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Hybrid biobjective evolutionary algorithms for the design of a hospital waste management network

JOURNAL OF HEURISTICS 2009年第2期15卷 153-176页

作者： Medaglia, Andres L. Villegas, Juan G. Rodriguez-Coca, Diana M. Univ Los Andes Ctr Optimizac & Probabilidad Aplicada Dept Ingn Ind Bogota Colombia Univ Antioquia Dept Ingn Ind Medellin Colombia Univ Los Andes Dept Ingn Ind Los Andes Colombia Univ Technol Troyes LOSI Troyes France Escuela Colombiana Ingn Fac Ingn Bogota Colombia

Colombian environmental authorities are exploring new alternatives for improving the disposal of hospital waste generated in the Department of Boyaca (Colombia). To design this hospital waste management network we propose a biobjective obnoxious facility location problem (BOOFLP) that deals with the existing tradeoff between a low-cost operating network and the negative effect on the population living near the waste management facilities. To solve the BOOFLP we propose a hybrid approach that combines a multiobjective evolutionary algorithm (NSGA II) with a mixed-integer program. The algorithms are compared against the Noninferior Set Estimation (NISE) method and tested on data from Boyaca's hospital waste management network and publicly available instances.

关键词： Facility location Hybrid genetic algorithms multiobjective evolutionary algorithms NSGA II multiobjective optimization

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Smart Industry Strategies for Shop-Floor Production Planning Problems to Support Mass Customization 1

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6th Iberoamerican Congress on Smart Cities (ICSC-Cities)

作者： Rossit, Diego Rossit, Daniel Nesmachnow, Sergio Univ Nacl CONICET INMABB Dept Ingn Bahia Blanca Buenos Aires Argentina Univ Republica Montevideo Uruguay

ISBN: (数字)9783031525179

ISBN: (纸本)9783031525162;9783031525179

The smart industry paradigm has revolutionized the landscape of production processes, ushering in new strategies to meet evolving demands. Among these strategies, mass customization stands out, for producing nearly tailored products based on customers preferences, while still using massive production techniques that allow keeping costs burdened. However, to embrace mass customization several operations at shop-floor level of the industry have to be adjusted, among them production planning strategies due to the emergence of missing operations. In this line, this article presents a suite of metaheuristic algorithms designed to tackle the multiobjective flowshop problem with missing operations while considering as optimization criteria the makespan, weighted total tardiness, and total completion time. Through extensive computational experiments on realistic instances, the performance of the applied metaheuristics is thoroughly evaluated. The results underscore the competitiveness of the proposed approaches in effectively addressing the intrinsic computational complexity of the addressed optimization problem, affirming their viability for real-world applications.

关键词： Smart industry Mass customization Missing operations Flowshop problem multiobjective evolutionary algorithms

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A classification-assisted environmental selection strategy for multiobjective optimization

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SWARM AND evolutionary COMPUTATION 2022年第0期71卷

作者： Zhang, Jinyuan Ishibuchi, Hisao He, Linjun Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Environmental selection of multiobjective evolutionary algorithms (MOEAs) is a key component that chooses promising solutions from a candidate set for later usage. Most environmental selection strategies choose solutions based on their function values. However, in real-world optimization problems, function evaluations can be time consuming. The necessity of a large number of function evaluations leads to the low efficiency of MOEAs. How to decrease the number of function evaluations is one of the main issues of MOEAs. The environmental selection can be regarded as a classification process. The selected solutions are the promising class, and the discarded solutions are the unpromising class. Based on this consideration, we propose a classification-assisted environmental selection (CAES) strategy in this paper to decrease the number of function evaluations in MOEAs. In the proposed method, solutions are divided into two classes. One is non-dominated solutions (i.e. promising class) and the other is dominated solutions (i.e. unpromising class). The classifier is built to classify the offspring solutions into these two classes. Only promising offspring are evaluated (unpromising ones are removed with no function evaluations). Therefore, the number of function evaluations is reduced. We integrate the proposed CAES strategy into six MOEAs. The effectiveness of the proposed CAES strategy is examined through computational experiments on various test suites and three real-world application problems. Our experimental results show that the proposed CAES strategy clearly reduces the number of function evaluations without severely degrading the search ability of the original MOEAs.

