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evolutionary multiobjective optimization for Automatic Agent-Based Model Calibration: A Comparative Study

IEEE ACCESS 2021年 9卷 55284-55299页

作者： Moya, Ignacio Chica, Manuel Cordon, Oscar Univ Granada Andalusian Res Inst DaSCI Data Sci & Computat Int Granada 18071 Spain Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

Complex problems can be analyzed by using model simulation but its use is not straight-forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. We propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent-based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e., the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.

关键词： Calibration Computational modeling Analytical models optimization methods Adaptation models Benchmark testing Predictive models Model calibration agent-based modeling evolutionary multiobjective optimization

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evolutionary multiobjective optimization of Topological Active Nets

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PATTERN RECOGNITION LETTERS 2010年第13期31卷 1781-1794页

作者： Novo, J. Penedo, M. G. Santos, J. Univ A Coruna Dept Comp Sci La Coruna 15071 Spain

In this work we used the evolutionary multiobjective optimization methodology for the optimization of Topological Active Nets. This is a deformable model that integrates features of region-based and boundary-based segmentation techniques. The model deformation is controlled by energy functions that must be minimized. When the minimization task is performed by means of a greedy local search or a global search method, an experimental tuning of the energy parameters is needed to obtain a correct segmentation. This tuning must be done for each kind of image. evolutionary multiobjective optimization gives a solution to this problem by considering the optimization of several objectives in parallel. We used the SPEA2 algorithm, adapted to our application, to the search of the Pareto optimal individuals. We tested the improvements and problems between the uses of the multiobjective optimization technique versus the use of a genetic algorithm and a greedy local search in our application of the optimization of the Topological Active Nets deformable model. We used several representative examples with images from different medical domains. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Deformable segmentation models Topological Active Nets Genetic algorithms evolutionary multiobjective optimization

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evolutionary multiobjective optimization algorithm for multimedia delivery in critical applications through Content-Aware Networks

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JOURNAL OF SUPERCOMPUTING 2017年第3期73卷 993-1016页

作者： Batalla, Jordi Mongay Mavromoustakis, Constandinos X. Mastorakis, George Negru, Daniel Borcoci, Eugen Seidor SA Barcelona Spain Natl Inst Telecommun Warsaw Poland Univ Nicosia Nicosia Cyprus Technol Educ Inst Crete Iraklion Greece Univ Bordeaux CNRS LaBRI Talence France Univ Politehn Bucuresti Bucharest Romania

Critical applications which need to deliver multimedia through the Internet, may achieve the required quality of service thanks to the Content-Aware Networks (CAN). The key element of CAN is an efficient decision algorithm responsible for the selection of the best content source and routing paths for content delivery. This paper proposes a two-phase decision algorithm, exploiting the evolutionary multiobjective optimization (EMO) approach. It allows to consider valid information in different time scales, adapting decision-maker to the evolving network and server conditions as well as to get the optimal solution in different shapes of Pareto front. The simulation experiments performed in a large-scale network model, confirm the effectiveness of the proposed two-phase EMO algorithm, comparing to other multi-criteria decision algorithms used in CAN.

关键词： Content-Aware Networks Critical applications evolutionary multiobjective optimization Multi-path multi-source Content server selection

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evolutionary multiobjective optimization for the Pickup and Delivery Problem with Time Windows and Demands

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MOBILE NETWORKS & APPLICATIONS 2016年第1期21卷 175-190页

作者： Phan, Dung H. Suzuki, Junichi Univ Massachusetts Dept Comp Sci Boston MA 02125 USA 100 Morrissey Blvd Boston MA 02125 USA

This paper studies an evolutionary algorithm to solve a new multiobjective optimization problem, the Pickup and Delivery Problem with Time Windows and Demands (PDP-TW-D), which is applicable to operational optimization in various mobile network systems. With respect to multiple optimization objectives, PDP-TW-D is to find a set of Pareto-optimal routes for a fleet of vehicles (e.g., mobile robots, drones and autonomous heavy-haulage trucks) in order to serve given transportation requests. The proposed algorithm uses a population of individuals, each of which represents a solution candidate, and evolves them through generations to seek the Pareto-optimal solutions. In addition to the evolution-based global search process, the proposed algorithm allows individuals to improve their optimality in each generation with a local search process, which is designed based on iterative neighborhood search. Experimental results demonstrate that the integration of global and local search processes improves the optimality of individuals and expedites convergence speed. The proposed algorithm outperforms two well-known existing EMOAs, NSGA-II and MOEA/D, in relatively large-scale problems that have up to 400 pickup and delivery locations.

