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evolutionary auto-design for aircraft engine cycle

COMPLEX & INTELLIGENT SYSTEMS 2024年第2期10卷 3169-3180页

作者： Feng, Xudong Liu, Zhening Wu, Feng Wang, Handing Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China Northwestern Polytech Univ Sch Power & Energy Xian Peoples R China

Traditional engine cycle innovation is limited by human experiences, imagination, and currently available engine component performance expectations. Thus, the engine cycle innovation process is quite slow for the past 90 years. In this work, we propose a mixed variable multi-objective evolutionary optimization method for automatic engine cycle design. In the first, a simulation toolkit is developed for performance evaluation of potentially viable engine cycle solutions. Then, the engine cycle solutions are mixed encoded by the pins and the parameters of different engine components. The new engine cycle solutions are generated through the mutation operator. Finally, we construct two optimization objectives to drive the optimization process. Through the experimental research, new engine cycle solutions are discovered that exceed the performance of known turbojet and turbofan engines.

关键词： multi-objective evolutionary algorithms Engineering design Mixed variable encoding

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Discovering gene association networks by multi-objective evolutionary quantitative association rules

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2014年第1期80卷 118-136页

作者： Martinez-Ballesteros, M. Nepomuceno-Chamorro, I. A. Riquelme, J. C. Univ Seville Dept Comp Sci Seville Spain

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Data mining multi-objective evolutionary algorithms Quantitative association rules Gene networks Microarray analysis

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Solving the ocean color inverse problem by using evolutionary multi-objective optimization of neuro-fuzzy systems

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2008年第5-6期12卷 339-355页

作者： Cococcioni, Marco Corsini, Giovanni Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elettron Informat Telecomunicaz Via Diotisalvi 2 I-56122 Pisa Italy

The ocean color inverse problem consists of determining the concentrations of optically active constituents, such as chlorophyll, suspended particulate matter and colored dissolved organic matter, from remotely sensed multispectral measurements of the reflected sunlight back-scattered by the water body. In this paper, we approach this regression problem by using an evolutionary multi-objective algorithm, namely the (2+2) Modified Pareto Archived evolutionary Strategy ((2+2)M-PAES), to optimize Takagi-Sugeno type (TS-type) fuzzy rule-based systems (FRBSs). Accuracy and complexity are the two competitive objectives to be simultaneously optimized. TS-type FRBSs are implemented as an artificial neural network;by training the neural network, the parameters of the fuzzy model are adjusted. In this way, the evolutionary optimization coarsely identifies the structure of the TS-type FRBSs, while the corresponding neural networks finely tune their parameters. As a result, a set of TS-type FRBSs with different trade-offs between accuracy and complexity is provided at the end of the optimization process. We show the effectiveness of our approach by comparing our results with those obtained on the ocean color inverse problem by other techniques recently proposed in the literature.

关键词： Ocean color Medium Resolution Imaging Spectrometer (MERIS) multi-objective evolutionary algorithms fuzzy rule-based systems Takagi-Sugeno fuzzy models Adaptive-Network-Based Fuzzy Inference System (ANFIS) (2+2) Modified-Pareto Archived evolutionary Strategy ((2+2)M-PAES)

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multi-objective evolutionary optimization of agent-based models: An application to emergency response planning

Multi-objective evolutionary optimization of agent-based mod...

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2nd IASTED International Conference on Computational Intelligence

作者： Narzisi, Giuseppe Mysore, Venkatesh Mishra, Bud Univ Catania Dept Math & Comp Sci Catania Italy NYU Courant Inst Mathemat Sci New York NY 10003 USA

ISBN: (纸本)9780889866027

Agent-based models (ABMs) / multi-agent systems (MASs) are today one of the most widely used modeling simulation-analysis approaches for understanding the dynamical behavior of complex systems. These models are often characterized by several parameters with nonlinear interactions which together determine the global system dynamics, usually measured by different conflicting criteria. The problem that emerges is that of tuning the controllable system parameters at the local level, in order to reach some desirable global behavior. In this research paper, we cast the tuning of an ABM for emergency response planning as a multi-objective optimization problem (MOOP). We then propose the use of multi-objective evolutionary algorithms (MOEAs) for exploration and optimization of the resultant search space. We employ two well-known MOEAs, the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto Archived Evolution Strategy (PAES), and test their performance for different pairs of objectives for plan evaluation. In the experimental results, the approximate Pareto front of the non-dominated solutions is effectively obtained. Further, a conflict between the proposed objectives is patent. Additional robustness analysis is performed to help policymakers select a plan according to higher-level information or criteria not present in the original problem description.

