A Natural Formalism and a MultiAgent Algorithm for Integrative Multidisciplinary Design Optimization

International audienceMultiDisciplinary Optimization (MDO) problems represent one of the hardest and broadest domains of continuous optimization. By involving both the models and criteria of different disciplines, MDO problems are often too complex to be tackled by classical optimization methods. We propose an approach which takes into account this complexity using a new representation (NDMO - Natural Domain Modeling for Optimization) and a self-adaptive multi-agent algorithm. Our method agentifies the different elements of the problem (such as the variables, the models, the objectives). Each agent is in charge of a small part of the problem and cooperates with others to find equilibrium on conflicting values. Despite the fact that no agent of the system has a complete view of the entire problem, the mechanisms we provide allow the emergence of a coherent solution. Evaluations on several academic and industrial test cases are provided

A novel approach to MDO using an adaptive multi-agent system

International audienceMultiDisciplinary Optimization (MDO) problems represent one of the hardest and broadest domains of continuous optimization, often too complex to be tackled by classical optimization methods. We propose an original approach for taking into account this complexity using a self-adaptive multi-agent system where each elements of the problem become an agent in charge of a small part of the problem

Agent-Based Natural Domain Modeling for Cooperative Continuous Optimization

International audienceWhile multi-agent systems have been successfully applied to combinatorial optimization, very few works concern their applicability to continuous optimization problems. In this article we propose a framework for modeling a continuous optimization problems as multi-agent system,which we call NDMO, by representing the problem as an agent graph, and complemented with optimization solving behaviors. Some of the results we obtained with our implementation on several continuous optimization problems are presented

The Application of Memetic Algorithms for Forearm Crutch Design: A Case Study

Product design has normally been performed by teams, each with expertise in a specific discipline such as material, structural, and electrical systems. Traditionally, each team would use its member\u27s experience and knowledge to develop the design sequentially. Collaborative design decisions explore the use of optimization methods to solve the design problem incorporating a number of disciplines simultaneously. It is known that such optimized product design is superior to the design found by optimizing each discipline sequentially due to the fact that it enables the exploitation of the interactions between the disciplines. In this paper, a bi-level decentralized framework based on Memetic Algorithm (MA) is proposed for collaborative design decision making using forearm crutch as the case. Two major decisions are considered: the weight and the strength. We introduce two design agents for each of the decisions. At the system level, one additional agent termed facilitator agent is created. Its main function is to locate the optimal solution for the system objective function which is derived from the Pareto concept. Thus to Pareto optimum for both weight and strength is obtained. It is demonstrated that the proposed model can converge to Pareto solutions

