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A coevolutionary algorithm for exploiting a large fuzzy outranking relation

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期323卷 540-552页

作者： Noriega, Jesus Jaime Solano Lopez, Juan Carlos Leyva Ochoa, Carlos Andre Onate Figueira, Jose Rui Univ Autonoma Occidente Econ & Management Sci Dept Culiacan Mexico Univ Lisbon CEGIST Inst Super Tecn Lisbon Portugal

The outranking approach in Multiple Criteria Decision Analysis (MCDA) uses ranking procedures to exploit a fuzzy outranking relation, which captures the decision maker's notion of a ranking. However, as decision problems become more complex and computer performance improves, new ranking procedures are needed to rank complex data sets that decision-makers may not interpret. This paper discusses recent efforts and potential directions for developing ranking procedures that use multiobjective evolutionary algorithms (MOEAs) to exploit a fuzzy outranking relation. After that, based on the cooperative coevolutionary algorithms (CCEA) approach, we suggest some fundamental modifications to extend the RP2-NSGA-II+H algorithm that improve the scalability of this MOEA to exploit large-sized fuzzy outranking relations. Empirical results indicate that adjustments improve the RP2-NSGA-II+H algorithm for the addressed problem. The proposed ranking procedure outperforms RP2- NSGA-II+H in terms of ranking error rates based on the experiments conducted. Our experimental results also demonstrate that the proposed approach can be scaled for instances of the ranking problem of up to one thousand alternatives.

关键词： Multiple criteria decision analysis Fuzzy outranking relations multiobjective evolutionary algorithms Cooperative coevolutionary algorithms Ranking procedures

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Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems

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APPLIED SOFT COMPUTING 2011年第2期11卷 2725-2734页

作者： Chitra, P. Rajaram, R. Venkatesh, P. Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India Penn State Univ University Pk PA 16802 USA

Task scheduling problem in heterogeneous systems (TSPHS) is a multiobjective optimization problem (MOP). multiobjective evolutionary algorithms (MOEA) are well suited for solving multiobjective task scheduling problem. In this paper, the two conflicting objectives namely, makespan and reliability are considered. The performance of MOEA scan be improved by hybridization with local search. Hybridization of MOEAs improves the convergence speed to Pareto front. Simple neighborhood search (SNS) algorithm is used as the local search algorithm. The weighted-sum based approach for solving the MOP with its hybrid version is compared. Then the two MOEAs: SPEA2 and NSGA-II are compared with each other in the pure and hybrid version for random task graphs and also for a real-time numerical application graph. The simulations confirm that Hybrid NSGA-II is best suited for solving the task scheduling problem. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Task scheduling Makespan Reliability multiobjective Optimization multiobjective evolutionary algorithms Local search

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evolutionary optimization of mould temperature control strategies: encoding and solving the multiobjective problem with standard evolution strategy and kit for evolution algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2004年第6期218卷 657-665页

作者： Mehnen, J Micheltisch, T Bartz-Beielstein, T Schmitt, K Univ Dortmund Dept Machining Technol D-44221 Dortmund Germany Univ Dortmund Dept Syst Anal Dortmund Germany

A modern injection mould is a high-technology tool for mass production. The products typically have to meet high quality demands because they are often visible to the user. In order to manufacture high quality parts, the temperature of the mould has to be kept at an appropriate level and the temperature has to be equally distributed within the form. A net of interconnected deep-hole bores leads a water-oil emulsion to the critical areas for controlling the temperatures in the tool. Usually, the layout of the bores is constructed by means of human expert knowledge. In this paper an objective mathematical measure for the multiobjective quality of a mould temperature control system is introduced. Two different encoding approaches for cooling systems are compared. An approach to solve the complex layout problem using an evolutionary algorithm is described. To show the complexity of this multiobjective problem and to interpret and compare the quality of the existing algorithms, a new software environment called KEA (kit for evolutionary algorithms) is used.

关键词： mould temperature control multiobjective evolutionary algorithms encoding fitness function design

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evolutionary optimization of mould temperature control strategies: encoding and solving the multiobjective problem with standard evolution strategy and kit for evolution algorithms

Evolutionary optimization of mould temperature control strat...