关键词： multiobjective evolutionary algorithms Surrogate-assisted evolutionary algorithms Classification Environmental selection

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A multiobjective evolutionary Algorithm Based on Similarity for Community Detection from Signed Social Networks

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2274-2287页

作者： Liu, Chenlong Liu, Jing Jiang, Zhongzhou Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Various types of social relationships, such as friends and foes, can be represented as signed social networks (SNs) that contain both positive and negative links. Although many community detection (CD) algorithms have been proposed, most of them were designed primarily for networks containing only positive links. Thus, it is important to design CD algorithms which can handle large-scale SNs. To this purpose, we first extend the original similarity to the signed similarity based on the social balance theory. Then, based on the signed similarity and the natural contradiction between positive and negative links, two objective functions are designed to model the problem of detecting communities in SNs as a multiobjective problem. Afterward, we propose a multiobjective evolutionary algorithm, called MEA(s) SN. In MEA(s)-SN, to overcome the defects of direct and indirect representations for communities, a direct and indirect combined representation is designed. Attributing to this representation, MEA(s)-SN can switch between different representations during the evolutionary process. As a result, MEA(s)-SN can benefit from both representations. Moreover, owing to this representation, MEA(s)-SN can also detect overlapping communities directly. In the experiments, both benchmark problems and large-scale synthetic networks generated by various parameter settings are used to validate the performance of MEA(s)-SN. The experimental results show the effectiveness and efficacy of MEA(s)-SN on networks with 1000, 5000, and 10 000 nodes and also in various noisy situations. A thorough comparison is also made between MEA(s)-SN and three existing algorithms, and the results show that MEA(s)-SN outperforms other algorithms.

关键词： Community detection problems direct representation indirect representation multiobjective evolutionary algorithms signed social networks similarity

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Development of a new spatial analysis tool in mental health: Identification of highly autocorrelated areas (hot-spots) of schizophrenia using a multiobjective evolutionary Algorithm model (MOEA/HS)

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Epidemiology and Psychiatric Sciences 2011年第4期19卷 302-313页

作者： Carlos R. García-Alonso Luis Salvador-Carulla Miguel A. Negrín-Hernández Berta Moreno-Küstner ETEA Department of Management and Quantitative Methods University of Córdoba Córdoba (Spain) Scientific Association PSICOST Jerez (Spain) Department of Quantitative Methods University of Las Palmas de Gran Canaria Las Palmas (Spain) Department of Personality Evaluation and Psychological Treatment University of Málaga Málaga (Spain).

Aims — This study had two objectives: 1) to design and develop a computer-based tool, called Multi-Objective evolutionary Algorithm/Hot-Spots (MOEA/HS), to identify and geographically locate highly autocorrelated zones or hot-spots and which merges different methods, and 2) to carry out a demonstration study in a geographical area where previous information about the distribution of schizophrenia prevalence is available and which can therefore be compared. Methods — Local Indicators of Spatial Aggregation (LISA) models as well as the Bayesian Conditional Autoregressive Model (CAR) were used as objectives in a multicriteria framework when highly autocorrelated zones (hot-spots) need to be identified and geographically located. A Multi-Objective evolutionary Algorithm (MOEA) model was designed and used to identify highly autocorrelated areas of the prevalence of schizophrenia in Andalusia. Hot-spots were statistically identified using exponential-based QQ-Plots (statistics of extremes). Results — Efficient solutions (Pareto set) from MOEA/HS were analysed statistically and one main hot-spot was identified and spatially located. Our model can be used to identify and locate geographical hot-spots of schizophrenia prevalence in a large and complicated region. Conclusions — MOEA/HS enables a compromise to be achieved between different econometric methods by highlighting very special zones in complex areas where schizophrenia shows a high *** of Interest: This study was partly supported by the Andalusian Government, P05-TIC-00531, PAI:P06-CTS-01765, CTS-587, PI-338/2008]; the Ministry of Education and Science [TIN2005–08386-C05–02] and the Ministry of Health [PI08/90752]. No additional financial sources have been received. No involvements are in conflict with this paper.

关键词： multiobjective evolutionary algorithms spatial analysis schizophrenia health care

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A multiobjective evolutionary neuro-controller for nonminimum phase systems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2004年第11期E87D卷 2517-2520页

作者： Nam, D Lee, H Park, S Park, LJ Park, CH Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea Kangnung Natl Univ Sch Elect Engn & Informat Technol Kangnung 210702 South Korea

Nonminimum phase systems are difficult to be controlled with a conventional PID-type controller because of their inherent characteristics of undershooting. A neuro-controller combined with a PID-type controller has been shown to improve the control performance of the nonminimum phase systems while maintaining stability. In this paper, we apply a multiobjective evolutionary optimization method for training the neurocontroller to reduce the undershooting of the nonminimum phase system. The computer simulation shows that the proposed multiobjective approach is very effective and suitable because it can minimize the control error as well as reduce undershooting and chattering. This method can be applied to many industrial nonminimum phase problems with ease.

