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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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A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks

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evolutionary INTELLIGENCE 2010年第3-4期3卷 137-153页

作者： Marcelloni, Francesco Vecchio, Massimo Univ Pisa Dipartimento Ingn Informaz Via Diotisalvi 2 I-56122 Pisa Italy Univ Vigo Signal Theory & Commun Dept Vigo Spain

Since tiny nodes of a wireless sensor network (WSN) are typically powered by batteries with, due to miniaturization and costs, a limited capacity, with the aim of extending the lifetime of WSNs and making the exploitation of WSNs appealing, a lot of research has been devoted to save energy. Although a number of factors contribute to power consumption, radio communication has been generally considered its main cause and thus most of the techniques proposed for energy saving have mainly focused on limiting transmission/reception of data, for instance, through data compression. As sensor nodes are equipped with limited computational and storage resources, enabling compression requires to develop purposely-designed algorithms. To this aim, we propose an approach to generate lossy compressors to be deployed on single nodes based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. The quantization levels and thresholds, which allow achieving different trade-offs between compression performance and information loss, are determined by a two-objective evolutionary algorithm. We tested our approach on four datasets collected by real WSN deployments. We show that the lossy compressors generated by our approach can achieve significant compression ratios despite negligible reconstruction errors and outperform LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes.

关键词： Wireless sensor networks Data compression multi-objective evolutionary algorithms Energy efficiency Signal processing

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EXPLORING URBAN CONFIGURATIONS FOR A WALKABLE NEW TOWN USING evolutionary algorithms

EXPLORING URBAN CONFIGURATIONS FOR A WALKABLE NEW TOWN USING...

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Proceedings of the 19th International Conference on Computer-Aided Architectural Design Research in Asia CAADRIA

作者： Chian, Eugene Janssen, Patrick Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9789881902658

multi-objective evolutionary algorithms have been successfully applied within various design domains in order to explore the trade-offs between conflicting design criteria. This research investigates how evolutionary algorithms can be used to develop urban configurations for walkable new towns, focusing in particular on the trade-off between travelling time using public transport and accessibility to open space. A population of optimised urban configurations was evolved and analysed, resulting in the identification of three differing typologies for walkable new towns.

关键词： Urban structure transportation network urban density multi-objective evolutionary algorithms

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multi-objective analysis of a component-based representation within an interactive evolutionary design system

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ENGINEERING OPTIMIZATION 2007年第5期39卷 591-613页

作者： Machwe, A. T. Parmee, I. C. Univ W England ACDDM Grp Fac Comp Engn & Math Sci Bristol BS16 1QY Avon England

This article describes research relating to a user-centered evolutionary design system that evaluates both engineering and aesthetic aspects of design solutions during early-stage conceptual design. The experimental system comprises several components relating to user interaction, problem representation, evolutionary search and exploration and online learning. The main focus of the article is the evolutionary aspect of the system when using a single quantitative objective function plus subjective judgment of the user. Additionally, the manner in which the user-interaction aspect affects system output is assessed by comparing Pareto frontiers generated with and without user interaction via a multi-objective evolutionary algorithm (MOEA). A solution clustering component is also introduced and it is shown how this can improve the level of support to the designer when dealing with a complex design problem involving multiple objectives. Supporting results are from the application of the system to the design of urban furniture which, in this case, largely relates to seating design.

关键词： multi-objective evolutionary algorithms interactive evolutionary design systems representation clustering

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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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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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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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Energy and Spectral Efficiency Tradeoff in NOMA: multi-objective evolutionary Approaches

Energy and Spectral Efficiency Tradeoff in NOMA: Multi-Objec...

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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Cetinkaya, Sinasi Arslan, Huseyin Univ S Florida Dept Elect Engn Tampa FL 33620 USA Istanbul Medipol Univ Dept Elect & Elect Engn TR-34810 Istanbul Turkey

ISBN: (纸本)9781728174402

Non-orthogonal multiple access (NOMA) deployment in future wireless networks has been recently considered a promising radio access technology to enhance spectral efficiency (SE). However, gain in SE comes always with the cost of energy efficiency (EE). In this paper, we investigate the SE and EE tradeoff in downlink NOMA with the consideration of quality of service (QoS) requirements based on three population-based multi-objective evolutionary algorithms (MOEAs): multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-2 (SPEA2). The tradeoff is optimized and Pareto optimal solutions are obtained through MOEAs. The effectiveness of the algorithms is evaluated based on the hypervolume metric and the capability of solving multi-objective optimization problems. Simulation results reveal that SPEA2 outperforms NSGA-II and MOPSO. Furthermore, NSGA-II is the loser among all algorithms in terms of finding Pareto optimal results.

关键词： Energy efficiency multi-objective evolutionary algorithms non-orthogonal multiple access (NOMA) power allocation spectral efficiency

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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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