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IEEE International Conference on System, Man, and Cybernetic

作者： Yang, Jia-Wei Cheng, Hsueh-Chien Chiang, Tsung-Che Fu, Li-Chen Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10764 Taiwan

ISBN: (纸本)9781424423835

In this paper, we solve two problems in a 300-mm wafer fabrication facility (fab). Firstly, for the lot scheduling problem, we propose a multi-objective genetic programming based rule generator (MOGPRG) to evolve useful dispatching rules, which can provide near-optimal lot schedules concerning multiple objectives. Secondly, the overhead hoist transports (OHT) routing problem is considered. As the modern automated material handling system (AMHS) is capable of doing tool-to-tool direct delivery, the congestion of OHTs may happen more often than the past. To deal with the traffic congestion in AMHS, a dynamic routing method is proposed to find the near-shortest and less-congested path for the OHT to travel along. It can reduce the traffic congestion and achieve fast lot delivery by adapting to the dynamic traffic environment. The proposed MOGPRG is integrated with the dynamic routing method to improve two fab performance metrics: mean cycle time and tardy rate. Experimental results show the effectiveness of the proposed MOGPRG and dynamic routing method.

关键词： multiobjective evolutionary algorithm production scheduling genetic programming dispatching rules overhead hoist transport (OHT) routing

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Sensor location in water distribution networks to detect contamination events - a multiobjective approach based on NSGA-II

Sensor location in water distribution networks to detect con...

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Antunes, Carlos Henggeler Dolores, Margarida Univ Coimbra Dept Elect & Comp Engn Polo 2 Coimbra Portugal Univ Coimbra INESC Coimbra Polo 2 Coimbra Portugal INESC Coimbra Rua Antero Quental 199 Coimbra Portugal

ISBN: (纸本)9781509006229

The distribution network is the most exposed part of water supply systems due to the large number and geographical dispersion of derivation nodes and access points. Therefore, a reliable monitoring and surveillance system based on a sensor network is necessary to timely detect contamination events. The sensor location problem in water distribution networks to detect (accidental or intentional) contamination events has been tackled by optimization approaches aimed to determine the best location for a set of sensors, thus allowing the management entity to detect those events in a short period of time and be able to minimize their impact on the population served. This paper presents a multiobjective evolutionary approach to determine the location of sensors in a water distribution network to detect a contamination event and minimize its potential consequences according to multiple, incommensurate and conflicting evaluation aspects of the merits of each solution. The objective functions are the expected time of detection, the expected population affected prior to detection, the expected consumption of contaminated water prior to detection, and the detection likelihood. A set of nondominated solutions representing the Pareto front is obtained, which have been validated with known solutions for the case studies. Further, this information enables to exploit tradeoffs and identify good compromise solutions according to the decision maker's preferences.

关键词： Water distribution networks sensor location multiobjective evolutionary algorithm

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multiobjective PSO algorithm with Multi-directional Convergence Strategy to Solve Flow Shop Scheduling Problems 13th

Multiobjective PSO Algorithm with Multi-directional Converge...

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13th International Conference on Management Science and Engineering Management (ICMSEM)

作者： Zhang, Wenqiang Hou, Wenlin Yang, Diji Xing, Zheng Gen, Mitsuo Henan Univ Technol Zhengzhou Peoples R China Fuzzy Log Syst Inst Iizuka Fukuoka Japan Tokyo Univ Sci Tokyo Japan

ISBN: (纸本)9783030212483;9783030212476

The flow shop scheduling problem (FSP) is a typical combinatorial optimization problem. In order to improve the efficacy in solving the FSP, an multiobjective PSO algorithm with multi-directional convergence strategy (MoPSO-MDCS) is proposed. The multi-directional convergence strategy tactfully combines the advantages of vector evaluated genetic algorithm (VEGA) and Pareto dominating and dominated relationship based fitness function (PDDR-FF). The strategy based on VEGA has a preference for the edge region of the Pareto front, the PDDR-FF strategy has the tendency converging toward the center area of the Pareto front, which preserve both the convergence rate and the distribution performance. The experimental results show that the convergence and distribution performance of MoPSO-MDCS is better than MoPSO, NSGA-II and SPEA2.

关键词： Particle swarm optimization Flow shop scheduling problem multiobjective evolutionary algorithm

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A Hybrid Constraint Handling Mechanism with Differential Evolution for Constrained multiobjective Optimization

A Hybrid Constraint Handling Mechanism with Differential Evo...