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller

[EN] Power converters are electronic devices widely applied in industry, and in recent years, for renewable energy electronic systems, they can regulate voltage levels and actuate as interfaces, however, to do so, is needed a controller. Proportional-Integral-Derivative (PID) are applied to power converters comparing output voltage versus a reference voltage to reduce and anticipate error. Using PID controllers may be complicated since must be previously tuned prior to their use. Many methods for PID controllers tunning have been proposed, from classical to metaheuristic approaches. Between the metaheuristic approaches, bio-inspired algorithms are a feasible solution; Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are often used; however, they need many initial parameters to be specified, this can lead to local solutions, and not necessarily the global optimum. In recent years, new generation metaheuristic algorithms with fewer initial parameters had been proposed. The Grey Wolf Optimizer (GWO) algorithm is based on wolves¿ herds chasing habits. In this work, a comparison between PID controllers tunning using GWO, PSO, and GA algorithms for a Boost Converter is made. The converter is modeled by state-space equations, and then the optimization of the related PID controller is made using MATLAB/Simulink software. The algorithm¿s performance is evaluated using the Root Mean Squared Error (RMSE). Results show that the proposed GWO algorithm is a feasible solution for the PID controller tunning problem for power converters since its overall performance is better than the obtained by the PSO and GA.The authors wish to thank the Institute of Energy Engineering of the Polytechnic University of Valencia, Spain, and the Department of Water and Energy Studies of the University of Guadalajara, Mexico, for all their support and collaboration.Águila-León, J.; Chiñas-Palacios, C.; Vargas-Salgado Carlos; Hurtado-Perez, E.; García, EXM. (2021). Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller. Advances in Science, Technology and Engineering Systems Journal. 6(1):619-625. https://doi.org/10.25046/aj060167S61962561J. Aguila-Leon, C.D. Chinas-Palacios, C. Vargas-Salgado, E. Hurtado-Perez, E.X.M. Garcia, "Optimal PID Parameters Tunning for a DC-DC Boost Converter: A Performance Comparative Using Grey Wolf Optimizer, Particle Swarm Optimization and Genetic Algorithms," in 2020 IEEE Conference on Technologies for Sustainability, SusTech 2020, 2020, doi:10.1109/SusTech47890.2020.9150507.H. Sira-Ramírez, R. Silva-Ortigoza, Control Design Techniques in Power Electronic Devices, 2013, doi:10.1017/CBO9781107415324.004.G.A. Raiker, S.R. B, P.C. Ramamurthy, L. Umanand, S.G. Abines, S.G. Vasisht, "Solar PV interface to Grid-Tie Inverter with Current Referenced Boost Converter," in 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), IEEE: 343-348, 2018, doi:10.1109/ICIINFS.2018.8721313.S.E. Babaa, G. El Murr, F. Mohamed, S. Pamuri, "Overview of Boost Converters for Photovoltaic Systems," Journal of Power and Energy Engineering, 06(04), 16-31, 2018, doi:10.4236/jpee.2018.64002.J. Berner, K. Soltesz, T. Hägglund, K.J. Åström, "An experimental comparison of PID autotuners," Control Engineering Practice, 73, 124-133, 2018, doi:10.1016/J.CONENGPRAC.2018.01.006.K. Ogata, Modern Control Engineering, 5th ed., Prentice Hall, 2010.K. Nisi, B. Nagaraj, A. Agalya, "Tuning of a PID controller using evolutionary multi objective optimization methodologies and application to the pulp and paper industry," International Journal of Machine Learning and Cybernetics, 10(8), 2015-2025, 2019, doi:10.1007/s13042-018-0831-8.M.T. Özdemir, D. Öztürk, "Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control," Energies, 10(12), 2017, doi:10.3390/en10122134.G.-Q. Zeng, X.-Q. Xie, M.-R. Chen, "An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations," Energies, 10(11), 1840, 2017, doi:10.3390/en10111840.Y. Sawle, S.C. Gupta, A.K. Bohre, "Optimal sizing of standalone PV/Wind/Biomass hybrid energy system using GA and PSO optimization technique," Energy Procedia, 117, 690-698, 2017, doi:10.1016/j.egypro.2017.05.183.S. Surender Reddy, C. Srinivasa Rathnam, "Optimal Power Flow using Glowworm Swarm Optimization," International Journal of Electrical Power & Energy Systems, 80, 128-139, 2016, doi:10.1016/J.IJEPES.2016.01.036.C.Y. Acevedo-arenas, A. Correcher, C. Sánchez-díaz, E. Ariza, D. Alfonso-solar, C. Vargas-salgado, J.F. Petit-suárez, "MPC for optimal dispatch of an AC-linked hybrid PV / wind / biomass / H2 system incorporating demand response," Energy Conversion and Management, 186(February), 241-257, 2019, doi:10.1016/j.enconman.2019.02.044.M. Çelebi, "Efficiency optimization of a conventional boost DC/DC converter," Electrical Engineering, 100(2), 803-809, 2018, doi:10.1007/s00202-017-0552-0.Q.Y. Lu, W. Hu, L. Zheng, Y. Min, M. Li, X.P. Li, W.C. Ge, Z.M. Wang, "Integrated coordinated optimization control of automatic generation control and automatic voltage control in regional power grids," Energies, 5(10), 3817-3834, 2012, doi:10.3390/en5103817.J. Aguila‐Leon, C. Chiñas‐Palacios, E.X.M. Garcia, C. Vargas‐Salgado, "A multimicrogrid energy management model implementing an evolutionary game‐theoretic approach," International Transactions on Electrical Energy Systems, 30(11), 2020, doi:10.1002/2050-7038.12617.Ovat Friday Aje, Anyandi Adie Josephat, "The particle swarm optimization (PSO) algorithm application - A review," Global Journal of Engineering and Technology Advances, 3(3), 001-006, 2020, doi:10.30574/gjeta.2020.3.3.0033.N.K. Jain, U. Nangia, J. Jain, A Review of Particle Swarm Optimization, Journal of The Institution of Engineers (India): Series B, 99(4), 407-411, 2018, doi:10.1007/s40031-018-0323-y.B. Hekimoǧlu, S. Ekinci, S. Kaya, "Optimal PID Controller Design of DC-DC Buck Converter using Whale Optimization Algorithm," in 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018, Institute of Electrical and Electronics Engineers Inc., 2019, doi:10.1109/IDAP.2018.8620833.S. Mirjalili, S.M. Mirjalili, A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, 69, 46-61, 2014, doi:10.1016/j.advengsoft.2013.12.007.S.-X. Li, J.-S. Wang, "Dynamic Modeling of Steam Condenser and Design of PI Controller Based on Grey Wolf Optimizer," Mathematical Problems in Engineering, 2015, 1-9, 2015, doi:10.1155/2015/120975.S. Yadav, S.K. Verma, S.K. Nagar, "Optimized PID Controller for Magnetic Levitation System," IFAC-PapersOnLine, 49(1), 778-782, 2016, doi:10.1016/J.IFACOL.2016.03.151.R.H.G. Tan, L.Y.H. Hoo, "DC-DC converter modeling and simulation using state space approach," 2015 IEEE Conference on Energy Conversion (CENCON), (2), 42-47, 2015, doi:10.1109/CENCON.2015.7409511

