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12th Ecuadorian Conference on Information and Communication Technologies

作者： Huilcapi, Victor Garcia, Geovanny Ghia, Elias Soto, Brian Univ Politecn Salesiana Guayaquil Ecuador

ISBN: (纸本)9783031754302;9783031754319

This paper proposes a design procedure based on multi-objective optimization for modeling a Peltier thermoelectric system using experimental data. A multi-objective evolutionary algorithm was used to identify a set of optimal parameters that satisfactorily characterize the system's dynamics. The proposed methodology offers a designer valuable information about the dynamics of the temperatures of the Peltier's hot and cold surfaces and the trade-offs between their design objectives (visualized in the Pareto fronts). In this way, a control engineer can be sufficiently informed to choose, according to their preferences, a model for the Peltier cell with the best performance (for different system operation scenarios). A Peltier cold-side temperature model was selected to tune a proportional-integral-derivative (PID) controller and evaluate the robustness of the model. The tuned PID controller works directly on the nonlinear model of the Peltier cell, allowing it to effectively control the temperature of the cold surface of the thermoelectric module. The methodology uses the Integral Absolute Error (IAE) as a performance index to evaluate the quality of system modeling. The results show that the methodological approach applied to model and control the system performs very satisfactorily.

关键词： Peltier cell multi-objective optimization Pareto front multi-objective evolutionary algorithm Nonlinear model PID control

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A stopping criterion for decomposition-based multi-objective evolutionary algorithms

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SOFT COMPUTING 2018年第1期22卷 253-272页

作者： Kadhar, K. Mohaideen Abdul Baskar, S. Dr Mahalingam Coll Engn & Technol ECE Dept Pollachi Tamil Nadu India Thiagarajar Coll Engn EEE Dept Madurai Tamil Nadu India

This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms (MOEA/Ds) to reduce the unnecessary usage of computational resource. In MOEA/D, a multi-objective problem is decomposed into a number of single-objective subproblems using a Tchebycheff decomposition approach. Then, optimal Pareto front (PF) is obtained by optimizing the Tchebycheff objective of all the subproblems. The proposed stopping criterion monitors the variations of Tchebycheff objective at every generation using maximum Tchebycheff objective error (MTOE) of all the subproblems and stops the algorithm, when there is no significant improvement in MTOE. test is used for statistically verifying the significant changes of MTOE for every generations. The proposed stopping criterion is implemented in a recently constrained MOEA/D variant, namely CMOEA/D-CDP, and a simulation study is conducted with the constrained test instances for choosing a suitable tolerance value for the MTOE stopping criterion. A comparison with the recent stopping methods demonstrates that the proposed MTOE stopping criterion is simple and has minimum computational complexity. Moreover, the MTOE stopping criterion is tested on real-world application, namely multi-objective loop shaping PID controller design. Simulation results revealed that the MTOE stopping criterion reduces the unnecessary usage of computational resource significantly when solving the constrained test instances and multi-objective loop shaping PID controller design problems.

关键词： multi-objective evolutionary algorithm Stopping criterion multi-objective optimization H-infinity loop shaping PID controller design MOEA/D Differential evolution

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Last-mile drone delivery combinatorial double auction model using multi-objective evolutionary algorithms

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SOFT COMPUTING 2022年第22期26卷 12355-12384页

作者： Lee, Chen-Wei Wong, Wai-Peng Univ Sains Malaysia Sch Management George Town 11800 Penang Malaysia Monash Univ Sch Informat Technol Malaysia CampusJalan Lagoon Selatan Subang Jaya 47500 Selangor Malaysia

This study proposes a combinatorial double auction bi-objective winner determination problem for last-mile delivery using drone. Prior studies are limited on solving mixed integer model, which are not efficient for large-scale scenario. However, this is not practical in real cases as the computation time to obtain the solution is longer due to number of combinations of packages and participants anticipated in the last-mile delivery platform. Four multi-objective evolutionary algorithms (MOEAs) with the decomposed winner determination problem model are experimented. This study is able to yield Pareto optimal solutions from multiple runs of mixed linear integer programming (MILP) using different objectives weights in the model. Unmanned aerial vehicle, or drone, has potential to reduce cost and save time for last-mile logistic operations. The result positively shows MOEAs are more efficient than MILP in yielding a set of feasible solutions for undertaking complex winner determination problem models. The percentage of improvement in terms of time spent identifying the best option is almost 100%. This is likely an unprecedented research in drone where combinatorial double auction is applied to complex drone delivery services and MOEAs are used to solve the associated winner determination problem model.