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3rd International Seminar on Intelligent Computation in Manufacturing Engineering (ICME 2002)

作者： Mehnen, J Micheltisch, T Bartz-Beielstein, T Schmitt, K Univ Dortmund Dept Machining Technol D-44221 Dortmund Germany Univ Dortmund Dept Syst Anal Dortmund Germany

关键词： mould temperature control multiobjective evolutionary algorithms encoding fitness function design

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Evaluating evolutionary multiobjective algorithms for the in silico Optimization of Mutant Strains

Evaluating Evolutionary Multiobjective Algorithms for the <i...

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8th IEEE International Conference on Bioinformatics and Bioengineering

作者： Maia, Paulo Rocha, Isabel Ferreira, Eugenio C. Rocha, Miguel Univ Minho Dept Informat CCTC P-4710057 Braga Portugal Univ Minho IBB Inst Biotechnol & Bioengn Ctr Biol Engn P-4710470 Braga Portugal

ISBN: (纸本)9781424428441

In Metabolic Engineering, the identification of genetic manipulations that lead to mutant strains able to produce a given compound of interest is a promising, while still complex process. evolutionary algorithms (EAs) have been a successful approach for tackling the underlying in silica optimization problems. The most common task is to solve a bi-level optimization problem, where the strain that maximizes the production of some compound is sought, while trying to keep the organism viable (maximizing biomass). In this work, this task is viewed as a multiobjective optimization problem and an approach based on multiobjective EAs is proposed. The algorithms are validated with a real world case study that uses E. coli to produce succinic acid. The results obtained are quite promising when compared to the available single objective algorithms.

关键词： multiobjective evolutionary algorithms Metabolic Engineering Flux-Balance Analysis Systems Biology

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Comparison between Pure and Surrogate-assisted evolutionary algorithms for multiobjective Optimization

Comparison between Pure and Surrogate-assisted Evolutionary ...

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International Conference on Fuzzy System and Data Mining (FSDM)

作者： Benini, Ernesto Venturelli, Giovanni Laniewski-Wollk, Lukasz Univ Padua Dept Ind Engn Via Venezia 1 Padua Italy Warsaw Univ Technol Inst Aeronaut & Appl Mech Warsaw Poland

ISBN: (纸本)9781614996194;9781614996187

In this paper, a comparison between a "pure" genetic algorithm (GeDEA-II) and a surrogate-assisted algorithm (ASEMOO) is carried out using up-to-date multiobjective and multidimensional test functions. The experimental results show that the use of surrogates greatly improves convergence when both two-and three-objective test cases are dealt with. However, its convergence capabilities depend on how the surrogate can have an accurate picture of the fitness function landscape and seem to decrease as the number of the objective increases from two to three. On the other hand, a pure genetic algorithm always assures a minimum level of "front coverage", regardless of the problem on hand. Such minimum level could be considered sufficient for real-life problem optimizations. Also The dimensionality of the design space affects in opposite directions the two algorithms: for ASEMOO the increase of dimensionality is detrimental on performance, while GeDEA-II experiences benefits due to total amount of direct evaluations. It seems that GeDEA-II has an optimal population size around 20, regardless the dimensionality of the problem at hand.

关键词： multiobjective evolutionary algorithms Nature inspired computing formatting Surrogates Optimization

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multiobjective Optimization of a Pin-Fin Heat Sink Using evolutionary algorithms

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JOURNAL OF ELECTRONIC PACKAGING 2012年第2期134卷 21008-21008页

作者： Kanyakam, Siwadol Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Fac Engn Khon Kaen 40002 Thailand

This paper presents the use of multiobjective evolutionary algorithms for the optimal geometrical design of a pin-fin heat sink. The multiobjective design problem is posed to minimize two conflicting objectives: the junction temperature and the fan pumping power of the heat sink. The design variables are mixed integer/continuous. The encoding/decoding process for this mixed integer/continuous design variables is detailed. The multiobjective optimizers employed to solve the design problem are population-based incremental learning, strength Pareto evolutionary algorithm, particles swarm optimization, and archived multiobjective simulated annealing. The approximate Pareto fronts obtained from using the various optimizers are compared based upon the hypervolume and generational distance indicators. From the results, population-based incremental learning (PBIL) outperforms the others. The new design approach is said to be superior to a classical design approach. It is also illustrated that the proposed multiobjective design process leads to better design compared to the current commercial pin-fin heat sinks. [DOI: 10.1115/1.4006514]