关键词： evolutionary multiobjective optimization Genetic algorithms Pickup and delivery problem with time windows and demands

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evolutionary multiobjective optimization in Materials Science and Engineering

Evolutionary Multiobjective Optimization in Materials Scienc...

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2nd International Conference on Neural Network and Genetic Algorithms in Materials Science and Engineering

作者： Coello Coello, Carlos A. Landa Becerra, Ricardo IPN CINVESTAV Dept Comp Evolutionary Computat Grp Mexico City 07360 DF Mexico

This article provides a short introduction to the evolutionary multiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary algorithms that have been developed, from a historical perspective. In the second part of the article, some representative applications within materials science and engineering are reviewed. In the final part of the article, some potential areas for future research in this area are briefly described.

关键词： Alloys design Applications of evolutionary algorithms Casting of metals Cold rolling evolutionary algorithms evolutionary multiobjective optimization Heat treatment Hot rolling Iron-making Metaheuristics multiobjective evolutionary algorithms multiobjective optimization Polymerization Reheating Steel-making

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evolutionary multiobjective optimization of steel structural systems in tall buildings

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4th International Conference on evolutionary Multi-Criterion optimization (EMO 2007)

作者： Kicinger, Rafal Obayashi, Shigeru Arciszewski, Tomasz George Mason Univ Volgenau Sch Informat Technol & Engn 4400 Univ Dr MS 6C1 Fairfax VA 22030 USA Tohoku Univ Inst Fluid Sci Aoba Ku Sendai 9808577 Japan

ISBN: (纸本)9783540709275

This paper presents results of extensive computational experiments in which evolutionary multiobjective algorithms were used to find Pareto-optimal solutions to a complex structural design problem. In particular, Strength-Pareto evolutionary Algorithm 2 (SPEA2) was combined with a mathematical programming method to find optimal designs of steel structural systems in tall buildings with respect to two objectives (both minimized): the total weight and the maximum horizontal displacement of a tall building. SPEA2 was employed to determine Pareto-optimal topologies of structural members (topology optimization) whose cross-sections were subsequently optimized by the mathematical programming method (sizing optimization). The paper also presents the shape of the Pareto front in this two-dimensional objective space and discusses its dependence on the building's aspect ratio. The results reported provide both qualitative and quantitative knowledge regarding the relationship between the two objectives. They also show the trade-offs involved in the process of conceptual and detailed design of complex structural systems in tall buildings.

关键词： evolutionary multiobjective optimization structural design Pareto front tall buildings

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evolutionary multiobjective optimization for Selecting Members of an Ensemble Streamflow Forecasting Model 13

Evolutionary Multiobjective Optimization for Selecting Membe...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Brochero, Darwin Gagne, Christian Anctil, Francois Univ Laval Chaire Rech EDS Previs & Act Hydrol Dep Genie Civil & Genie Eaux Quebec City PQ G1V 0A6 Canada Univ Laval Dept Gen Elect & Gen Informat Lab Vis & Syst Numeriques Quebec City PQ G1V 0A6 Canada

ISBN: (纸本)9781450319638

We are proposing to use the Nondominated Sorting Genetic Algorithm II (NSGA-II) for optimizing a hydrological forecasting model of 800 simultaneous streamflow predictors. The optimization is based on the selection of the best 48 predictors from the 800 that jointly define the "best" ensemble in terms of two probabilistic criteria. Results showed that the difficulties in simplifying the ensembles mainly originate from the preservation of the system reliability. We conclude that Pareto fronts generated with NSGA-II allow the development of a decision process based explicitly on the trade-off between different probabilistic properties. In other words, evolutionary multiobjective optimization offers more flexibility to the operational hydrologists than a priori methods that produce only one selection.

