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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Song, Hong Xi Zhang, Jian Xin Gao, Kun Meng, Wei Cheng, Li Inner Mongolia Univ Technol Sch Elect Power Hohhot 010080 Inner Mongolia Peoples R China

ISBN: (纸本)9783037856932

The charge plan is a basic problem of the metallurgical industry Through a comprehensive consideration of the process constraints and production costs, charge plan is a typical multi-objective optimization problem. A multi-objective optimization model of charge plan was built in this paper. As direct encoding is hard to be used in the many constraints problem case, a new method of generate feasible solutions based on constraint satisfaction was proposed. A novel adaptive grid algorithm was also proposed to improve the current multi-objective evolutionary algorithm's population maintain strategy. Finally, the proposed multi-objective charge plan model was solved, and more meaningful solutions for this problem were obtained.

关键词： Steelmaking and continuous casting Charge plan Adaptive grid Maintain strategy constraint satisfaction multi-objective evolutionary algorithm

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A multi-objective evolutionary algorithm for Tuning Type-2 Fuzzy Sets with Rule and Condition Selection on Fuzzy Rule-Based Classification System 10th

A Multi-objective Evolutionary Algorithm for Tuning Type-2 F...

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10th Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT) / 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets (IWIFSGN)

作者： Cardenas, Edward Hinojosa Camargo, Heloisa A. Univ San Agustin Arequipa Peru Univ Fed Sao Carlos Sao Carlos SP Brazil

ISBN: (纸本)9783319668307;9783319668291

This paper presents a multi-objective evolutionary algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on Fuzzy Rule-Based Classification Systems (FRBCS). Before the tuning and selection process, the Rule Base is learned by means of a modified Wang-Mendel algorithm that considers type-2 fuzzy sets in the rules antecedents and in the inference mechanism. The multi-objective evolutionary algorithm used in the tuning process has three objectives. The first objective reflects the accuracy where the correct classification rate of the FRBCS is optimized. The second objective reflects the interpretability of the system regarding complexity, by means of the quantity of rules and is to be minimized through selecting rules from the initial rule base. The third objective also reflects the interpretability as a matter of complexity and models the quantity of conditions in the Rule Base. Finally, we show how the FRBCS tuned by our proposed algorithm can achieve a considerably better classification accuracy and complexity, expressed by the quantity of fuzzy rules and conditions in the RB compared with the FRBCS before the tuning process.

关键词： Fuzzy Rule-Based Classification System Type-2 fuzzy sets multi-objective evolutionary algorithm

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Optimising a Waste Heat Recovery System using multi-objective evolutionary algorithm

Optimising a Waste Heat Recovery System using Multi-Objectiv...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Mokhtar, Maizura Hunt, Ian Burns, Stephen Ross, Dave Edinburgh Napier Univ Sch Engn & Built Environm Merchiston Campus Edinburgh EH10 5DT Midlothian Scotland Roslin Inst Bldg Scotlands Rural Coll Easter Bush EH25 9RG Midlothian Scotland

ISBN: (纸本)9781450343237

A waste heat recovery system (WHRS) on a process with variable output, is an example of an intermittent renewable process. WHRS recycles waste heat into usable energy. As an example, waste heat produced from refrigeration can be used to provide hot water. However, consistent with most intermittent renewable energy systems, the likelihood of waste heat availability at times of demand is low. For this reason, the WHRS may be coupled with a hot water reservoir (HWR) acting as the energy storage system that aims to maintain desired hot water temperature T-d (and therefore energy) at time of demand. The coupling of the WHRS and the HWR must be optimised to ensure higher efficiency given the intermittent mismatch of demand and heat availability. Efficiency of an WHRS can be defined as achieving multiple objectives, including to minimise the need for back-up energy to achieve T-d, and to minimise waste heat not captured (when the reservoir volume V-res is too small). This paper investigates the application of a multi objective evolutionary algorithm (MOEA) to optimise the parameters of the WHRS, including the V-res and depth of discharge (DoD), that affect the WHRS efficiency. Results show that one of the optimum solutions obtained requires the combination of high V-res, high DoD, low water feed in rate, low power external back-up heater and high excess temperature for the HWR to ensure efficiency of the WHRS.

关键词： multi-objective evolutionary algorithm waste heat recovery system process integration renewable energy system optimisation

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A multi-objective evolutionary algorithm-based soft computing model for educational data mining: A distance learning experience

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Asian Association of Open Universities Journal 2017年第1期12卷 106-123页

作者： Tan, Choo Jun Lim, Ting Yee Bong, Chin Wei Liew, Teik Kooi School of Science and Technology Wawasan Open University Penang Malaysia Department Registry Directorate of Quality Assurance and External Relations Wawasan Open University Penang Malaysia

Purpose: The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach: A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide. Findings: The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value: A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed. © 2017, Choo Jun Tan, Ting Yee Lim, Chin Wei Bong and Teik Kooi Liew.

关键词： Classification Educational data mining multi-objective evolutionary algorithm Optimization Student achievement

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An Opposition-based Repair Operator for multi-objective evolutionary algorithm in Constrained Optimization Problems 11

An Opposition-based Repair Operator for Multi-objective Evol...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Fan, Zhun Li, Wenji Cai, Xinye Huang, Han Xie, Shuxiang Goodman, Erik Shantou Univ Dept Elect Engn Shantou 515063 Guangdong Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

ISBN: (纸本)9781467376792

In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them. The proposed repair operator is used to fix the solutions that violate the box constraints. More specifically, it employs a reversed correction strategy that can effectively avoid the population falling into local optimum. In addition, we integrate the proposed repair operator into two classical multi-objective evolutionary algorithms MOEA/D and NSGA-II. The proposed repair operator is compared with other two kinds of commonly used repair operators on benchmark problems CTPs and MCOPs. The experiment results demonstrate that our proposed approach is very effective in terms of convergence and diversity.

