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13th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Chen, Chun-Hao Ye, Chong-You Lee, Yeong-Chyi Hong, Tzung-Pei Natl Taipei Univ Technol Dept Informat & Finance Management Taipei Taiwan Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Cheng Shiu Univ Dept Informat Management Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan

ISBN: (纸本)9783030732790;9783030732806

Trading strategies are usually employed for finding trading signals for increasing returns as well as reducing risks. As a result, many approaches have been proposed for obtaining trading strategy portfolio. The group trading strategy portfolio (GTSP) optimization approaches that can be used to provide various trading strategy portfolios were also proposed. Because different criteria should be considered to derive GTSPs, a MOGA (multi-objective genetic algorithm) based approach has been presented for searching non-dominated solutions. In this paper, to extract a better set of non-dominated solutions, we propose a SPEA-based algorithm for deriving GTSPs with two objective functions. Since the goal of trading is to get profit, the first objective function is utilized to evaluate the return and risk of a candidate GTSP. The second objective function is used to evaluate whether the numbers of strategies between groups are similar and weights of groups as well. Experiments were conducted on a financial dataset to show the effectiveness of the proposed approach and comparison results of the proposed approach and the previous approach.

关键词： Grouping genetic algorithm Group trading strategy portfolio Trading strategy multi-objective problem multi-objective optimization algorithms

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An Improvement of Applying multi-objective optimization Algorithm into Higher Order Mutation Testing 6th

An Improvement of Applying Multi-objective Optimization Algo...

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6th International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA)

作者： Quang-Vu Nguyen Hai-Bang Truong Korea Vietnam Friendship Informat Technol Coll Quangnam Vietnam Vietnam Natl Univ Ho Chi Minh City VNU HCM Univ Informat Technol Fac Comp Sci Ho Chi Minh City Vietnam

ISBN: (纸本)9783030383640;9783030383633

In order to raise the quality of higher order mutation testing, in this paper, we propose an approach for effect improving of multi-objective optimization algorithms which can be used in the field of higher order mutation testing in order to reduce the number of generated mutant, generate the hard-to-kill mutant and construct the quality higher order mutants. We have performed an empirical evaluation with 20 real-word, open-source projects and 10 multi-objective optimization algorithms (including 5 original algorithms and 5 corresponding modification algorithms) to evaluate experimental results as well as bring out some opinions to effectiveness apply multi-objective optimization algorithms into higher order mutation testing. The study results indicate that our approach is an effectiveness one to get better the quality of higher order mutation testing.

关键词： Mutation testing Higher order mutation testing Quality of higher order mutation testing multi-objective optimization algorithms Mutant reduction Quality mutants

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Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第7期29卷 10675-10701页

作者： Ehteram, Mohammad Panahi, Fatemeh Ahmed, Ali Najah Huang, Yuk Feng Kumar, Pavitra Elshafie, Ahmed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Univ Kashan Fac Nat Resources & Earth Sci Kashan Iran Univ Tenaga Nasl UNITEN Coll Engn Inst Energy Infrastruct IEI Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates

Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that

关键词： multi-objective optimization algorithms Evaporation MLP Pareto front

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Green Energy Forecast-Based Bi-objective Scheduling of Tasks Across Distributed Clouds

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2022年第3期7卷 619-630页

作者： Bi, Jing Yuan, Haitao Zhang, Jia Zhou, MengChu Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Southern Methodist Univ Sch Engn Dept Comp Sci Dallas TX 75205 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Current large-scale green cloud data centers (GCDCs) tend to consume a huge amount of energy and generate enormous carbon emissions. Existing studies have tried to solve this problem by either realizing prediction of green energy, or optimizing task scheduling. In contrast, this work seamlessly combines green energy prediction and task scheduling to jointly optimize revenue and energy cost of GCDCs. Specifically, this work designs a prediction method, named Savitzky-Golay and Long Short-Term Memory network (SG-LSTM), to realize noise filtering and forecast green energy. Based on such prediction, a bi-objective optimization method, named Decomposition-based multi-objective evolutionary algorithm with Gaussian mutation and Crowding distance (DMGC), is developed to optimize the revenue and energy cost of GCDCs. Its performance is demonstrated over real-life datasets including Google cluster traces, wind speeds, solar irradiance and prices of electricity. Experimental results show that SG-LSTM outperforms its two peers, back propagation neural network and gated recurrent unit, in terms of root mean square errors and mean absolute errors. In addition, DMGC surpasses its such peers as NSGA-II, SPEA2, and MOEA/D in terms of revenue, energy cost and average execution time. Particularly, DMGC's revenue is 18%, 20% and 13.1% higher, energy cost is 16%, 19.8% and 15.2% lower, and average execution time is 60.02%, 38.47% and 24.17% lower than those of NSGA-II, SPEA2, and MOEA/D, respectively.

