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IEEE 15th International Conference of System of Systems Engineering (SoSE)

作者： Vanfossan, Samuel Dagli, Cihan H. Kwasa, Benjamin Missouri Univ Sci & Technol Engn Management & Syst Engn Rolla MO 65409 USA

ISBN: (纸本)9781728180502

Sponsored by expedient technologic innovation, consumers frequently expect manufacturer offerings to exhibit extensive product variety and regular product advancement. These expectations have rendered many traditional production practices less applicable. Chiefly impacted is the notion of mass-produced, low-variety artifacts via massive assembly lines. These operations have difficulty meeting the high-customization, short-lifecycle requirements imposed by contemporary demand. Many industries and organizations have begun the transformation from these rigid assembly mechanisms to a more versatile, cellular production strategy known as seru production. To facilitate this transition, methods are needed to aid manufacturers in appropriately selecting and arranging seru system components, a critical step in seru system design. Herein, a generalized model is proposed utilizing a system-of-systems architecting approach to determine the component assembly best suiting the needs of the manufacturing entity. Candidate architectures are generated and evaluated using a multi-objective genetic algorithm from which a preferred alternative is selected through a fuzzy inference system. Directing this genetic algorithm, domain-independent objectives are presented, maintaining applications to most seru production design scenarios. The proposed method is then applied to a camera production example, culminating in the identification of a well-performing architecture. The presented method should find increased use as organizations further adopt this flexible production methodology.

关键词： Seru production system meta-architecture generation multi-objective genetic algorithm fuzzy inference system system-of-systems architecting

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Portfolio management using computational intelligence approaches: Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary algorithms

Portfolio management using computational intelligence approa...

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作者： Prisadarng SKOLPADUNGKET University of Bradford

学位级别：博士

Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of multi-objective genetic algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated genetic algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.

关键词： Portfolio optimisation Realistic constraints multi-objective genetic algorithm estimation error model risk Fuzzy model selection Strength Pareto Evolutionary algorithm 2

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"Load Forecasting on the User‐side by means of Computational Intelligence algorithms"

"Load Forecasting on the User‐side by means of Computationa...

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作者： Juan Jose Cardenas Araujo Universitat Politecnica de Catalunya

学位级别：博士

Nowadays, it would be very difficult to deny the need to prioritize sustainable development through energy efficiency at all consumption levels. In this context, an energy management system (EMS) is a suitable option for continuously improving energy efficiency, particularly on the user side. An EMS is a set of technological tools that manages energy consumption information and allows its analysis. EMS, in combination with information technologies, has given rise to intelligent EMS (iEMS), which, aside from lending support to monitoring and reporting functions as an EMS does, it has the ability to model, forecast, control and diagnose energy consumption in a predictive way. The main objective of an iEMS is to continuously improve energy efficiency (on-line) as automatically as possible. The core of an iEMS is its load modeling forecasting system (LMFS). It takes advantage of historical information on energy consumption and energy-related variables in order to model and forecast load profiles and, if available, generator profiles. These models and forecasts are the main information used for iEMS applications for control and diagnosis. That is why in this thesis we have focused on the study, analysis and development of LMFS on the user side. The fact that the LMFS is applied on the user side to support an iEMS means that specific characteristics are required that in other areas of load forecasting they are not. First of all, the user-side load profiles (LPs) have a higher random behavior than others, as for example, in power system distribution or generation. This makes the modeling and forecasting process more difficult. Second, on the user side --for example an industrial user-- there is a high number and variety of places that can be monitored, modeled and forecasted, as well as their precedence or nature. Thus, on the one hand, an LMFS requires a high degree of autonomy to automatically or autonomously generate the demanded models. And on the other hand, it needs

关键词： Energy efficiency Load profile Load forecasting User side Energy management system Intelligent energy management system Load modeling and forecasting system Adaptability Autonomy Computational intelligence ANFIS ANN genetic algorithm multi-objective genetic algorithm Hilbert-Huang transform Hilbert-spectrum analysis

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Prioritizing Natural Language Test Cases Based on Highly-Used Game Features 2023

Prioritizing Natural Language Test Cases Based on Highly-Use...