关键词： evolutionary multiobjective optimization Probabilistic forecasting Streamflow forecasting Hydrological ensemble prediction system Uncertainty cascade model

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evolutionary multiobjective optimization with a segment-based external memory support for the multiobjective quadratic assignment problem

Evolutionary multiobjective optimization with a segment-base...

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IEEE Congress on evolutionary Computation

作者： Acan, A Ünveren, A Eastern Mediterranean Univ Dept Comp Engn TRNC Gazimagusa Turkey

ISBN: (纸本)0780393635

multiobjective evolutionary optimization has been demonstrated to be an efficient method for some difficult multiobjective optimization problems;particularly the quadratic assignment problem which is a provably difficult NP-complete problem with a multitude of real-world applications. This paper introduces the use of a segment-based external memory in evolutionary multiobjective optimization. In principle, variable-size solution segments taken from a number of previously promising solutions are stored in an external memory whose elements are used in the construction of new solutions. In the construction of a solution, a solution segment is retrieved from the external memory and used in the construction of complete solutions through evolutionary recombination operators. The aim is to provide further intensification around promising solutions without weakening the exploration capabilities. Different instances of the multiobjective quadratic assignment problem are used for performance evaluations and, almost in all trials, the proposed external memory strategy provided significantly better results than the multiobjective genetic algorithm (MOGA).

关键词： computational complexity evolutionary computation optimisation evolutionary multiobjective optimization evolutionary recombination operators multiobjective genetic algorithm multiobjective quadratic assignment problem segment based external memory variab

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evolutionary multiobjective optimization on a chip

Evolutionary multiobjective optimization on a chip

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IEEE Workshop on Evolvable and Adaptive Hardware

作者： Bonissone, Stefano Subbu, Raj Gen Elect Global Res 1 Res Circle Niskayuna NY 12309 USA

ISBN: (纸本)9781424406999

The majority of research in Evolvable Hardware is focused on evolving logic for deployment on reconfigurable hardware. There are far fewer reports concerned with the implementation of evolutionary Algorithms (EAs) in hardware. The focus of our research is directed toward using reconfigurable hardware as a means to speed up evolutionary search, and in particular evolutionary multiobjective optimization (EMO). evolutionary multiobjective optimization utilizes an evolutionary search to find solutions to difficult multiobjective optimization problems. We present an implementation of an EMO algorithm in reconfigurable hardware, and discuss how it may be utilized in practical deployment situations.

关键词： evolutionary multiobjective optimization Evolvable hardware Field programmable gate array Neural networks

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evolutionary multiobjective optimization to target social network influentials in viral marketing

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 147卷 113183-113183页

作者： Francisco Robles, Juan Chica, Manuel Cordon, Oscar Univ Granada Andalusian Res Inst DaSCI Data Sci & Computat Int E-18071 Granada Spain Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

Marketers have an important asset if they effectively target social networks' influentials. They can advertise products or services with free items or discounts to spread positive opinions to other consumers (i.e., word-of-mouth). However, main research on choosing the best influentials to target is single-objective and mainly focused on maximizing sales revenue. In this paper we propose a multiobjective approach to the influence maximization problem with the aim of increasing the revenue of viral marketing campaigns while reducing the costs. By using local social network metrics to locate influentials, we apply two evolutionary multiobjective optimization algorithms, NSGA-II and MOEA/D, a multiobjective adaptation of a single-objective genetic algorithm, and a greedy algorithm. Our proposal uses a realistic agent-based market framework to evaluate the fitness of the chromosomes by simulating the viral campaigns. The framework also generates, in a single run, a set of non-dominated solutions that allows marketers to consider multiple targeting options . The algorithms are evaluated on five network topologies and a real data-generated social network, showing that both MOEA/D and NSGA-II outperform the single-objective and the greedy approaches. More interestingly, we show a clear correlation between the algorithms' performance and the diffusion features of the social networks. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Viral marketing Influence maximization Influentials targeting Social networks evolutionary multiobjective optimization Agent-based modeling

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