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multi-objective evolutionary algorithm for tuning the Type-2 inference engine on classification task

SOFT COMPUTING 2018年第15期22卷 5021-5031页

作者： Hinojosa, Edward C. Camargo, Heloisa A. Natl Univ San Agustin Arequipa Peru Univ Fed Sao Carlos Sao Carlos SP Brazil

Type-2 fuzzy systems have been investigated as an alternative formalism to deal with uncertainty when the classic Type-1 fuzzy systems do not offer the suitable flexibility for the representation of the information being modeled. The higher flexibility in representation comes with a higher complexity in the system modeling, mainly in the design of the Type-2 fuzzy sets and in the definition of the inference engine parameters. In this paper, we focus on the Type-2 fuzzy systems design, proposing a multi-objective evolutionary approach for tuning the Type-2 inference engine of a fuzzy rule-based classification system by means of automatically choosing the t-norm used in the inference process. The selection of the t-norm used plays an important hole, since different operators could lead to different results. In a preliminary version of this work, we have proposed an approach to design and optimize Type-2 fuzzy systems that includes the tuning of Type-2 fuzzy sets and the selection of rules. The additional tuning process proposed in this paper is an extension of the previous method in the sense that the same evolutionary procedure performs simultaneously the tuning of the inference mechanism and the tasks performed before. The evolutionary process is executed by means of a multi-objective genetic algorithm with three objectives that aim to balance the accuracy and interpretability of the system generated: the accuracy, the number of rules and the number of conditions in the rules. The proposed method has been compared with a state-of-the-art method proposed in the literature, presenting good results.

关键词： Type-2 fuzzy inference system Fuzzy rule-based classification systems Tuning multi-objective evolutionary algorithms

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Adaptively weighted decomposition based multi-objective evolutionary algorithm

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APPLIED INTELLIGENCE 2021年第6期51卷 3801-3823页

作者： Meghwani, Suraj S. Thakur, Manoj Vellore Inst Technol Sch Comp Sci & Engn SCOPE Vellore Tamil Nadu India Indian Inst Technol Sch Basic Sci Mandi Himachal Prades India

multi-objective evolutionary algorithm based on Decomposition (MOEA/D) decomposes a multi-objective problem into a number of scalar optimization problems using uniformly distributed weight vectors. However, uniformly distributed weight vectors do not guarantee uniformity of solutions on approximated Pareto-Front. This study proposes an adaptive strategy to modify these scalarizing weights after regular intervals by assessing the crowdedness of solutions using crowding distance operator. Experiments carried out over several benchmark problems with complex Pareto-Fronts show that such a strategy helps in improving the convergence and diversity of solutions on approximated Pareto-Front. Proposed algorithm also shows better performance when compared with other state-of-the-art multi-objective algorithms over most of the benchmark problems.

关键词： multi-objective evolutionary algorithms MOEA D algorithm Inverted generational distance Crowding distance

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Integration of evolutionary based assimilation into Kalman-type methods for streamflow simulations in ungauged watersheds

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JOURNAL OF HYDROLOGY 2012年 475卷 428-440页

作者： Dumedah, Gift Coulibaly, Paulin Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia McMaster Univ Sch Geog & Earth Sci Dept Civil Engn Hamilton ON L8S 4L8 Canada

Data assimilation (DA) has emerged as a valuable tool for the design and application of streamflow forecasting systems. But DA applications for streamflow simulations in ungauged basins are still very limited primarily because most updated ensemble members are not usually associated with converged state and model parameterizations. Other limitations include the evaluation of massive number of ensemble members, weak/unknown relationships between parameter values and predictors, and the transfer of several members from gauged watersheds to ungauged ones is computationally expensive. But the inherent dynamics of DA to account for uncertainties in model, forcing data, and imperfect observation provide an appealing approach to simulate watershed response in ungauged basins. This study proposes a DA method namely the Pareto-Particle-Ensemble Kalman Filter (ParetoParticleEnKF) to generate and archive a small number of continuously evolved members using multi-objective evolutionary strategy where these members are updated using particle and ensemble Kalman filtering methods. The archived members for gauged watersheds are combined using inverse distance weighting where they are applied to simulate watershed response in ungauged basins. The proposed method is demonstrated by assimilating daily streamflow into the Sacramento Soil Moisture Accounting (SAC-SMA) model for 10 watersheds in southern Ontario, Canada. After successfully transferring ensemble members from gauged watersheds to ungauged ones, the updated ensembles were applied to simulate streamflow for up to 10-days ahead to determine how long into the future would the quality/accuracy of simulations persist before they begin to deteriorate in the ungauged basin. The results show that the designed method can facilitate simulation of accurate streamflows for any time step, and generate accurate simulations for up to 10 days ahead in the ungauged basins. A unique evaluation procedure is the transfer of updated members in