关键词： multiobjective optimization pin-fin heat sinks multiobjective evolutionary algorithms performance comparison computational fluid dynamics

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Weight grouping operators selection strategy for a multiobjective evolutionary algorithm based on decomposition

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APPLIED INTELLIGENCE 2023年第9期53卷 10585-10601页

作者： Shi, Lin Tan, Yanyan Yan, Zeyuan Meng, Lili Liu, Li Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Shandong Normal Univ Inst Data Sci & Technol Jinan 250358 Peoples R China

multiobjective evolutionary algorithm based on decomposition (MOEA/D) works by using a set of uniformly distributed weight vectors to decompose a multiobjective optimization problem (MOP) into multiple single objective optimization subproblems for simultaneous optimization. Selecting parents from the neighborhood when performing reproduction is highly probable, thus different operators and neighborhoods may produce different offspring. However, one omnipotent operator usually cannot handle all different complex MOPs very well, for this reason, we propose a weight grouping operators selection (WGOS) strategy. Firstly, we divide the weight vectors into several groups and assign different reproduction operators to each group. The subproblems in each group are optimized by using the reproduction operators assigned. Then, the size of each group is dynamically adjusted according to the quality of the generated offspring, and the group of the reproduction operator that generates the better offspring expands, and the group of the reproduction operator that generates the worse offspring shrinks correspondingly until all the groups are merged into one and all adopt the same reproduction operator. This process will eventually pick out one operator for subsequent evolution. Finally, we will verify the performance of the selected operator to decide whether to continue using it or switch to another operator. A large number of comparative experiments have proved this strategy has better performance.

关键词： multiobjective evolutionary algorithms Decomposition Weight vector group Operator selection

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multiobjective strain design: A framework for modular cell engineering

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METABOLIC ENGINEERING 2019年 51卷 110-120页

作者： Garcia, Sergio Trinh, Cong T. Univ Tennessee Dept Chem & Biomol Engn 1512 Middle DrDO 432 Knoxville TN 37996 USA Oak Ridge Natl Lab Ctr Bioenergy Innovat Oak Ridge TN USA

Diversity of cellular metabolism can be harnessed to produce a large space of molecules. However, development of optimal strains with high product titers, rates, and yields required for industrial production is laborious and expensive. To accelerate the strain engineering process, we have recently introduced a modular cell design concept that enables rapid generation of optimal production strains by systematically assembling a modular cell with an exchangeable production module(s) to produce target molecules efficiently. In this study, we formulated the modular cell design concept as a general multiobjective optimization problem with flexible design objectives derived from mass balance. We developed algorithms and an associated software package, named ModCell2, to implement the design. We demonstrated that ModCell2 can systematically identify genetic modifications to design modular cells that can couple with a variety of production modules and exhibit a minimal tradeoff among modularity, performance, and robustness. Analysis of the modular cell designs revealed both intuitive and complex metabolic architectures enabling modular production of these molecules. We envision ModCell2 provides a powerful tool to guide modular cell engineering and sheds light on modular design principles of biological systems.

关键词： Modular cell engineering Modular cell Production modules Modular design Modularity multiobjective optimization multiobjective evolutionary algorithms

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A tutorial on multiobjective optimization: fundamentals and evolutionary methods

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NATURAL COMPUTING 2018年第3期17卷 585-609页

作者： Emmerich, Michael T. M. Deutz, Andre H. Leiden Univ LIACS Leiden Netherlands

In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization. The aim is to provide a starting point for researching in this active area, and it should also help the advanced reader to identify open research topics.

关键词： multiobjective optimization multiobjective evolutionary algorithms Decomposition-based MOEAs Indicator-based MOEAs Pareto-based MOEAs Performance assessment

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