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IEEE Congress on evolutionary Computation (CEC)

作者： Hsieh, Min-Nan Chiang, Tsung-Che Fu, Li-Chen Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn & Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781424478354

In real-world applications, the optimization problems usually include some conflicting objectives and subject to many constraints. Much research has been done in the fields of multiobjective optimization and constrained optimization, but little focused on both topics simultaneously. In this study we present a hybrid constraint handling mechanism, which combines the epsilon-comparison method and penalty method. Unlike original epsilon-comparison method, we set an individual epsilon-value to each constraint and control it by the amount of violation. The penalty method deals with the region where constraint violation exceeds the epsilon-value and guides the search toward the epsilon-feasible region. The proposed algorithm is based on a well-known multiobjective evolutionary algorithm, NSGA-II, and introduces the operators in differential evolution (DE). A modified DE strategy, DE/better-to-best_feasible/1, is applied. The better individual is selected by tournament selection, and the best individual is selected from an archive. Performance of the proposed algorithm is compared with NSGA-II and an improved version with a self-adaptive fitness function. The proposed algorithm shows competitive results on sixteen public constrained multiobjective optimization problem instances.

关键词： multiobjective evolutionary algorithm Constraint Handling Differential Evolution Constrained multiobjective Optimization

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Progressive Diversity Management in evolutionary multiobjective Optimisation

Progressive Diversity Management in Evolutionary Multiobject...

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2010 IEEE World Congress on Computational Intelligence

作者： Adra, Salem F. Fleming, Peter J. Univ Sheffield Dept Comp Sci Sheffield S10 2TN S Yorkshire England Univ Sheffield Automat Control & Syst Engn Dept Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9781424481262

Convergence towards, and diversity across the Pareto-optimal front are the two main requirements when optimising a multiobjective optimisation problem (MOP) with conflicting objectives. Most established multiobjective evolutionary algorithms (MOEAs) have mechanisms that address these requirements. However, in many-objective optimisation, where the number of objectives is greater than 2 or 3, it has been found that these two requirements can conflict with one another, introducing problems such as dominance resistance and speciation. In this study, a previously introduced diversity management mechanism is deployed within a Progressive Preference Articulation (PPA) technique to optimise an 8-objective real-world problem of aircraft control system design. This paper illustrates the effective application of the new diversity management mechanism used in conjunction with the PPA technique when optimising a multiobjective real-world engineering problem.

关键词： Pareto optimisation Pareto-optimal diversity aircraft control aircraft control aircraft instrumentation decision making diversity management mechanism evolutionary computation evolutionary multiobjective optimisation multiobjective evolutionary algorithm progressive preference articulation

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multiobjective Optimization of Microwave Circuits with Many Structural Parameters and Objectives 11

Multiobjective Optimization of Microwave Circuits with Many ...

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International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Ding, Dawei Zhang, Xiuyin Zhang, Jun Cao, Yunfei Bai, Jin Liang Yang, Jun South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Peoples R China State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Beijing Inst Space Long March Vehicle Beijing 10076 Peoples R China

ISBN: (纸本)9781728121680

For many microwave circuit designs, there are multiple performance characteristics to be optimized simultaneously. It becomes more and more difficult to design these microwave circuits with the number of structural parameters and performance characteristics increasing. In this paper, a new multiobjective optimization technique, MOEA/D (multiobjective evolutionary algorithm based on decomposition) combined with FDTD (Finite Difference Time Domain) method, is proposed. As an example, a microstrip low-pass filter with 160 structural parameters and 4 performance characteristics is designed for obtaining low insertion loss, sharp out-of-band rejection, high stop-band attenuation, and good linearity. Comparisons on computational cost and searching speed are made to verify the high-efficiency of the proposed multiobjective optimization method.

关键词： Finite Difference Time Domain (FDTD) method microwave circuit multiobjective evolutionary algorithm multiobjective optimization method

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An Enhanced MOEA/D-DE and Its Application to multiobjective Analog Cell Sizing

An Enhanced MOEA/D-DE and Its Application to Multiobjective ...

