration and subproblem exploitation, we propose to utilize both history and neighbor solutions for offspring reproduction. This idea is implemented through two operators based on the multivariate Gaussian distribution model, one is based on neighbor solutions and the other is based on previously visited solutions. When generating a new trial solution for a subproblem, one of the two operators is chosen with a probability. The proposed reproduction strategy is embedded in the MOEA/D framework and applied to a test suite. The comparison study has demonstrated that the new reproduction strategy is promising.

关键词： multiobjective evolutionary algorithm decomposition probabilistic model history information

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A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第9期26卷 2045-2057页

作者： Chaves-Gonzalez, Jose M. Vega-Rodriguez, Miguel A. Granado-Criado, Jose M. Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Caceres 10003 Spain

The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for biomolecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published. (C

关键词： multiobjective optimization Swarm intelligence Artificial bee colony DNA sequence design multiobjective evolutionary algorithm Molecular computing

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A new humanoid robot gait generation based on multiobjective optimization

A new humanoid robot gait generation based on multiobjective...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Capi, G Yokota, M Mitobe, K Fukuoka Inst Technol Fac Informat Engn Higashi Ku Fukuoka 8110195 Japan

ISBN: (纸本)0780390466

Up to now, the optimization algorithms are applied for humanoid robot gait generation, where a single fitness function drives the optimization process. But often, the humanoid robot gait generation problem is subject to several objectives. In order to deal with this problem, in this paper, we propose a new method based on multiobjective evolutionary algorithm. In order to verify the effectiveness of our proposed method, we considered two important conflicting objectives: minimum energy and minimum torque change, simultaneously. The angle trajectories are generated without neglecting the stability of humanoid robot. Results using the Bonten-Maru humanoid robot show a good performance of the proposed method.

关键词： humanoid robot multiobjective evolutionary algorithm gait synthesis

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An Efficient multiobjective evolutionary Approach for a Simultaneous Inventory Control and Facility Location Problem

An Efficient Multiobjective Evolutionary Approach for a Simu...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2009)

作者： Liao, Shu-Hsien Hsieh, Chia-Lin Chou, Wen-Min Tamkang Univ Dept Management Sci & Decis Making Taipei Taiwan Aletheia Univ Dept Ind Management & Enterpise Informat Taipei Taiwan

ISBN: (纸本)9781424448692

Supply chain network system provides an optimal platform for efficient and effective supply chain management (SCM). SCM usually involves multiple and conflicting objectives. A Multi-Objective Location Inventory Problem (MOLIP) model is initially formulated that includes elements of total cost, volume fill rate and responsiveness level as its objectives and also integrates the effects of facility location, distribution, and inventory issues. In this paper, we presented a hybrid evolutionary algorithm based on the Nondominated Sorting Genetic algorithm (NSGAII) for solving MOLIP. We analyzed a randomly generated set of problem instances of the MOLIP model to understand the model performance and compared this algorithm with one of the well-known multiobjective evolutionary algorithms called SPEA2 to understand the efficiency between two approaches. Computational and comparative results of our NSGAII-based algorithm have presented promise solutions for different sizes of problems and proved to be an innovative and efficient approach for so called difficult-to-solve problems.

关键词： integrated supply chain network design multiobjective evolutionary algorithm NSGAII

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