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Comparison of multiobjective evolutionary algorithms for Prioritized Urban Waste Collection in Montevideo, Uruguay

Electronic Notes in Discrete Mathematics 2018年 69卷 93-100页

作者： Nesmachnow, Sergio Rossit, Diego Toutouh, Jamal Universidad de la República Uruguay Universidad Nacional del Sur-CONICET Argentina University of Malaga Spain

Urban waste collection is an important problem in modern cities, where efficient techniques are demanded to reduce large budgetary expenses, and avoid environmental and social problems. This article presents two state-of-the-art multiobjective evolutionary algorithms to solve a variant of the urban waste collection problem considering priorities and the conflicting goals of minimizing the total distance while maximizing the Quality of Service. The main results for real scenarios in Montevideo, Uruguay, show that accurate trade-off solutions outperformed greedy approaches, including the current routing methodology applied by local authorities. The competitiveness of the evolutionary algorithms was also confirmed when solving a prototype scenario using dynamic information. © 2018 Elsevier B.V.

关键词： multiobjective evolutionary algorithms routing Urban waste collection

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Hybrid multiobjective evolutionary algorithms for Unsupervised QPSO, BBPSO and Fuzzy clustering

Hybrid Multiobjective Evolutionary Algorithms for Unsupervis...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Lai, Daphne Teck Ching Sato, Yuji Univ Brunei Darussalam Inst Appl Data Analyt Gadong Brunei Univ Brunei Darussalam Digital Sci Fac Sci Gadong Brunei Hosei Univ Fac Comp & Informat Sci Tokyo Japan

ISBN: (纸本)9781728183923

While there has been many new developments in multiobjective evolutionary algorithms, they have not been applied or investigated in clustering problems. In this paper, ten different unsupervised clustering techniques applying different MOEA (SPEA2, IBEA, MOEA/D and MOEA/GLU), PSO (QPSO and BBPSO) and Fuzzy approaches are experimented on ten public datasets. The rationale to apply MOEA is to increase the exploitation capabilities of clustering techniques to further refine the cluster solutions found by fuzzy or PSO clustering. The aim is to investigate in the performance of different types of MOEA applications in clustering, determining whether MOEA Fuzzy clustering outperform MOEA PSO variants. Overall, MOEA/D BBPSO was found to produced the best results. It outperformed MOEA Fuzzy techniques, having tested on datasets with high number of classes, that are imbalanced and/or overlapping classes. IBEA Fuzzy clustering was found to produce the worst results. MOEA/D clustering was found to perform better than other MOEA techniques. In this work, we showed that MOEA/D BBPSO clustering produced the best results on challenging datasets. It was able to use MOEA/D to deepen its exploitation capability while benefiting from the exploratory ability of BBPSO when clustering challenging datasets.

关键词： fuzzy clustering swarm intelligence cluster analysis multiobjective evolutionary algorithms

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Graph-based Sequence Clustering through multiobjective evolutionary algorithms for Web Recommender Systems 07

Graph-based Sequence Clustering through Multiobjective Evolu...

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Demir, Gul Nildem Uyar, A. Sima Oguducu, Sule Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey

ISBN: (纸本)9781595936974

In web recommender systems, clustering is done offline to extract usage patterns and a successful recommendation highly depends oil the quality, Of this clustering solution. lit these types of applications, data to be clustered is in the form of user sessions which area sequences of web pages visited by the user. Sequence clustering is one of the important tools to work with this type of data. One way to represent sequence data is through weighted, undirected graphs where each sequence, is a vertex and the pairwise similarities between the user session,, are the edges. Through this representation, the problem becomes equivalent to graph partitioning which is NP-complete and is best, approached using multiple objectives. Hence it is suitable to use multiobjective evolutionary algorithms (MOEA) to solve it. The main focus of this paper is to determine an effective MOEA to cluster sequence data.. Several existing approaches in literature are compared oil sample data sets and the most suitable approach is determined.