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2010 IEEE World Congress on Computational Intelligence

作者： Liu, Bo Fernandez, Francisco V. Zhang, Qingfu Pak, Murat Sipahi, Suha Gielen, Georges Katholieke Univ Leuven ESAT MICAS Louvain Belgium Univ Seville CSIC IMSE Seville Spain Univ Essex Sch Elect Engn & Comp Sci Colchester Essex England Bogazici Univ Istanbul Turkey

ISBN: (纸本)9781424481262

Recently, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) and its extended version by using differential evolution (DE) as the main search engine (MOEA/D-DE) were proposed, which outperform several widely used multiobjective evolutionary algorithms. MOEA/D decomposes a multiobjective problem into a number of scalar optimization sub-problems with a neighborhood structure and optimizes them simultaneously to approximate the Pareto-optimal set. In this paper, two mechanisms are investigated to enhance the performance of MOEA/D-DE. Firstly, a new replacement mechanism is proposed to call for a balance between the diversity of the population and the employment of good information from neighbors. Secondly, the scaling factor in DE is randomized to enhance the search ability. Comparisons are carried out with MOEA/D-DE on ten benchmark problems, showing that the proposed method exhibits significant improvements. Finally, the enhanced MOEA/D-DE is applied to a real world problem, the sizing of a folded-cascode amplifier with four performance objectives.

关键词： Pareto optimal set Pareto optimisation decomposition differential equations differential evolution evolutionary computation multiobjective analog cell sizing multiobjective evolutionary algorithm multiobjective problem replacement mechanism scalar optimization sub-problem search engine search problems

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Gridless evolutionary Approach for Line Spectral Estimation With Unknown Model Order

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第2期54卷 935-947页

作者： Yan, Bai Zhao, Qi Zhang, Jin Zhang, J. Andrew Yao, Xin Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Univ Technol Sydney Sch Elect & Data Engn Ultimo NSW 2007 Australia

Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic l(0) norm (i.e., the continuous analog of l(0) norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of the atomic l(0) norm, such as the nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order using the atomic l(0) norm. To accomplish this idea, we build a multiobjective optimization model. The measurement error and the atomic l(0) norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic l(0) norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that helpfully exploring the variable and open-ended frequency search space and provide extensive potentials toward the optima. Another innovation is a model-order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model-order selection.

关键词： Atomic measurements Frequency estimation Estimation Computational modeling Search problems Optimization Minimization Gridless line spectral estimation (LSE) multiobjective evolutionary algorithm sparse representation

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Efficient fog prediction with multi-objective evolutionary neural networks

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APPLIED SOFT COMPUTING 2018年 70卷 347-358页

作者： Duran-Rosal, A. M. Fernandez, J. C. Casanova-Mateo, C. Sanz-Justo, J. Salcedo-Sanz, S. Hervas-Martinez, C. Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg3rd Floor Cordoba 14014 Spain Univ Valladolid LATUV Remote Sensing Lab Valladolid Spain Univ Politecn Madrid Dept Civil Engn Construct Infrastruct & Transport Madrid Spain Univ Alcala Dept Signal Proc & Commun Alcala De Henares Spain

This paper proposes the application of novel artificial neural networks with evolutionary training and different basic functions (sigmoidal, product and radial), for a real problem of fog events classification from meteorological input variables. Specifically, a multiobjective evolutionary algorithm is considered as artificial neural network training mechanism in order to obtain a binary classification model for the detection of fog events at Valladolid airport (Spain). The evolutionary neural models developed are based on two-dimensional performance measures: traditional accuracy and the minimum sensitivity, as the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. These performance measures are directly related to features associated with any classifier: its global performance and the rate of the worst classified class. These two objectives are usually in conflict when the optimization process tries to construct models with a high classification rate level in the generalization dataset, and also with a good classification level for each class or minimum sensitivity. A sensitivity analysis of the proposed models is carried out, and thus the subjacent relations between the input variables and the output classification target can be better understood. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiobjective evolutionary algorithm Neural networks Fog events prediction Time series

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Fuzzy-Based Pareto Optimality for Many-Objective evolutionary algorithms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第2期18卷 269-285页

作者： He, Zhenan Yen, Gary G. Zhang, Jun Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74075 USA Sun Yat Sen Univ Dept Comp Sci Guangzhou 510006 Guangdong Peoples R China

evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general. However, when problems with many objectives are encountered, nearly all algorithms perform poorly due to loss of selection pressure in fitness evaluation solely based upon the Pareto optimality principle. In this paper, we introduce a new fitness evaluation mechanism to continuously differentiate individuals into different degrees of optimality beyond the classification of the original Pareto dominance. The concept of fuzzy logic is adopted to define a fuzzy Pareto domination relation. As a case study, the fuzzy concept is incorporated into the designs of NSGA-II and SPEA2. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the original ones for solving many-objective optimization problems.

关键词： Fuzzy logic multiobjective evolutionary algorithm NSGA-II Pareto optimality SPEA2

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