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6th International Conference on evolutionary multi-Criterion Optimization

作者： Gaspar-Cunha, Antonio Loyens, Dirk van Hattum, Ferrie Univ Minho Inst Polymer & Composites I3N Campus Azurem P-4800058 Guimaraes Portugal Univ Minho Sch Architecture P-4800058 Guimaraes Portugal ESAD Escola Superior Artes Design Avenida Calouste Gulbenkian P-4460268 Senhora Hora Matosinhos Portugal

ISBN: (纸本)9783642198922

The use of computational methodologies for the optimization of aesthetic parameters is not frequent mainly due to the fact that these parameters are not quantifiable and are subjective. In this work an interactive methodology based on the use of multi-objective optimization algorithms is proposed. This strategy associates the results of different optimization runs considering the existent quantifiable objectives and different sets of boundary conditions concerning the decision variables, as defined by an expert decision maker. The associated results will serve as initial population of solutions for a final optimization run. The idea is that a more global picture of potential "good" solutions can be found. At the end this will facilitate the work of the expert decision maker since more solutions are available. The method was applied to a case study and the preliminary results obtained showed the potentially of the strategy adopted.

关键词： aesthetic design multi-objective evolutionary algorithms

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A Survey of Applications of multi-objective evolutionary algorithms in Biotechnology 13

A Survey of Applications of Multi-Objective Evolutionary Alg...

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13th IEEE Congress on evolutionary Computation, CEC 2024

作者： Felipe Coello Castillo, Carlos A. Coello, Carlos Universidad Autó Noma Metropolitana Unidad Cuajimalpa Posgrado en Ciencias Naturales e Ingeniería Av. Vasco de Quiroga 4871 Col. Santa Fe Cuajimalpa Delegació CuajimalpaCiudad de Maxico Mexico05348 Mexico Departamento de Computación Av. IPN No. 2508 Col. San Pedro Zacatenco Mexico Mexico

ISBN: (纸本)9798350308365

This paper presents a survey of applications of multi-objective evolutionary algorithms in several biotechnology areas. The application areas covered in the survey include: molecular docking, metabolic engineering, synthetic biology, optimization of industrial bio-processes and data processing for bioinformatics (which covers multiple sequence alignment and feature selection and classification for diagnosis of diseases). In the final part of the paper, some potential areas for future research are briefly discussed. © 2024 IEEE.

关键词： applications biotechnology multi-objective evolutionary algorithms multi-objective optimization

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The Elite Optimality Procedure for multi-objective evolutionary algorithms

The Elite Optimality Procedure for Multi-Objective Evolution...

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2016 International Conference on Applied Mathematics, Simulation and Modelling(AMSM2016)

作者： Truong Hong Trinh University of Economics The University of Danang

ISBN: (纸本)9781510833890

multi-objective evolutionary algorithms(MOEAs) are likely used to identify non-dominated solutions or Pareto front(the known Pareto front) in multi-objective optimization problems. The quality of Pareto front depends on evolution strategies that are evaluated under performance metrics of generational distance, spacing, and error ratio. In this paper, a procedure of Elite optimality is proposed to transform the known Pareto front(PFknown) into the true Pareto front(PFtrue). The Elite optimality procedure improves the quality of the Pareto fronts that deals with the biggest challenge in the multi-objective evolutionary algorithms.

关键词： multi-objective imization multi-objective evolutionary algorithms pareto front elite optimality

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Preventive maintenance optimisation of multi-equipment manufacturing systems by combining discrete event simulation and multi-objective evolutionary algorithms

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PRODUCTION PLANNING & CONTROL 2008年第4期19卷 342-355页

作者： Oyarbide-Zubillaga, A. Goti, A. Sanchez, A. Univ Mondragon Ind Management Dept Arrasate Mondragon Spain Univ Politecn Valencia Dept Stat & Operat Res E-46071 Valencia Spain

This paper is focused on preventive maintenance optimisation in manufacturing environments, with the objective of determining the optimal preventive maintenance frequencies for multi-equipment systems under cost and profit criteria. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multi-objective evolutionary algorithms have shown to deal with multi-objective problems to develop a maintenance management and optimisation approach. An application case is performed where the activities applied on a system that produces hubcaps for the car making industry, showing the quantitative benefits of adopting the detailed approach.