关键词： graph-based clustering sequence clustering multiobjective evolutionary algorithms

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Connection Sparsification and Orbit Stabilization of Dynamic Binary Neural Networks based on multiobjective evolutionary algorithms

Connection Sparsification and Orbit Stabilization of Dynamic...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Togawa, Tomoyuki Saito, Toshimichi HOSEI Univ EE Dept Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781728169262

0 A dynamic binary neural network is characterized by ternary connection parameters and can generate various binary periodic orbits. This paper studies a two-objective problem for sparsity of the connection parameters and stability of the binary periodic orbits. In order to optimize the two-objective problem, we present a simple algorithm (ALG/M) based on the multiobjective evolutionary algorithm based on decomposition. The ALG/M decomposes the two-objective problem into multiple subproblems and can optimize the problem effectively. Performing elementary numerical experiments for typical examples of binary periodic orbits, it is confirmed that the ALG/M realizes both appropriate connection sparsity and strong orbit stability. It is also confirmed that the ALG/M outperforms another algorithm based on the regularization algorithms such as the Lasso.

关键词： binary neural networks stability sparsification periodic orbits multiobjective evolutionary algorithms

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Effects of Initialization Methods on the Performance of Surrogate-Based multiobjective evolutionary algorithms

Effects of Initialization Methods on the Performance of Surr...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Zhang, Jinyuan Ishibuchi, Hisao He, Linjun Nan, Yang Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665430654

Initialization plays a crucial role in surrogate-based multiobjective evolutionary algorithms (MOEAs) when tackling computationally expensive multiobjective optimization problems. During the initialization process, solutions are generated to train surrogate models. Consequently, the accuracy of these surrogate models depends on the quality of the initial solutions, which in turn directly impacts the performance of surrogate-based MOEAs. Despite the widespread use of Latin hypercube sampling as an initialization method in surrogate-based MOEAs, there is a lack of comprehensive research examining the effectiveness of different initialization methods. Additionally, the impact of the number of initial solutions on the performance of surrogate-based MOEAs remains largely unexplored. This paper aims to bridge these research gaps by comparing the usefulness of two commonly employed initialization methods (i.e., random sampling and Latin hypercube sampling) in surrogate-based MOEAs. Furthermore, it investigates how varying the number of initial solutions influences the performance of surrogate-based MOEAs. © 2023 IEEE.

关键词： initialization multiobjective evolutionary algorithms surrogate models

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Do Existing multiobjective evolutionary algorithms Use a Sufficient Number of Operators? An Empirical Investigation for Water Distribution Design Problems

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WATER RESOURCES RESEARCH 2020年第5期56卷 e2019WR026031-e2019WR026031页

作者： Wang, Peng Zecchin, Aaron C. Maier, Holger R. Zheng, Feifei Newman, Jeffrey P. Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China

multiobjective evolutionary algorithms (MOEAs) have been used extensively to solve water resources problems. Their success is dependent on how well the operators that control an algorithm's search behavior are able to identify near-optimal solutions. As commonly used MOEAs contain a relatively small number of operators (generally between 2 and 7), this study investigates whether the performance of MOEAs could potentially be improved by increasing their operator set size. This is done via a series of controlled computational experiments isolating the influence of the size of the operator set (i.e., how many operators are used, ranging from 2 to 12), the composition of the operator set (i.e., which operators are used, given a set number of operators), the search strategy used (e.g., parent selection and survivor selection), and increasing the operator set size of an existing MOEA. These experiments are performed on six benchmark water distribution optimization problems. Results of the 3,150 optimization runs indicate that operator set size is the dominant factor affecting algorithm performance, having a significantly greater influence than operator set composition and other factors affecting algorithm search behavior. In addition, increasing the operator set size of the state-of-the-art MOEA GALAXY, which has been designed specifically for solving water distribution optimization problems, from its currently used value of 6 to 12 increased its performance significantly. These results suggest there is value in investigating the potential of increasing operator set size for a range of algorithms and problem types.

关键词： multiobjective evolutionary algorithms operators water distribution system design optimization Borg GALAXY NSGA-II

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A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2016年 58卷 55-68页

作者： Chica, Manuel Bautista, Joaquin Cordon, Oscar Damas, Sergio European Ctr Soft Comp Mieres 33600 Spain Univ Politecn Cataluna ETSEIB E-08028 Barcelona Spain Univ Granada DECSAI E-18071 Granada Spain Univ Granada CITIC UGR E-18071 Granada Spain

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Robust optimization Assembly line balancing multiobjective evolutionary algorithms Uncertain demand

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Scaling-up multiobjective evolutionary clustering algorithms using stratification

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PATTERN RECOGNITION LETTERS 2017年 93卷 69-77页