关键词： Task analysis Green products Costs optimization Time series analysis Scheduling Cloud computing Green clouds intelligent optimization machine learning task scheduling Savitzky-Golay filter recurrent neural network multi-objective optimization algorithms

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Open-source multi-objective optimization software for menu planning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 252卷

作者： Sahin, Omur Aytekin-Sahin, Gizem Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkiye Nuh Naci Yazgan Univ Dept Nutr & Dietet Kayseri Turkiye

Healthy nutrition is essential for maintaining health and preventing chronic diseases. However, with the increase in dining out, the prevalence of obesity and related chronic diseases is increasing daily. Therefore, to provide adequate and balanced nutrition in public, it is necessary to serve healthy menus following the energy and nutrient requirements of the target group in food service institutions. However, menu planning in food services is a complex process involving several factors. The planned menus should provide sufficient nutrients for the target group and consider factors such as color, consistency, appearance, and a variety of food groups. In addition, existing studies have limited constraints, and the fact that they are not open -source makes it difficult to conduct more comprehensive new studies. Furthermore, the lack of dietitian opinions limits its applicability in practice. Therefore, this study applied four different multi -objective optimization algorithms (AGEMOEA, SMSEMOA, NSGA2, and NSGA3) to solve the menu planning problems. The results were shared with 20 food service dietitians who are experts in the area and were asked to score the menus. In addition, an open -source tool called EvoMeal has been developed. In conclusion, our primary findings showed that AGEMOEA and SMSEMOA were used for the first time in the literature for the menu planning problem, which is a multi -objective problem, and they performed better than both NSGA2 and NSGA3 in general. In addition, expert opinions confirmed that AGEMOEA and SMSEMOA give better results than other algorithms. However, future studies need to conduct a more comprehensive expert evaluation to increase its applicability to the field.

关键词： Menu planning problem multi-objective optimization algorithms AGEMOEA SMSEMOA NSGA2 NSGA3

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multi-objective optimization and the effect of the economic factors on the design of the microgrid hybrid system

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SUSTAINABLE CITIES AND SOCIETY 2021年 65卷

作者： Kharrich, Mohammed Mohammed, Omar Hazem Alshammari, Nahar Akherraz, Mohammed Mohammed V Univ Mohammadia Sch Engineers Dept Elect Engn Ibn Sina St PB 765 Rabat Morocco Northern Tech Univ Dept Tech Power Engn Tech Coll Mosul 41002 Iraq Western Michigan Univ Dept Elect & Comp Engn Kalamazoo MI 49008 USA

The hybrid microgrid system is based principally on renewable energy resources to avoid problems encountered from the use of conventional energy sources. The paper deals with the sizing problem of the hybrid microgrid system that consists of multiple resources, otherwise, a method to compare the multi-objective algorithms is proposed based on the Six Sigma approach. Three multi-objective optimization algorithms, namely MOPSO, PESA II, and SPEA2 are applied to design the hybrid PV/wind/diesel/battery system. Furthermore, three objective functions are considered;the Net Present Cost, the penalty cost of emission and the quantity of the CO2 released into the atmosphere, subject to multiple constraints such as LPSP (system reliability), the availability and renewable fraction. Besides that, this paper evaluated the project feasibility by calculating the LCOE and the dumped energy quantity that will be consumed by a dump load. The results showed that the SPEA2 is the better algorithm using the probabilistic and statistical approach of Six Sigma with a hybrid power system of 24445 $ as NPC. It is observed that the microgrid design which respect the reliability and availability both has more credibility. However, the microgrid designed satisfied all constraints with a competitive cost of energy.

关键词： Isolated hybrid microgrid system Optimal microgrid design Loss of power supply probability Net present cost multi-objective optimization algorithms Six sigma approach

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Application of Evolutionary optimization algorithms for Rehabilitation of Water Distribution Networks

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JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2020年第7期146卷

作者： Elshaboury, Nehal Attia, Tarek Marzouk, Mohamed Housing & Bldg Natl Res Ctr Construct & Project Management Res Inst Construct Engn & Management Giza 1770 Egypt Cairo Univ Construct Engn & Management Struct Engn Dept Fac Engn Giza 12613 Egypt