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31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Viggiato, Markos Paas, Dale Bezemer, Cor-Paul Univ Alberta Edmonton AB Canada Prodigy Educ Oakville ON Canada

ISBN: (纸本)9798400703270

Software testing is still a manual activity in many industries, such as the gaming industry. But manually executing tests becomes impractical as the system grows and resources are restricted, mainly in a scenario with short release cycles. Test case prioritization is a commonly used technique to optimize the test execution. However, most prioritization approaches do not work for manual test cases as they require source code information or test execution history, which is often not available in a manual testing scenario. In this paper, we propose a prioritization approach for manual test cases written in natural language based on the tested application features (in particular, highly-used application features). Our approach consists of (1) identifying the tested features from natural language test cases (with zero-shot classification techniques) and (2) prioritizing test cases based on the features that they test. We leveraged the NSGA-II genetic algorithm for the multi-objective optimization of the test case ordering to maximize the coverage of highly-used features while minimizing the cumulative execution time. Our findings show that we can successfully identify the application features covered by test cases using an ensemble of pre-trained models with strong zero-shot capabilities (an F-score of 76.1%). Also, our prioritization approaches can find test case orderings that cover highly-used application features early in the test execution while keeping the time required to execute test cases short. QA engineers can use our approach to focus the test execution on test cases that cover features that are relevant to users.

关键词： Test case prioritization multi-objective genetic algorithm Zeroshot classification Feature usage

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multi-objective demand fulfillment problem for solar cell industry 46

Multi-objective demand fulfillment problem for solar cell in...

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46th International Conference on Computers and Industrial Engineering (CIE)

作者： Zheng, Jia-Nian Chien, Chen -Fu Wu, Jei-Zheng Asia Univ Dept Business Adm 500 Lioufeng Rd Taichung 41354 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Soochow Univ Dept Business Adm 56 Sect 1Kuei Yang St Taipei 10048 Taiwan

With the growth of green energy market, there are many companies with vertical integration and outsourcing process. Their business models in the solar cell industry cause different influence on the business objectives including revenue, profit, and gross margin. The sales managers are hard to generate fast and reliable ATP (Available-to-promise) which covered all business objectives. Under the various products types with limited quantity, the loading of factory is uncertain and the quantity of order becomes huger. This study formulates multi-objective demand fulfillment problem in solar cell industry and applies a multi-objective genetic algorithm (moGA) to optimize the ATP. The model trade-offs objectives with by Pareto approach without sacrificing other benefits. The multi-population strategy used for stability of solutions. Results of empirical study show that the proposed moGA can improve gross margin while maintaining the desired revenue and profit. With effective solutions and reasonable computing time, the decision support system with proposed moGA has become a regular communication bridge between sales and production departments in the case company.

关键词： Demand fulfillment Solar cell multi-objective genetic algorithm Decision support system

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On Performance Improvement Based on Restart Meta-Heuristic Implementation for Solving multi-objective Optimization Problems 8th

On Performance Improvement Based on Restart Meta-Heuristic I...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Brester, Christina Ryzhikov, Ivan Semenkin, Eugene Siberian State Aerosp Univ Krasnoyarsk Russia

ISBN: (纸本)9783319618333;9783319618326

One of the possible goals of multi-objective optimization is finding a set of non-dominated solutions or, in other words, a Pareto set approximation. Population-based algorithms, in particular, genetic algorithms, are widely used for this purpose because they deal with a set of alternative solutions, which might be helpful when a number of trade-off points should be obtained. To get a representative approximation, various regions of a search space should be explored. However, during the algorithm execution a search might be stuck in some areas. Therefore, in this article we present a new restart operator for multi-objective genetic algorithms which can prevent a search from stagnating, help to explore new regions and, as a result, improve the algorithm performance significantly. In our proposal we answer the two crucial questions of a restarting concept which are when to restart an algorithm and how to use previously found solutions. We introduce the algorithm independent restart operator, even though in this work we investigate it in combination with a certain MOGA. The experimental results prove the high effectiveness of the modified MOGA with the incorporated restart operator in comparison with the conventional one.

关键词： multi-objective genetic algorithm Restart operator Performance Benchmark problems

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Optimization of frying process for maintaining nutritional quality to satisfy consumers' sensory attributes: A novel application of multi-criteria decision-making approach

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JOURNAL OF multi-CRITERIA DECISION ANALYSIS 2023年第1-2期30卷 44-61页

作者： Sadhu, Tithli Lahiri, Sandip Kumar Roy, Jagannath Bhattacharjee, Ashish Chakrabarty, Jitamanyu Natl Inst Technol Durgapur Dept Chem Durgapur 713209 India SR Univ Sch Agr Dept Biochem Hanumakonda India Natl Inst Technol Durgapur Dept Chem Engn Durgapur West Bengal India Natl Inst Technol Warangal Dept Math Hanumakonda India Natl Inst Technol Durgapur Dept Biotechnol Durgapur India