关键词： multi-objective optimization agent-based modeling Pareto front multi-objective evolutionary algorithms robustness disaster management

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Enhancing Deep Vein Thrombosis Diagnosis with multi-objective evolutionary Algorithm and Machine Learning 4

Enhancing Deep Vein Thrombosis Diagnosis with Multi-Objectiv...

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4th International Conference on Applied Artificial Intelligence (ICAPAI)

作者： Sorano, Ruslan Ripon, Kazi Shah Nawaz Magnusson, Lars Vidar Halstensen, Thor-David Ghanima, Waleed Ostfold Univ Coll Dept Comp Sci & Commun Halden Norway Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Ostfold Univ Coll Dept Hlth Welf & Org Halden Norway Ostfold Hosp Trust Dept Res Gralum Norway

ISBN: (纸本)9798350349764;9798350349771

Deep vein thrombosis is a serious medical condition requiring prompt and accurate diagnosis. The identification of thrombosis presents a challenging task characterized by conflicting objectives. Maintaining a delicate balance between optimizing overall diagnostic accuracy and averting the misclassification of ill patients as healthy is paramount in the diagnostic process. Our earlier works focused on optimizing the disease prediction accuracy in machine learning models by experimenting with different techniques. We employed single-objective optimization to fine-tune the classification threshold. Additionally, we applied a multi-objective evolutionary algorithm for hyperparameter optimization, both independently and in combination with feature reduction. Expanding on our previous works, this work employs a multi-objective evolutionary algorithm that concurrently tunes hyperparameters, reduces features, and adjusts the classification threshold. By addressing the inherent conflicting objectives in thrombosis diagnostics, the proposed approach generates a set of Pareto-optimal solutions, representing a balance between maximizing overall diagnostic accuracy and minimizing false negatives. Experimental results indicate that this approach enhances the outcomes of the deep vein thrombosis diagnosis prediction, effectively navigating the trade-off in competing objectives for improved clinical efficacy.

关键词： multi-objective evolutionary algorithms healthcare applications decision support deep vein thrombosis hyperparameter tuning feature reduction classification threshold

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An investigation on noisy environments in evolutionary multi-objective optimization

An investigation on noisy environments in evolutionary multi...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Goh, C. K. Chiam, S. C. Tan, K. C. Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore

ISBN: (纸本)9781424400225

In addition to the need of satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. While studies have shown that many multi-objective evolutionary optimizers are capable of achieving optimization goals, their ability to deal with noise is rarely studied. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multi-objective optimization based upon five benchmark problems characterized by different difficulties in local optimality, non-uniformity, discontinuity and non-convexity. Interestingly, the baseline algorithm employed tends to evolve better solution sets in the presence of low noise levels for some problems. Nevertheless, the evolutionary optimization process degenerates into random search under increasing noise levels.

关键词： multi-objective evolutionary algorithms noise

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Non-dominated multi-objective evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery

Non-dominated Multi-objective Evolutionary Algorithm Based o...

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4th International Workshop on Hybrid Artificial Intelligence Systems

作者： Carmona, C. J. Gonzalez, P. del Jesus, M. J. Herrera, F. Univ Jaen Dept Comp Sci Jaen Spain AI Univ Granada Dept Comp Sci Granada Spain

ISBN: (纸本)9783642023187

A new multi-objective evolutionary model for Subgroup discovery with fuzzy rules is presented in this paper. The method resolves Subgroup discovery problems based on the hybridization between fuzzy logic and genetic algorithms, with the aim of extracting interesting, novel and interpretable fuzzy rules. To do so, the algorithm includes different mechanisms for improving diversity in the population. This proposal focuses on the classification of individuals in fronts, based on non-dominated sort. A study can be seen for the proposal and other previous methods for different databases. In this study good results are obtained for subgroup discovery by this new evolutionary model in comparison with existing algorithms.