关键词： Data assimilation Ungauged basins Regionalization Ensemble Kalman filter Particle filter multi-objective evolutionary algorithms

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multi-objective evolutionary simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem

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SWARM AND evolutionary COMPUTATION 2018年 40卷 255-271页

作者： Rahmati, Seyed Habib A. Ahmadi, Abbas Karimi, Behrooz Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran

This research develops a novel stochastic reliability-centered maintenance (RCM) mechanism within a new multi objective joint maintenance and production planning problem. RCM in this integrated problem is an agent that monitors and manages the maintenance functions of a stochastic complex production-planning problem, namely flexible job shop scheduling problem (FJSP). The novel developed RCM takes benefit of stochastic condition based maintenance (CBM) approach that works based on stochastic shocking scheme of machines during their process time. It activates the maintenance activities, including preventive and corrective maintenance, according to the degradation level of system reliability after shocks and not merely according to the predetermined intervals. In addition to the maintenance activation times, the maintenance durations of different kinds are also modeled stochastically. Furthermore, different types of stochastic maintenance costs are also considered alongside system reliability and complementation time (C-max). Moreover, as the problem belongs to the NP-Hard class of optimization problems, four multi-objective simulation based optimization (SBO) algorithms, called multi-objective biogeography based optimization (MOBBO) algorithm, Pareto envelope-based selection algorithm (PESA), new version of non-dominated sorting genetic algorithm (NSGAIII) and multi-objective evolutionary algorithm based on decomposition (MOEAD) are employed to solve the underlying problem. A novel visualization approach joint by Gant chart is also proposed to discuss the whole RCM scheme, systematically. Different test problems, statistical tests and outputs explain the problem and algorithms' performance explicitly.

关键词： Reliability-centered maintenance (RCM) Stochastic production model Condition-based maintenance (CBM) Shocking mechanism multi-objective simulation-based optimization (SBO) multi-objective evolutionary algorithms

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Setup coordination between two stages of a production system: A multi-objective evolutionary approach

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ANNALS OF OPERATIONS RESEARCH 2006年第1期147卷 175-198页

作者： Meloni, Carlo Naso, David Turchiano, Biagio Politecn Bari Dipartimento Elettrotecn & Elettron I-70125 Bari Italy

This paper describes the application of evolutionary algorithms to a typical multi-objective problem of serial production systems, in which two consecutive departments must organize their internal work, each taking into account the requirements of the other department. In particular, the paper compares three approaches based on different combinations of multi-objective evolutionary algorithms and local-search heuristics, using both small-size test instances and larger problems derived from an industrial production process. The analysis of the case-studies confirms the effectiveness of the evolutionary approaches, also enlightening the advantages and shortcomings of each considered algorithm.

关键词： multi-objective evolutionary algorithms scheduling sequencing manufacturing systems

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A risk-driven multi-objective evolutionary approach for selecting software requirements

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evolutionary INTELLIGENCE 2019年第3期12卷 421-444页

作者： Amaral, Aruan Elias, Gledson Univ Fed Paraiba Informat Ctr Joao Pessoa PB Brazil

Exploring iterative and incremental approaches, current software projects define various delivery releases, which ought to be developed within budgetary constraints, but raising customers' satisfaction as much as possible. In search based software engineering, such a problem is referred to as next release problem (NRP), in which the selection of software requirements for the next release is reformulated as an optimization problem. More recently, NRP proposals have evolved to a multi-objective perspective, providing non-dominated recommendations that balance the trade-off surface with respect to customers' satisfaction and development cost. Despite key advancements in NRP proposals, most of them do not deal with software risks, which represent a vital aspect that can deeply impact on project cost and customers' satisfaction. In such a direction, using multi-objective evolutionary algorithms (MOEAs), this paper proposes and evaluates a risk-driven systematic approach for the NRP problem, in which a risk analysis is incorporated for estimating the impact of software risks in development cost and customers' satisfaction. By contrasting three different well-known MOEAs, empirical results based on two semi-real datasets reveal the potential efficiency and practical applicability, bringing not only more realistic, precise and meaningful estimations, but also finding a significant percentage of unanticipated solutions.