The objective of the study was to assess the optimum frying condition of fish considering the multiple perspectives (retention of nutritional quality indices [NQI], reduction of preparation time, and improvement of health benefit) to satisfy consumer-preferred sensory attributes by controlling the most impactful process variables (temperature, time, and oil amount). The multi-criteria decision-making (MCDM) approach is appropriate to handle the numerous conflicting criteria and numerous multiple objectives. First, an artificial neural network (ANN) model was developed to build a non-linear correlation between the cooking process parameters and NQI by an automatic exhaustive search of all available algorithms and activation functions. All the NQI are conflicting in nature. Therefore, the ANN-based multi-objective genetic algorithm was implemented to obtain the Pareto optimal solutions to improve all NQI simultaneously. Five optimised conditions were selected amongst the Pareto optimal solutions, satisfying the above-mentioned multiple criteria. Finally, a well-known MCDM approach, the analytical hierarchy process (AHP), was applied for sensory analysis to evaluate the overall acceptance of the optimised conditions based on the relative importance of consumers' general sensory modalities (flavour, colour & appearance, and taste). Furthermore, the following condition (140.01 & DEG;C, 7.62 min, 47.87 ml oil/kg of fish) was selected as the most accepted in terms of all quality attributes that may be implemented as the standard condition in domestic and industrial purposes.

关键词： analytical hierarchy process artificial neural network frying multi-objective genetic algorithm nutritional quality indices sensory evaluation

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multi-objective vehicle delivery path optimization model based on improved constrained evolutionary control operator 2021

Multi-objective vehicle delivery path optimization model bas...

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2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Ma, Tianxing Bai, Pengyu Zhang, Hekai Cent South Univ Changsha Hunan Peoples R China Emory Univ 1380 Oxford Rd NE Atlanta GA 30322 USA Heilongjiang Univ Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781450390200

By designing a constraint control evolutionary operator that considers the infeasible solution, the traditional multi-objective genetic algorithm can not deal with the complex constraints. In this paper, a multi-objective vehicle distribution path optimization model with the shortest path in the form of a vehicle is established to achieve the system's multi-objective optimization. Besides, considering the constraints of vehicle driving distance, the working time constraints of distribution personnel, and the unique constraints, the model is changed into a constraint optimization model. The results show that the computational cost is low, the iterative convergence speed is fast, and the satisfactory solution can be obtained within the acceptable time range.

关键词： Rich-VRP multi-objective genetic algorithm constrained control evolutionary operator

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multi-objective Optimization of a Large-span Wing Analog Beam with Variable Cross-sections

Multi-objective Optimization of a Large-span Wing Analog Bea...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Li, Kui Qiu, Yuanying Sheng, Ying Xidian Univ Key Lab Elect Equipment Struct Minist Educ Xian 710071 Peoples R China

ISBN: (纸本)9783037855010

Aiming at the difficulty with structure and parameter design for a wing analog beam under a large deformation condition, a large-span cantilever truss model with Pro/e is designed. Then a mathematical model of the wing beam based on the multi-objective genetic algorithm is proposed. Thereby the sizing optimization and shape optimization for the wing analog beam are conducted with the finite element analysis and meanwhile two main objectives minimizing the maximum von Mises stress and the mean-square error of the deformation displacement curve fitting are satisfied. A Pareto optimal solution aggregate is obtained after the optimization. Finally an optimal structure is concluded via the multi-objective optimization design, which improves the curve fitting accuracy of the wing and satisfies the stress strength requirement as well.

关键词： Wing analog beam multi-objective genetic algorithm Optimization design Pareto optimal solution aggregate

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Optimization Control of Internal Combustion Engine-Compressed Air Energy Storage Cogeneration System Based on genetic algorithm

Optimization Control of Internal Combustion Engine-Compresse...

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第37届中国控制会议

作者： Xiaopeng Hai Xingguo Wei Ke Li Junjie Gao Chenghui Zhang School of Control Science and Engineering Shandong University

In order to make better use of new energy and improve the utilization rate of energy, much concern have been paid to large-scale energy storage and combined cooling, heating and power（CCHP） technology. This study proposes a novel combined cooling, heating and power（CCHP） system with compressed air energy storage（CAES）. In order to improve the power generation efficiency and operating economy of the system, the multi-objective optimization algorithm based on genetic algorithm is used to control the output power of the two subsystems. The results show that the efficiency of the gas internal combustion engine can be maintained at a high level, while operating cost is low, the output power can be selected according to the actual requirements.

关键词： combined cooling, heating and power compressed air energy storage multi-objective genetic algorithm generation efficiency economic analysis

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