关键词： Combinatorial double auction Decomposed problem Drone delivery multi-objective evolutionary algorithm Winner determination problem

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Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design

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APPLIED ENERGY 2016年 164卷 140-151页

作者： Mahbub, Md Shahriar Cozzini, Marco Ostergaard, Poul Alberg Alberti, Fabrizio FBK ARES Via Sommar 18 I-38123 Povo Trento Italy Univ Trento Doctoral Sch ICT Via Sommar 9 I-38123 Povo Trento Italy EURAC Res Inst Renewable Energy Viale Druso 1 I-39100 Bolzano Italy Aalborg Univ Dept Dev & Planning Skibbrogade 5 DK-9000 Aalborg Denmark

Environmental and security concerns urge energy planners to design more sustainable energy systems, reducing fossil fuel consumptions in favour of renewable solutions. The proposed scenarios typically rely on a mixing of different energy sources, thereby mitigating the availability and intermittency problems typically related to renewable technologies. Optimizing this combination is of crucial importance to cope with economic, technical, and environmental issues, which typically give rise to multiple contradictory objectives. To this purpose, this article presents a generalized framework coupling EnergyPLAN - a descriptive analytical model for medium/large-scale energy systems - with a multi-objective evolutionary algorithm - a type of optimizer widely used in the context of complex problems. By using this framework, it is possible to automatically identify a set of Pareto-optimal configurations with respect to different competing objectives. As an example, the method is applied to the case of Aalborg municipality, Denmark, by choosing cost and carbon emission minimization as contrasting goals. Results are compared with a manually identified scenario, taken from previous literature. The automatic approach, while confirming that the available manual solution is very close to optimality, yields an entire set of additional optimal solutions, showing its effectiveness in the simultaneous analysis of a wide range of combinations. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Energy systems optimization multi-objective evolutionary algorithm EnergyPLAN modelling Automatic energy scenario design

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Scheduling flexible manufacturing systems using parallelization of multi-objective evolutionary algorithms

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第3-4期30卷 279-285页

作者： Sankar, S. Saravana Ponnambalam, S. G. Gurumarimuthu, M. Arulmigu Kalasalingam Coll Engn Dept Mech Engn Krishnankoil 626190 India Monash Univ Selangor 46150 Malaysia

Solving multi-objective scientific and engineering problems is, generally, a very difficult goal. In these optimization problems, the objectives often conflict across a high-dimensional problem space and require extensive computational resources. In this paper, a migration model of parallelization is developed for a genetic algorithm (GA) based multi-objective evolutionary algorithm (MOEA). The MOEA generates a near-optimal schedule by simultaneously achieving two contradicting objectives of a flexible manufacturing system (FMS). The parallel implementation of the migration model showed a speedup in computation time and needed less objective function evaluations when compared to a single-population algorithm. So, even for a single-processor computer, implementing the parallel algorithm in a serial manner (pseudo-parallel) delivers better results. Two versions of the migration model are constructed and the performance of two parallel GAs is compared for their effectiveness in bringing genetic diversity and minimizing the total number of functional evaluations.

关键词： multi-objective evolutionary algorithm genetic algorithm parallelization scheduling optimization flexible manufacturing system

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Rule-based credit risk assessment model using multi-objective evolutionary algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 126卷 144-157页

作者： Soui, Makram Gasmi, Ines Smiti, Salima Ghedira, Khaled Higher Inst Management Gabes Tunisia Natl Sch Comp Sci Manouba Tunisia Honoris United Univ Interdisciplinary Res Ctr Tunis Tunisia

Credit risk assessment is considered as one of the vital topics in financial institutions. The existing credit risk evaluation methods are based on black box models or transparent models. The black box models cannot adequately reveal information hidden in the data and the credit risk evaluation remains difficult. In addition, there exist relatively few transparent models that take into consideration interpretability and comprehensibility. To address this problem, we aim to build a reliable credit risk evaluation model which generates a set of classification rules. In fact, we consider the credit risk evaluation as a search-based optimization problem where the goal is to minimize the complexity of the generated solution, to maximize the accuracy, and also to maximize weight which represents rules importance. We conducted a comparative study of four multi-objective evolutionary algorithms in terms of their performance. The obtained results confirm the efficiency of the SMOPSO algorithm regarding generating classification rules for credit risk assessment. The proposed credit risk evaluation model revealed an attractive trade-off between accuracy and comprehensibility. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Credit risk multi-objective evolutionary algorithm Classification rules

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Risk conscious solution of planning problems under uncertainty by hybrid multi-objective evolutionary algorithms

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COMPUTERS & CHEMICAL ENGINEERING 2011年第11期35卷 2521-2539页

作者： Tometzki, Thomas Engell, Sebastian Tech Univ Dortmund Dept Biochem & Chem Engn Proc Dynam & Operat Grp D-44227 Dortmund Germany

We consider the risk conscious solution of planning problems with uncertainties in the problem data. The problems are formulated as two-stage stochastic mixed-integer models in which some of the decisions (first-stage) have to be made under uncertainty and the remaining decisions (second-stage) can be made after the realization of the uncertain parameters. The uncertain model parameters are represented by a finite set of scenarios. The risk conscious optimization problem under uncertainty is solved by a stage decomposition approach using a multi-objective evolutionary algorithm which optimizes the expected scenario costs and the risk criterion with respect to the first-stage decisions. The second-stage scenario decisions are handled by mathematical programming. Results from numerical experiments for two real-world problems are shown. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Planning under uncertainty Risk conscious planning Stage decomposition Hybrid algorithm multi-objective evolutionary algorithm