Optimization of Agro-Socio-Hydrological Networks under Water Scarcity Conditions: Inter- and Trans-disciplinary Approaches for Sustainable Water Resources Management

Sustainable agriculture is one of the greatest challenges of our time. The pathways to sustainable agriculture consist of successive decisions for optimization that are often a matter of negotiation as resources are shared at all levels. This work essentially comprises three research projects with novel inter- and transdisciplinary methods to better understand and optimize agricultural water management under water scarcity conditions. In the first project, climate variability in the US Corn Belt was analyzed with a focus on deficit irrigation to find the optimal irrigation strategies for possible future changes. Two optimization methods for deficit irrigation showed positive water savings and yield increases in the predicted water scarcity scenarios. In the second project, a serious board game was developed and game sessions were carried out to simulate the complex decision space of actors in irrigated agriculture under climate and groundwater variability. The aim of the game was to understand how decisions are made by actors by observing the course of the game and linking these results to common behavioral theories implemented in socio-ecological models. In the third project, two frameworks based on innovation theories and agro-social-hydrological networks were developed and tested using agent-based models. In the first framework, centralized and decentralized irrigation management in Kansas US was compared to observe the development of collective action and the innovation diffusion of sustainable irrigation strategies. The second framework analyzed different decision processes to perform a sensitivity analysis of innovation implementation, groundwater abstraction and saline water intrusion in the Al Batinah region in Oman. Both frameworks allowed the evaluation of diverse behavior theories and decision-making parameters to find the optimal irrigation management and the impact of diverse socio-ecological policies. Inter- and Trans-disciplinary simulations of the interactions between human decisions and water systems, like the ones presented in here, improve the understanding of irrigation systems as anthropogenic landscapes in socio-economic and ecological contexts. The joint application of statistical and participatory approaches enables different but complementary perspectives that allow for a multidimensional analysis of irrigation strategies and water resources management.:Contents Declaration of Independent Work i Declaration of Conformity iii List of Publications v Acknowledgments ix Abstract xi Zusammenfassung xiii Contents xv List of Figures xvii List of Tables xix List of Abbreviations xxi 1. Introduction 3 1.1 Complex Networks Approach 3 1.2 Research Objectives 4 1.3 Thesis Outline 5 2. Literature Review 9 2.1 Agro-Hydrological Systems 9 2.1.1 Necessary Disciplinary Convergence 9 2.1.2 Multi-Objective Optimization Approaches 10 2.2 Optimization of Crop-Water Productivity 11 2.2.1 Irrigation Strategies 11 2.3 Sustainable Management of A-S-H Networks 12 2.3.1 Socio-Hydrology 13 2.3.2 Representation of Decision-Making Processes 14 2.3.3 Influence of Social Network 16 2.4 Socio-Hydrological Modeling Approaches 17 2.4.1 Game Theory Approach 17 2.4.2 Agent-Based Modeling 18 2.4.3 Participatory Modeling 20 2.5 Education for Sustainability 21 2.5.1 Experiential Learning 21 2.5.2 Serious Games 22 2.6 Summary of Research Gaps 24 3. Irrigation Optimization in The US Corn Belt 27 3.1 Agriculture in The Corn Belt 27 3.2 Historical and Prospective Climatic Variability 29 3.3 Simulated Irrigation Strategies 29 3.4 Optimal Irrigation Strategies Throughout the Corn Belt 30 3.5 Summary 31 4. Participatory Analysis of A-S-H Dynamics 35 4.1 Decision-Making Processes in A-S-H Networks 36 4.1.1 Collaborative and Participatory Data Collection Approaches 37 4.2 MAHIZ 38 4.2.1 Serious Game Development 38 4.2.2 Implementation of Serious Game Sessions 39 4.4 Evaluation of The Learning Process in Serious Games 40 4.5 Evaluation of Behavior Theories and Social Parameters 42 4.6 Summary 43 5 Robust Evaluation of Decision-Making Processes In A-S-H Networks 47 5.1 Innovation in A-S-H Networks 47 5.1.1 Multilevel Social Networks 48 5.1.2 Theoretical Framework of Developed ABMs 49 5.2 DInKA Model: Irrigation Expansion in Kansas, US 50 5.2.1 Robust Analysis of Innovation Diffusion 53 5.3 SAHIO Implementation: Coastal Agriculture in Oman 54 5.3.1 SAHIO Sensitivity analysis 58 5.4 Summary 60 6 Conclusions and Outlook 63 6.1 Limitations 64 6.2 Outlook 64 Bibliography 69 Appendix A. Implementation Code 79 A.1 DInKA 79 A.2 SAHIO 82 Appendix B. SAHIO’s Decision-Making Process for Each MoHuB Theory 91 Appendix C. SAHIO A-S-H Innovation Results 97 Appendix D. Selected Publications 101 D.1 Evaluation of Hydroclimatic Variability and Prospective Irrigation Strategies in the U.S. Corn Belt. 103 D.2 A Serious Board Game to Analyze Socio-Ecological Dynamics towards Collaboration in Agriculture. 121 D.2.1 MAHIZ Rulebook 140 D.2.2 MAHIZ Feedback Form 15