关键词： preventive maintenance multi-equipment optimisation discrete event simulation multi-objective evolutionary algorithms metaheuristics

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Performance comparison of multi-objective evolutionary algorithms for exergetic and exergoenvironomic optimization of a benchmark combined heat and power system

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ENERGY 2021年 233卷 121135-121135页

作者： Nondy, J. Gogoi, T. K. Tezpur Univ Dept Mech Engn Tezpur 784028 Assam India

This paper compares the Non-dominated sorting genetic algorithm-II, Pareto envelope-based selection algorithm-II, and Strength Pareto evolutionary algorithm-II, while optimizing a benchmark combined heat and power system with two conflicting objectives. The most effective algorithm is determined based on the statistical parameters evaluated from 30 runs of execution, considering the hypervolume indicator and the average computational time as the performance criteria. A comparative assessment shows that the Pareto envelope-based selection algorithm-II is superior to the other two algorithms. Further, in this study, a multi-criteria decision analysis is performed on the Pareto set obtained from the Pareto envelope-based selection algorithm-II, using the technique for order preference by similarity to an ideal solution, combined with the Entropy method. To show the advantages of multi-objective optimization, the optimal solutions are also compared with the base case, and previously published results of the benchmark problem corresponding to single-objective optimization. From the Pareto envelope-based selection algorithm-II derived optimal solutions, 15.82% increase in the exergy efficiency and 12.22% reduction in the system cost rate are achieved over the base case results. The present optimal exergy efficiency is 11.89% higher and the system cost rate is 1.9% lower than the single-objective based optimal results. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Exergoenvironomic optimization multi-objective evolutionary algorithms NSGA-II PESA-II SPEA-II multi-criteria decision analysis

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Result diversification by multi-objective evolutionary algorithms with theoretical guarantees

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ARTIFICIAL INTELLIGENCE 2022年 309卷

作者： Qian, Chao Liu, Dan-Xuan Zhou, Zhi-Hua Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

Given a ground set of items, the result diversification problem aims to select a subset with high "quality " and "diversity " while satisfying some constraints. It arises in various real world artificial intelligence applications, such as web-based search, document summarization and feature selection, and also has applications in other areas, e.g., computational geometry, databases, finance and operations research. Previous algorithms are mainly based on greedy or local search. In this paper, we propose to reformulate the result diversification problem as a bi-objective maximization problem, and solve it by a multi objective evolutionary algorithm (EA), i.e., the GSEMO. We theoretically prove that the GSEMO can achieve the (asymptotically) optimal theoretical guarantees under both static and dynamic environments. For cardinality constraints, the GSEMO can achieve the optimal polynomial-time approximation ratio, 1/2. For more general matroid constraints, the GSEMO can achieve an asymptotically optimal polynomial-time approximation ratio, 1/2 - epsilon/(4n), where epsilon > 0 and n is the size of the ground set of items. Furthermore, when the objective function (i.e., a linear combination of quality and diversity) changes dynamically, the GSEMO can maintain this approximation ratio in polynomial running time, addressing the open question proposed by Borodin et al. [7]. This also theoretically shows the superiority of EAs over local search for solving dynamic optimization problems for the first time, and discloses the robustness of the mutation operator of EAs against dynamic changes. Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the stateof-the-art algorithms (i.e., the greedy algorithm and local search) under both static and dynamic environments. (C) 2022 Published by Elsevier B.V.

关键词： Result diversification Monotone submodular functions Diversities Cardinality constraints Matroid constraints Dynamic environments multi-objective evolutionary algorithms Running time analysis Experimental studies

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jHawanet: An Open-Source Project for the Implementation and Assessment of multi-objective evolutionary algorithms on Water Distribution Networks

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WATER 2019年第10期11卷 2018-2018页

作者： Gutierrez-Bahamondes, Jimmy H. Salgueiro, Yamisleydi Silva-Rubio, Sergio A. Alsina, Marco A. Mora-Melia, Daniel Fuertes-Miquel, Vicente S. Univ Talca Fac Engn Dept Computat Sci Engn Syst Doctoral Program Curico 3340000 Chile Univ Talca Fac Engn Dept Computat Sci Curico 3340000 Chile Univ Talca Fac Engn Dept Engn & Bldg Management Curico 3340000 Chile Univ Politecn Valencia Dept Hydraul Engn & Environm E-46022 Valencia Spain