关键词： Data mining Subgroup Discovery multi-objective evolutionary algorithms Fuzzy Rules Genetic Fuzzy Systems

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A Study for Parallelization of multi-objective evolutionary Algorithm Based on Decomposition and Directed Mating 2019

A Study for Parallelization of Multi-Objective Evolutionary ...

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3rd International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence (ISMSI)

作者： Miyakawa, Minami Sato, Hiroyuki Sato, Yuji Hosei Univ 3-7-2 Kajino Cho Tokyo 1848584 Japan Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

ISBN: (纸本)9781450372114

This work proposes a parallel multi-objective evolutionary algorithm based on decomposition for solving constrained multi-objective optimization problems. A representative decomposition-based algorithm, MOEA/D, decomposes multi-objective problems into a number of single-objective sub-problem using weight vectors and a scalarizing function. It keeps only the best solution for each sub-problem and neighbor solutions are used to generate offspring. Therefore, to independently execute solution generation in parallel by using multi-core, at least two solutions have to be included in a core. Hence, maximum parallel number of MOEA/D-based parallel algorithm is the population size over 2. However, in proposed parallel algorithm, it can be the population size since it keeps not only the best feasible solution but also an archive population of useful infeasible solutions for each sub-problem. The experimental results using discrete knapsack problems with 2 objectives and {2, 6, 10} constraints show that the proposed parallel algorithm achieves higher search performance by utilizing infeasible solutions even if the number of parallelization is higher than a parallel decomposition-based algorithm.

关键词： multi-objective optimization multi-objective evolutionary algorithms parallelization

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On the Many-objective Pickup and Delivery Problem: Analysis of the Performance of Three evolutionary algorithms 16th

On the Many-Objective Pickup and Delivery Problem: Analysis ...

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16th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Garcia-Najera, Abel Lopez-Jaimes, Antonio Zapotecas-Martinez, Saul Univ Autonoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Av Vasco de Quiroga 4871 Ciudad De Mexico 05300 Mexico

ISBN: (纸本)9783030028374;9783030028367

Many-objective optimization focuses on solving optimization problems with four or more objectives. Effort has been made mainly on studying continuous problems, with interesting results and for which several optimizers have been proposed. Nevertheless, combinatorial problems have not received as much attention, making this an open research area. An important result on continuous problems states that the problem does not necessarily becomes more difficult while more objectives are considered, but, does this result hold for combinatorial problems? This investigation takes this subject on by studying a many-objective combinatorial problem, particularly, the pickup and delivery problem (PDP), which is an important combinatorial optimization problem in the transportation industry and consists in finding a collection of routes with minimum cost. Traditionally, cost has been associated with the number of routes and the total travel distance, however, some other objectives emerge in many applications, for example, travel time, workload imbalance, and uncollected profit. If we consider all these objectives equally important, PDP can be tackled as a many-objective problem. This study is concerned with the study of the performance of three multi-objective evolutionary algorithms on the PDP varying the number of objectives, in order to analyze the change of PDP's difficulty when the number of objectives is increased. Results show that the problem becomes more difficult to solve while more objectives are considered.

关键词： Many-objective optimization Pickup and delivery problem multi-objective evolutionary algorithms Combinatorial optimization

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Machine scheduling in custom furniture industry through neuro-evolutionary hybridization

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APPLIED SOFT COMPUTING 2011年第2期11卷 1600-1613页

作者： Vidal, Juan C. Mucientes, Manuel Bugarin, Alberto Lama, Manuel Univ Santiago de Compostela Dept Elect & Comp Sci Santiago De Compostela 15782 Spain

Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron neural networks. (C) 2010 Elsevier B. V. All rights reserved.

关键词： Machine scheduling optimization multi-objective evolutionary algorithms NSGA-II Neural networks Hybrid approach

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