An adaptive multi-agent system for self-organizing continuous optimization

Cette thèse présente une nouvelle approche pour la distribution de processus d'optimisation continue dans un réseau d'agents coopératifs. Dans le but de résoudre de tels problèmes, le domaine de l'optimisation multidisciplinaire a été proposé. Les méthodes d'optimisation multidisciplinaire proposent de distribuer le processus d'optimisation, généralement en reformulant le problème original d'une manière qui réduit les interconnexions entre les disciplines. Cependant, ces méthodes présentent des désavantages en ce qui concerne la difficulté de les appliquer correctement, ainsi que leur manque de flexibilité. En se basant sur la théorie des AMAS (Adaptive Multi-Agent Systems), nous proposent une représentation générique à base d'agents des problèmes d'optimisation continue. A partir de cette représentation, nous proposons un comportement nominal pour les agents afin d'exécuter le processus d'optimisation. Nous identifions ensuite certaines configurations spécifiques qui pourraient perturber le processus, et présentons un ensemble de comportements coopératifs pour les agents afin d'identifier et de résoudre ces configurations problématiques. Enfin, nous utilisons les mécanismes de coopération que nous avons introduit comme base à des patterns de résolution coopérative de problèmes. Ces patterns sont des recommandations de haut niveau pour identifier et résoudre des configurations potentiellement problématiques qui peuvent survenir au sein de systèmes de résolution collective de problèmes. Ils fournissent chacun un mécanisme de résolution coopérative pour les agents, en utilisant des indicateurs abstraits qui doivent être instanciés pour le problème en cours.In an effort to tackle such complex problems, the field of multidisciplinary optimization methods was proposed. Multidisciplinary optimization methods propose to distribute the optimization process, often by reformulating the original problem is a way that reduce the interconnections between the disciplines. However these methods present several drawbacks regarding the difficulty to correctly apply them, as well as their lack of flexibility. Based on the AMAS (Adaptive Multi-Agent Systems) theory, we propose a general agent-based representation of continuous optimization problems. From this representation we propose a nominal behavior for the agents in order to do the optimization process. We then identify some specific configurations which would disturb this nominal optimization process, and present a set of cooperative behaviors for the agents to identify and solve these problematic configurations. At last, we use the cooperation mechanisms we introduced as the basis for more general Collective Problem Solving Patterns. These patterns are high-level guideline to identify and solve potential problematic configurations which can arise in distributed problem solving systems. They provide a specific cooperative mechanism for the agents, using abstract indicators that are to be instantiated on the problem at hand