Efficient design and management of water distribution networks is critical for conservation of water resources and minimization of both energy requirements and maintenance costs. Several computational routines have been proposed for the optimization of operational parameters that govern such networks. In particular, multi-objective evolutionary algorithms have proven to be useful both properly describing a network and optimizing its performance. Despite these computational advances, practical implementation of multi-objective optimization algorithms for water networks is an abstruse subject for researchers and engineers, particularly since efficient coupling between multi-objective algorithms and the hydraulic network model is required. Further, even if the coupling is successfully implemented, selecting the proper set of multi-objective algorithms for a given network, and addressing the quality of the obtained results (i.e., the approximate Pareto frontier) introduces additional complexities that further hinder the practical application of these algorithms. Here, we present an open-source project that couples the EPANET hydraulic network model with the jMetal framework for multi-objective optimization, allowing flexible implementation and comparison of different metaheuristic optimization algorithms through statistical quality assessment. Advantages of this project are discussed by comparing the performance of different multi-objective algorithms (i.e., NSGA-II, SPEA2, SMPSO) on case study water pump networks available in the literature.

关键词： optimization multi-objective evolutionary algorithms water distribution networks hydraulic network modeling EPANET jMetal NSGA-II

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Investigation and comparison of the performance of multi-objective evolutionary algorithms based on decomposition and dominance in portfolio optimization

Investigation and comparison of the performance of multi-obj...

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Iranian Conference on Electrical Engineering

作者： Mahsa Rajabi Hamid Khaloozadeh Electrical and Control department K. N. Toosi University of technology Tehran Iran

A new approach in multi-objective evolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionary multi-objective optimization, has not been widely used. Using this method, a multi-objective optimization problem is converted into a number of scalar subproblems, and all the subproblems are simultaneously optimized. In this paper, the performance and efficiency of the algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) with the performance of two algorithms NSGA-II and MOPSO (evolution optimization methods based on dominance) for solving constrained portfolio optimization in Tehran Stock Exchange, has been compared. Portfolio return and its risk has been considered as the optimization objectives and CvaR has been considered ax a risk measure. The results indicate the high potential of these algorithms for constrained portfolio optimization. Also, the results indicate that the optimization algorithm based on decomposition has lower computational complexity, and Pareto front is more extensive than the other two methods.

关键词： multi-objective evolutionary algorithms Portfolio optimization Value at conditional risk Decomposition Dominance

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A stopping criterion for multi-objective optimization evolutionary algorithms

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INFORMATION SCIENCES 2016年 367卷 700-718页

作者： Marti, Luis Garcia, Jesus Berlanga, Antonio Molina, Jose M. Univ Fed Fluminense Inst Comp Av Gal Milton Tavares de Souza BR-24210346 Niteroi RJ Brazil Univ Carlos III Madrid Dept Informat Grp Appl Artificial Intelligence Av Univ Carlos III Madrid 28270 Spain

This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. The MDR indicator, which is also introduced, is a special-purpose progress indicator designed for the purpose of stopping a multi-objective optimization. As part of the paper we describe the criterion from a theoretical perspective and examine its performance on a number of test problems. We also compare this method with similar approaches to the issue. The results of these experiments suggest that MGBM is a valid and accurate approach. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Stopping criteria Convergence detection Stagnation Progress indicators multi-objective evolutionary algorithms multi-objective optimization Kalman filters

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Result diversification with negative type distances by multi-objective evolutionary algorithms

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Fundamental Research 2024年

作者： Liu, Dan-Xuan Qian, Chao State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China

The result diversification problem is to select an optimal subset with high “quality” and “diversity” from a given ground set of items, which is popular in various applications such as web-based search, multi-document summarization and ensemble pruning. The diversity relies on the distance between items. Previous works mainly focused on metric distances, and applied a greedy or local search algorithm with theoretical guarantees. As a kind of global search algorithm inspired by Darwin's theory of evolution, evolutionary algorithms (EAs) can have a better optimization ability than greedy and local search, but often lack theoretical support. Recently, EAs have been introduced to result diversification, achieving good theoretical guarantees besides superior empirical performances. In this paper, we study whether EAs can still achieve good theoretical guarantees for result diversification with negative type distances, which are also a class of important dissimilarity measures, especially in information retrieval and sketching techniques. We propose to reformulate the result diversification problem with negative type distances as a bi-objective maximization problem, and solve it by multi-objective evolutionary algorithms (MOEAs). We prove that a simple MOEA (i.e., GSEMO) can achieve the best-known polynomial-time approximation ratio. Experiments are also performed to examine the performance of different MOEAs on the application of web-based search. © 2023

关键词： Diversities Monotone submodular functions multi-objective evolutionary algorithms Negative type distances Result diversification

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