关键词： Software requirements Risk management Next release problem multi-objective evolutionary algorithms Search based software engineering

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multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems

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evolutionary INTELLIGENCE 2009年第1-2期2卷 21-37页

作者： Antonelli, Michela Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elett Informat Telecom Via Diotisalvi 2 I-56122 Pisa Italy

In this paper, we propose a multi-objective evolutionary algorithm (MOEA) to generate Mamdani fuzzy rule-based systems with different trade-offs between accuracy and complexity by learning concurrently granularities of the input and output partitions, membership function (MF) parameters and rules. To this aim, we introduce the concept of virtual and concrete partitions: the former is defined by uniformly partitioning each linguistic variable with a fixed maximum number of fuzzy sets;the latter takes into account, for each variable, the number of fuzzy sets determined by the evolutionary process. Rule bases and MF parameters are defined on the virtual partitions and, whenever a fitness evaluation is required, mapped to the concrete partitions by employing appropriate mapping strategies. The implementation of the MOEA relies on a chromosome composed of three parts, which codify the partition granularities, the virtual rule base and the membership function parameters, respectively, and on purposely-defined genetic operators. The MOEA has been tested on three real-world regression problems achieving very promising results. In particular, we highlight how starting from randomly generated solutions, the MOEA is able to determine different granularities for different variables achieving good trade-offs between complexity and accuracy.

关键词： Accuracy-interpretability trade-off Granularity learning Mamdani fuzzy rule-basedsystems multi-objective evolutionary algorithms Piecewise linear transformation

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A Parallel Framework of Combining Satisfiability Modulo Theory with Indicator-Based evolutionary Algorithm for Configuring Large and Real Software Product Lines

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2019年第4期29卷 489-513页

作者： Shi, Kai Yu, Huiqun Guo, Jianmei Fan, Guisheng Chen, Liqiong Yang, Xingguang East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai Peoples R China

multi-objective evolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-ofthe-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest LINUX SPL from 5 to 30 min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator.

关键词： Constraint solving search-based software engineering software product lines multi-objective evolutionary algorithms parallelization

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A multi-objective evolutionary algorithm for protein structure prediction with immune operators

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2009年第4期12卷 407-413页

作者： Judy, M. V. Ravichandran, K. S. Murugesan, K. SASTRA Univ Sch Comp Thanjavur India Natl Inst Technol Dept Math Tiruchirappalli India

Genetic algorithms (GA) are often well suited for optimisation problems involving several conflicting objectives. It is more suitable to model the protein structure prediction problem as a multi-objective optimisation problem since the potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms) and experiments have shown that those types of interactions are in conflict, by using the potential energy function, Chemistry at Harvard Macromolecular Mechanics. In this paper, we have modified the immune inspired Pareto archived evolutionary strategy (I-PAES) algorithm and denoted it as MI-PAES. It can effectively exploit some prior knowledge about the hydrophobic interactions, which is one of the most important driving forces in protein folding to make vaccines. The proposed MI-PAES is comparable with other evolutionary algorithms proposed in literature, both in terms of best solution found and the computational time and often results in much better search ability than that of the canonical GA.

关键词： protein structure prediction multi-objective evolutionary algorithms genetic algorithms immune operators Pareto archived evolutionary strategy

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A multi-objective evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2013年第10期66卷 1971-1980页

作者： Garcia-Alonso, Carlos R. Campoy-Munoz, Pilar Salazar Ordonez, Melania Univ LOYOLA Andalucia Dept Quantitat Methods Cordoba 14004 Spain Univ LOYOLA Andalucia Dept Econ Cordoba 14004 Spain

Bayesian Networks are increasingly being used to model complex socio-economic systems by expert knowledge elicitation even when data is scarce or does not exist. In this paper, a multi-objective evolutionary Algorithm (MOEA) is presented for assessing the parameters (input relevance/weights) of fuzzy dependence relationships in a Bayesian Network (BN). The MOEA was designed to include a hybrid model that combines Monte-Carlo simulation and fuzzy inference. The MOEA-based prototype assesses the input weights of fuzzy dependence relationships by learning from available output data. In socio-economic systems, the determination of how a specific input variable affects the expected results can be critical and it is still one of the most important challenges in Bayesian modeling. The MOEA was checked by estimating the migrant stock as a relevant variable in a BN model for forecasting remittances. For a specific year, results showed similar input weights than those given by economists but it is very computationally demanding. The proposed hybrid-approach is an efficient procedure to estimate output values in BN. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms Fuzzy inference Bayesian networks Monte-Carlo simulation Remittances

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