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A new adaptive decomposition-based evolutionary algorithm for multi- and many-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Bao, Chunteng Gao, Diju Gu, Wei Xu, Lihong Goodman, Erik D. Shanghai Maritime Univ Key Lab Marine Technol & Control Engn Minist Transport Shanghai 201306 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

In decomposition-based multi-objective evolutionary algorithms (MOEAs), a set of uniformly distributed refer-ence vectors (RVs) is usually adopted to decompose a multi-objective optimization problem (MOP) into several single-objective sub-problems, and the RVs are fixed during evolution. When it comes to multi-objective opti-mization problems (MOPs) with complex Pareto fronts (PFs), the effectiveness of the multi-objective evolu-tionary algorithm (MOEA) may degrade. To solve this problem, this article proposes an adaptive decomposition -based evolutionary algorithm (ADEA) for both multi-and many-objective optimization. In ADEA, the candidate solutions themselves are used as RVs, so that the RVs can be automatically adjusted to the shape of the Pareto front (PF). Also, the RVs are successively generated one by one, and once a reference vector (RV) is generated, the corresponding sub-objective space is dynamically decomposed into two sub-spaces. Moreover, a variable metric is proposed and merged with the proposed adaptive decomposition approach to assist the selection operation in evolutionary many-objective optimization (EMO). The effectiveness of ADEA is compared with several state-of-the-art MOEAs on a variety of benchmark MOPs with up to 15 objectives. The empirical results demonstrate that ADEA has competitive performance on most of the MOPs used in this study.

关键词： multi-objective evolutionary algorithm Many-objective optimization Pareto front Adaptive decomposition

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Study on the performance and efficiency of multi-objective evolutionary algorithms for trimaran outrigger layout seakeeping optimization problem

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OCEAN ENGINEERING 2024年第Part2期309卷

作者： Wang, Shangming Duan, Fei Wang, Enhao Shanghai Jiao Tong Univ State Key Lab Ocean Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Ocean & Civil Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Tsinghua Univ Inst Ocean Engn Shenzhen Int Grad Sch Shenzhen 518055 Guangdong Peoples R China

Trimaran as a high-performance vessel, possesses excellent characteristics such as high speed, low wave -making resistance, and good seakeeping performance. Different from conventional monohull ship, the design of a trimaran requires additional consideration of the outrigger layout, as the position of two outriggers can affect its hydrodynamic performance. In this study, high-speed slender body potential flow theory (also named the 2D+t theory or the 2.5D method) was employed to calculate the heave and pitch motion in order to evaluate the seakeeping performance of trimarans with different outrigger layouts at varying speeds. Model experiments were subsequently conducted to validate the effectiveness of the 2.5D method in simulating the motion of trimarans under different outrigger layouts. Three multi -objective evolutionary algorithms, including Non -dominated sorting genetic algorithm II (NSGA-II), Non -dominated sorting genetic algorithm III (NSGA-III), and multi -objective evolutionary algorithm based on decomposition (MOEA/D), as well as their applications in the optimization of trimaran outrigger layouts were introduced. Utilizing the 2.5D method based computational program as the solver, the efficiency and performance of these multi -objective evolutionary algorithms were compared. Discuss the advantages and disadvantages of three multi -objective evolutionary algorithms in solving trimaran outrigger layout optimization problem by comparing the optimization results from two aspects: optimization efficiency, and optimal solution performance. The results indicate that for optimization efficiency, the weighted sum approach based MOEA/D exhibits best optimization efficiency. NSGA-II and NSGA-III show a good advantage in terms of optimal solution performance, and compared to NSGA-II, NSGA-III can obtain more Pareto solutions.

关键词： Trimaran Seakeeping multi-objective optimization multi-objective evolutionary algorithm Model tests

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Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from multi-objective evolutionary algorithms

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 289-305页

作者： Carlos Fernandez, Juan Cruz-Ramirez, Manuel Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg3rd Floor E-14071 Cordoba Spain

This paper proposes a framework to obtain ensembles of classifiers from a multi-objective evolutionary algorithm (MOEA), improving the restrictions imposed by two non-cooperative performance measures for multiclass problems: (1) the Correct Classification Rate or Accuracy (CCR) and, (2) the Minimum Sensitivity (MS) of all classes, i.e., the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. The proposed framework is based on collecting Pareto fronts of Artificial Neural Networks models for multiclass problems by the Memetic Pareto evolutionary NSGA2 (MPENSGA2) algorithm, and it builds a new Pareto front (ensemble) from stored fronts. The ensemble built significantly improves the closeness to the optimum solutions and the diversity of the Pareto front. For verifying it, the performance of the new front obtained has been measured with the habitual use of weighting methodologies, such as Majority Voting, Simple Averaging and Winner Takes All. In addition to CCR and MS measures, three trade-off measures have been used to obtain the goodness of a Pareto front as a whole: Hyperarea, Laumanns's Hyperarea (LAUMANNS) and Zitzler's Spread (M3). The proposed framework can be adapted for any MOEA that aims to improve the compaction and diversity of its Pareto front, and whose fitness functions impose severe restrictions for multiclass problems.

关键词： Ensemble multi-objective evolutionary algorithm multiclass classification Artificial Neural Networks Minimum Sensitivity Pareto Performance measures

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