作者： Garcia-Piquer, Alvaro Bacardit, Jaume Fornells, Albert Golobardes, Elisabet Inst Space Sci IEEC CSIC Campus UABC Can Magrans S-N Barcelona 08193 Spain Newcastle Univ Sch Comp Sci Newcastle NE1 7RU England Univ Ramon Llull HTSI Res Grp Hospitality Tourism & Mobil Av Marques de Mulhacen 40-42 Barcelona 08034 Spain Univ Ramon Llull GR SETAD Av Quatre Camins 30 Barcelona 08022 Spain

multiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms when more than one criterion is necessary to obtain understandable patterns from the data. However, these kind of techniques are expensive in terms of computational time and memory usage, and specific strategies are required to ensure their successful scalability when facing large-scale data sets. This work proposes the application of a data subset approach for scaling-up multiobjective clustering algorithms and it also analyzes the impact of three stratification methods. The experiments show that the use of the proposed data subset approach improves the performance of multiobjective evolutionary clustering algorithms without considerably penalizing the accuracy of the final clustering solution. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multiobjective evolutionary algorithms Clustering Scaling-Up Stratification

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A Bayesian Optimization Approach to Algorithm Parameter Tuning in Constrained multiobjective Optimization 7th

A Bayesian Optimization Approach to Algorithm Parameter Tuni...

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7th International Conference on Optimization and Learning

作者： Cork, Jordan N. Filipic, Bogdan Jozef Stefan Inst Jamova Cesta 39 Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Jamova Cesta 39 Ljubljana 1000 Slovenia

ISBN: (纸本)9783031779404;9783031779411

Algorithm parameter tuning is an often neglected step in the optimization process. This study shows that constrained multiobjective optimization can benefit significantly from tuning, in both the specialized (for an individual problem) and generalized (over a number of problems) parameter setting approaches. Numerical experiments conducted with three multiobjective optimization algorithms on 139 test problems from 13 benchmark suites quantify the algorithm performance improvement on individual problems. Additionally, regarding the generalized approach, alternative default parameter settings are identified. The study also identifies Bayesian optimization as an adequate method for tuning multiobjective evolutionary algorithms with constraint handling. Overall, it is concluded that, given sufficient computational resources to apply to a problem, parameter tuning, using an approach such as Bayesian optimization, should be conducted. If computational resources do not allow such tuning, then the proposed default parameters are applicable.

关键词： Constrained multiobjective optimization multiobjective evolutionary algorithms Algorithm parameter tuning Bayesian optimization Benchmark problems

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Automatic tuning of a fuzzy visual system using evolutionary-algorithms:: Single-objective versus multiobjective approaches

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2008年第2期16卷 485-501页

作者： Munoz-Salinas, Rafael Aguirre, Eugenio Cordon, Oscar Garcia-Silvente, Miguel Univ Cordoba Dept Comp Cordoba Spain Univ Granada ETS Ingn Informat Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain European Ctr Soft Comp Mieres 33600 Spain

One of the main advantages of fuzzy systems is their ability to design comprehensible models of real-world systems, thanks to the use of a fuzzy rule structure easily interpretable by human beings. This is especially useful for the design of fuzzy logic controllers, where the knowledge base can be extracted from expert knowledge. Even more, the availability of a readable structure allows the human expert to customize the fuzzy controller to different environments by manually tuning its components. Nevertheless, this tuning task is usually a time-consuming procedure when done manually, especially when several measures are considered to evaluate the controller performance, and thus the interest in the design of automatic tuning procedures for fuzzy systems has increased along the last few years. In this paper, we tackle the tuning of the fuzzy membership functions of a fuzzy visual system for autonomous robots. This fuzzy visual system is based on a hierarchical structure of three different fuzzy classifiers, whose combined action allows the robot to detect the presence of doors in the images captured by its camera. Although the global knowledge represented in the fuzzy system knowledge base makes it perform properly in the door detection task, its adaptation to the specific conditions of the environment where the robot is operating can significantly improve the classification accuracy. However, the tuning procedure is complex as two different performance indexes are involved in the optimization process (true positive and false positive detections), thus becoming a multiobjective problem. Hence, in order to automatically put the fuzzy system tuning into effect, different single and multiobjective evolutionary algorithms are considered to optimize the two criteria, and their behavior in problem solving is compared.

关键词： autonomous robots fuzzy system tuning genetic fuzzy systems multiobjective evolutionary algorithms

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