关键词： nonminimum phase systems multiobjective evolutionary algorithms neural networks neuro-control

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Design of a motorcycle frame using neuroacceleration strategies in MOEAs

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JOURNAL OF HEURISTICS 2009年第2期15卷 177-196页

作者： Rodriguez, Jorge E. Medaglia, Andres L. Coello Coello, Carlos A. Univ Los Andes Dept Ingn Ind Ctr Optimizac & Probabilidad Aplicada Bogota Colombia CINVESTAV IPN Dept Comp Mexico City 07300 DF Mexico

Designing a low-budget lightweight motorcycle frame with superior dynamic and mechanical properties is a complex engineering problem. This complexity is due in part to the presence of multiple design objectives-mass, structural stress and rigidity-, the high computational cost of the finite element (FE) simulations used to evaluate the objectives, and the nature of the design variables in the frame's geometry (discrete and continuous). Therefore, this paper presents a neuroacceleration strategy for multiobjective evolutionary algorithms (MOEAs) based on the combined use of real (FE simulations) and approximate fitness function evaluations. The proposed approach accelerates convergence to the Pareto optimal front (POF) comprised of nondominated frame designs. The proposed MOEA uses a mixed genotype to encode discrete and continuous design variables, and a set of genetic operators applied according to the type of variable. The results show that the proposed neuro-accelerated MOEAs, NN-NSGA II and NN-MicroGA, improve upon the performance of their original counterparts, NSGA II and MicroGA. Thus, this neuroacceleration strategy is shown to be effective and probably applicable to other FE-based engineering design problems.

关键词： multiobjective evolutionary algorithms Finite element analysis Neural networks Motorcycle Engineering design multiobjective optimization

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Automatic Device Model Parameter Extractions via Hybrid Intelligent Methodology

Automatic Device Model Parameter Extractions via Hybrid Inte...

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International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)

作者： Liu, Cheng-Che Li, Yiming Yang, Ya-Shu Chen, Chieh-Yang Chuang, Min-Hui Natl Chiao Twig Univ Parallel & Sci Comp Lab 1001 Ta Hsueh Rd Hsinchu 30010 Taiwan Natl Chiao Twig Univ Inst Commun Engn 1001 Ta Hsueh Rd Hsinchu 30010 Taiwan Natl Chiao Twig Univ Dept Elect & Comp Engn 1001 Ta Hsueh Rd Hsinchu 30010 Taiwan

ISBN: (纸本)9784863487635

We report an advanced hybrid intelligent methodology for device model parameter extractions combining multiobjective evolutionary algorithms, numerical optimization methods, and unsupervised learning neural networks on a unified optimization framework. The results between experimentally measured data and the calculation from industrial standard compact models are accurate, stable and convergent rapidly for all I-V curves. Verifications from diodes, bipolar transistors, MOSFETs, FinFETs, to nanowire MOSFETs confirm the robustness of the developed prototype, where the extraction is within 5% of accuracy.

关键词： Automatic model parameter extraction Hybrid intelligent methodology multiobjective evolutionary algorithms

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A Fuzzy-Dominance-Driven RMLS-BLGA and its Application in Traffic route guidance

A Fuzzy-Dominance-Driven RMLS-BLGA and its Application in Tr...

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International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2012)

作者： Wang Gaoping Zhao Weiwei Henan Univ Technol Sch Informat Sci & Engn Zhengzhou 450000 Peoples R China

ISBN: (纸本)9783037854853

In this paper, we study the fuzzification of the pareto dominance relation and its application in traffic route guidance. A generic ranking scheme is presented that assigns dominance degrees to any set of vectors in a scale-independent, nonsymmetric and set-dependent manner. Different fuzzy-based definitions of optimality and dominated solution are introduced. The corresponding extension of the RMLS-BLGA, so-called Fuzzy-Dominance-Driven RMLS-BLGA (FRMLS-BLGA), will be presented as well. To verify the usefulness of such an approach, the approach is tested on analytical test cases in order to show its validity. The solutions, provided by the proposed algorithm for the Traffic route guidance model,are promising when compared with an existing well-known algorithm.

关键词： multiobjective evolutionary algorithms The shortest path Route Guidance RMLS BLGA

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Integration Test of Classes and Aspects with a Multi-evolutionary and Coupling-Based Approach

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3rd International Symposium on Search-Based Software Engineering, SSBSE 2011

作者： Colanzi, Thelma Elita Guez Assuncao, Wesley Klewerton Vergilio, Silvia Regina Pozo, Aurora DInf Fed Univ Parana BR-19031970 Curitiba Parana Brazil

ISBN: (纸本)9783642237157;9783642237164

The integration test of aspect-oriented systems involves the determination of an order to integrate and test classes and aspects, which should be associated to a minimal possible stubbing cost. To determine such order is not trivial because different factors influence on the stubbing process. Many times these factors are in conflict and diverse good solutions are possible. Due to this, promising results have been obtained with multi-objective and evolutionary algorithms that generally optimize two coupling measures: number of attributes and methods. However, the problem can be more effectively addressed considering as many as coupling measures could be associated to the stubbing process. Therefore, this paper introduces MECBA, a Multi-evolutionary and Coupling-Based Approach to the test and integration order problem, which includes the definition of models to represent the dependency between modules and to quantify the stubbing costs. The approach is instantiated and evaluated considering four AspectJ programs and four coupling measures. The results represent a good trade-off between the objectives and an example of use of the obtained results shows how they can be used to reduce test effort and costs.

关键词： Integration testing aspect-oriented software multiobjective evolutionary algorithms

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