关键词： multi-objective evolutionary algorithm Repair Operator Constrained Optimization

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Local Search Enhanced multi-objective evolutionary algorithm for Fuzzy Flexible Job Shop Scheduling

Local Search Enhanced Multi-Objective Evolutionary Algorithm...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Zhang, Xuwei Zhao, Ziyan Liu, Shixin College of Information Science and Engineering Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (纸本)9781665430654

The uncertainty in actual manufacturing systems often manifests as uncertain processing times, especially in flexible manufacturing systems. This work proposes a Decomposition-based evolutionary algorithm with Local Search (DLSEA) to solve flexible scheduling with fuzzy processing times by minimizing makespan and total machine workload. Considering the different scales of objectives, two normalization methods are employed on subpopulations, respectively, aiming to mitigate the potential detrimental effects of a single normalization method. This work also introduces a local search method to enhance the performance of DLSEA. The proposed DLSEA is compared with four state-of-the-art algorithms on two series of cases. The experimental results show that DLSEA exhibits superior search capabilities. © 2023 IEEE.

关键词： Fuzzy flexible job shop scheduling Local search multi-objective evolutionary algorithm Normalization

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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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A multi-objective evolutionary algorithm for steady-state constrained multi-objective optimization problems

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APPLIED SOFT COMPUTING 2021年 101卷 107042-107042页

作者： Yang, Yongkuan Liu, Jianchang Tan, Shubin Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Northeastern Univ Dept Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Many multi-objective evolutionary algorithms (MOEAs) are developed to solve constrained multiobjective optimization problems (CMOPs). However, they encounter low efficiency for steady-state CMOPs which are to optimize a known feasible solution named the current steady-state operation point. This paper proposes a multi-objective evolutionary algorithm (MOEA), termed FACE, for tackling the steady-state CMOPs. In FACE, the known feasible solution is maintained in the second population and evolves together with the main population. The main population is evolved by global search without handling constraints to accelerate the convergence. And the second population is evolved by local search to hold and achieve more feasible solutions. Two kinds of performance comparison between FACE and five representative MOEAs are made: the first using two cases of steady-state multisource compressed-air pipeline optimization problems to evaluate the performance of FACE on real life applications, and the second using a set of bench-mark test problems of CMOPs assigned with a known feasible solution to further assess the scalability of FACE. The results demonstrate the efficiency and the scalability of FACE with a potential for steady-state CMOPs. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Steady-state constrained multi-objective optimization Co-evolution

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A multi-objective evolutionary algorithm for Fuzzy Mean-variance-entropy Portfolio Models with Transaction Cost and Liquidity

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电脑学刊 2018年第4期29卷 39-56页

作者： Wei Yue Yuping Wang Zhan Peng

The main drawbacks of mean-variance model are to generate corner solutions and low diversity in the portfolios. To overcome these defects, firstly, we propose a new proportion entropy function as an objective function to generate well-diversified portfolio. Secondly, considering the transaction cost and liquidity, we present a new fuzzy mean-variance-entropy multi-objective portfolio selection model to find tradeoffs between risk, return and the diversification degree of portfolio, which is able to address a more realistic portfolio selection problem. Thirdly, we combined several efficient schemes to form an efficient algorithm to maintain the diversity of obtained solutions and to solve the presented multi-objective portfolio selection model. The proposed multi-objective portfolio model combined with the multi-objective evolutionary algorithm can overcome these defects fundamentally. Finally, to demonstrate the efficiency and effectiveness of the proposed model and algorithm, the designed algorithm is compared with two famous algorithms: multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm II (NSGA-II) through some simulations based on the data of the Shanghai Stock Exchange Market. Simulation results show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto fronts than the other two algorithms, and our proposed portfolio model can yield good performance of portfolio.

关键词： entropy fuzzy variable multi-objective evolutionary algorithm portfolio selection possibilistic moments

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Neural architecture search via reference point based multi-objective evolutionary algorithm

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PATTERN RECOGNITION 2022年 132卷

作者： Tong, Lyuyang Du, Bo Wuhan Univ Sch Comp Sci Natl Engn Res Ctr Multimedia Software Inst Artificial Intelligence Wuhan Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan Peoples R China

For neural architecture search, NSGA-Net has searched a representative neural architecture set of Pareto-optimal solutions to consider both accuracy and computation complexity simultaneously. However, some decision-makers only concentrate on such neural architectures in the subpart regions of Pareto-optimal Frontier that they have interests in. Under the above circumstances, certain uninterested neural architectures may cost many computing resources. In order to consider the preference of decision-makers, we propose the reference point based NSGA-Net (RNSGA-Net) for neural architecture search. The core of RNSGA-Net adopts the reference point approach to guarantee the Pareto-optimal region close to the reference points and also combines the advantage of NSGAII with the fast nondominated sorting approach to split the Pareto front. Moreover, we augment an extra bit value of the original encoding to represent two types of residual block and one type of dense block for residual connection and dense connection in the RNSGA-Net. In order to satisfy the decision-maker preference, the multi-objective is measured to search competitive neural architecture by minimizing an error metric and FLOPs of computational complexity. Experiment results on the CIFAR-10 dataset demonstrate that RNSGA-Net can improve NSGA-Net in terms of the more structured representation space and the preference of decision-makers. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Neural architecture search multi-objective evolutionary algorithm The image classification

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