Deteriorated water distribution networks require significant investments to maximize their functionality. The problem is that limited financial resources are allocated for rehabilitation strategies. This deficiency highlights the importance of developing a tool that helps decision makers develop maintenance and replacement management plans. The optimization tool is employed using two evolutionary algorithms: genetic algorithms and particle swarm optimization. The efficacy of the developed model is demonstrated through its application in a case study of Shaker Al-Bahery, Egypt. Furthermore, evaluation metrics are considered to compare the performance of the aforementioned algorithms. The results reveal that the particle swarm optimization exhibited superior results when compared with the genetic algorithms. Moreover, the following two multicriteria decision-making techniques are used to provide a ranking for the near-optimum solutions: multiobjective optimization on the basis of ratio analysis and technique for order preference by similarity to ideal solution. Finally, the Spearman correlation coefficient is utilized to assess the correlation between rankings obtained from different decision-making methods. The results indicate a very strong relation among the aforementioned techniques.

关键词： Water distribution network Budget allocation multi-objective optimization algorithms Performance metrics multi-criteria decision-making

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Heat dissipation analysis and multi-objective optimization of a permanent magnet synchronous motor using surrogate assisted method

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CASE STUDIES IN THERMAL ENGINEERING 2021年 27卷

作者： Li, Yongsheng Li, Congbo Garg, Akhil Gao, Liang Li, Wei Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Permanent magnet synchronous motors (PMSM) have been substantially used in electric vehicles (EVs) due to their advantages such as low loss, large torque, and high power density. With the continuous improvement of the PMSM performance requirements, its heat dissipation has also attracted increasing attention. This paper proposes a cooling system to realize the heat dissipation of the motor through internal oil circulation and external water circulation. Meanwhile, to obtain the best cooling system parameters, an optimization framework is developed for heat dissipation optimization of the motor. First, the most suitable Latin hypercube sampling (LHS) method is selected for sampling through the Coordinates exchange algorithm. Second, we separately study the modeling accuracy of thirteen surrogate models and finally select the back propagation (BP) neural network model. Then, we use six multi-objective optimization algorithms (MOOAs) to optimize the model, and select the optimal solution via the utopian point method. Finally, the motor heat dissipation situation is effectively improved, and the effectiveness and reliability of the optimization framework are proved, which provides an alternative mean for the heat dissipation design optimization of the motor and has prominent practical significance.

关键词： Permanent magnet synchronous motors Electric vehicles Water oil mixed cooling system optimization framework Surrogate model multi-objective optimization algorithms Utopian point method

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multi-objective CSPS System Considering Energy Consumption

Multi-objective CSPS System Considering Energy Consumption

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Chinese Automation Congress (CAC)

作者： Tan, Qi Xia, Tianlin Tang, Hao Cheng, Mingang Hefei Univ Technol Sch Elect Engn & Automat Hefei Peoples R China Jiangsu Yuanwang Shenzhou Software Ltd Corp Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781728176871

Resource consumption in the production line has become an important consideration. The energy consumption and processing time of the jobs meet the resource consumption function in the production line with variable service rate. This paper applies NSGAII to the conveyor-serviced production station (CSPS) considering energy consumption, and establishes a detailed semi-Markov decision process (SMDP) that optimizes the maximum processing rate and the minimum energy consumption at the same time. This paper proposes a two-population co-evolution algorithm(TPCA), which is applied to the NSGAII algorithm. On this basis, this paper also proposes a divergence strategy(DS) to improve the exploration ability of multi-objective optimization algorithm .The experimental results reveal that the proposed algorithms are more adequate for pareto front exploration and find more solutions for decision makers.

关键词： variable service rate semi-Markor decision process multi-objective optimization algorithms two-population co-evolution divergence strategy

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Bypassing or flying above the obstacles? A novel multi-objective UAV path planning problem

Bypassing or flying above the obstacles? A novel multi-objec...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Golabi, Mahmoud Ghambari, Soheila Lepagnot, Julien Jourdan, Laetitia Brevilliers, Mathieu Idoumghar, Lhassane Univ Haute Alsace IRIMAS UR 7499 F-68100 Mulhouse France Univ Lille Cent Lille CNRS UMR 9189CRIStAL Lille France

ISBN: (纸本)9781728169293

This study proposes a novel multi-objective integer programming model for a collision-free discrete drone path planning problem. Considering the possibility of bypassing obstacles or flying above them, this study aims to minimize the path length, energy consumption, and the accumulated maximum path risk simultaneously. The static environment is represented as 3D grid cells. Due to the NP-hardness nature of the problem, several state-of-the-art evolutionary multi-objective optimization (EMO) algorithms with customized crossover and mutation operators are applied to find a set of non-dominated solutions. The results show the effectiveness of applied algorithms in solving several generated test cases.

关键词： Mathematical modelling UAV Offline path planning multi-objective optimization algorithms

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