Relevant bibliographies by topics / Multi-objective maximization

Academic literature on the topic 'Multi-objective maximization'

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Published: 11 March 2023

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Journal articles on the topic "Multi-objective maximization"

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Nguyen, Duy Van. "Global maximization of UTA functions in multi-objective optimization." European Journal of Operational Research 228, no. 2 (July 2013): 397–404. http://dx.doi.org/10.1016/j.ejor.2012.06.022.

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Feng, Chao, and Chao Qian. "Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12302–10. http://dx.doi.org/10.1609/aaai.v35i14.17460.

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Submodular maximization has attracted much attention due to its wide application and attractive property. Previous works mainly considered one single objective function, while there can be multiple ones in practice. As the objectives are usually conflicting, there exists a set of Pareto optimal solutions, attaining different optimal trade-offs among multiple objectives. In this paper, we consider the problem of minimizing the regret ratio in multi-objective submodular maximization, which is to find at most k solutions to approximate the whole Pareto set as well as possible. We propose a new algorithm RRMS by sampling representative weight vectors and solving the corresponding weighted sums of objective functions using some given \alpha-approximation algorithm for single-objective submodular maximization. We prove that the regret ratio of the output of RRMS is upper bounded by 1-\alpha+O(\sqrt{d-1}\cdot(\frac{d}{k-d})^{\frac{1}{d-1}}), where d is the number of objectives. This is the first theoretical guarantee for the situation with more than two objectives. When d=2, it reaches the (1-\alpha+O(1/k))-guarantee of the only existing algorithm Polytope. Empirical results on the applications of multi-objective weighted maximum coverage and Max-Cut show the superior performance of RRMS over Polytope.
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Osiadacz, Andrzej J., and Niccolo Isoli. "Multi-Objective Optimization of Gas Pipeline Networks." Energies 13, no. 19 (October 2, 2020): 5141. http://dx.doi.org/10.3390/en13195141.

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The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.
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Qiu, Jianfeng, Minghui Liu, Lei Zhang, Wei Li, and Fan Cheng. "A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization." Memetic Computing 11, no. 3 (February 9, 2019): 285–96. http://dx.doi.org/10.1007/s12293-019-00280-7.

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Alshareef, Haya, and Mashael Maashi. "Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem." Applied Sciences 12, no. 11 (June 2, 2022): 5649. http://dx.doi.org/10.3390/app12115649.

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Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreover, multi-objective software module clustering involves assembling a specific group of modules according to specific cluster criteria. Software module clustering classifies software modules into different clusters to enhance the software maintenance process. A structure with low coupling and high cohesion is considered an excellent software module structure. In this study, we apply a multi-objective hyper-heuristic method to solve the multi-objective module clustering problem with three objectives: (i) minimize coupling, (ii) maximize cohesion, and (iii) ensure high modularization quality. We conducted several experiments to obtain optimal and near-optimal solutions for the multi-objective module clustering optimization problem. The experimental results demonstrated that the HHMO_CF_GDA method outperformed the individual multi-objective evolutionary algorithms in solving the multi-objective software module clustering optimization problem. The resulting software, in which HHMO_CF_GDA was applied, was more optimized and achieved lower coupling with higher cohesion and better modularization quality. Moreover, the structure of the software was more robust and easier to maintain because of its software modularity.
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Hashir, Syed Muhammad, Sabyasachi Gupta, Gavin Megson, Ehsan Aryafar, and Joseph Camp. "Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization." Electronics 11, no. 3 (January 28, 2022): 401. http://dx.doi.org/10.3390/electronics11030401.

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In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems.
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Ene, Seval, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.1391.

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In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: Green supply chain management, multi-objective modelling, network optimization;
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Ene, Seval, and Nursel Ozturk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (January 15, 2018): 15–24. http://dx.doi.org/10.18844/gjbem.v7i1.1875.

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In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: green supply chain management, multi-objective modelling, network optimization.
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Küçükoğlu, Ilker, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (October 20, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.2561.

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Abstract In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: green supply chain management, multi-objective modelling, network optimization.
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Adeyeye, Ademola David, and Festus Adekunle Oyawale. "Lexicographic Multi-Objective Optimization Approach for Welding Flux System Design." European Journal of Engineering Science and Technology 4, no. 1 (June 18, 2022): 1–14. http://dx.doi.org/10.33422/ejest.v4i1.593.

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Multiple response optimization of welding flux performance has been found to be cost effective and useful for the achievement of the best balance among conflicting welding flux quality attributes. Many multi-criteria optimization methods (MCOM) have been applied in flux formulation situations where flux quality attributes are of comparable importance. However, information on applications of MCOM to flux design situations where quality attributes are in hierarchical order of importance is scarce in the open literature. In this study, a Lexicographic Multi-objective Optimization (LMO) model was proposed for handling flux design situations in which the attributes are in hierarchical order of importance. The model was applied using data from literature. Two priority levels were used: acicular ferrite (AF) maximization was assigned first priority while the maximization of polygonal ferrite (PF) content and weld-metal impact toughness (WIT) were assigned second priority subject to oxygen content constraint of 250 – 350ppm. The respective solutions for AF, PF, WIT and oxygen content were 51.19%, 21.80%, 23.70J at -20oC and 315ppm. The corresponding flux formulation was CaO (25.90) MgO (15.00) CaF2 (31.10) and Al2O3 (8.00%). Various priority structures were used to explore trade-off options and to generate three more pareto efficient solutions from which the flux formulator can select the most preferred one. The proposed model has filled the existing gap in the literature being a pioneering work in the application of lexicographic multi-objective optimization method in welding flux design.
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Dissertations / Theses on the topic "Multi-objective maximization"

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Dall'aglio, Giovanni. "PREFERENCE BASED APPROACH TO RISK SHARING." Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/11011.

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2013/2014
It is well known that optimal risk sharing is an argument that deserves both theoretical and practical interest. It originally appears in the context of reinsurance problems, but now is widely used in a variety of financial and economical applications. The problem concerning the existence of individually rational Pareto optimal allocations, namely optimal solutions, is generally treated in the literature by considering the usual requirement of completeness over decision makers’ preferences. In this thesis we present several conditions for the existence of optimal solutions in a modern preference-based approach provided that agents’ preferences are expressed by not necessarily total preorders and by considering a topological context. We prove the equivalence between optimality and maximality with respect to a coalition preorder traducing the problem of finding optimal solutions to that of guaranteeing the existence of maximal elements for a not necessarily total preorder. In this framework a "folk theorem" is of help since it guarantees the existence of a maximal element for an upper semicontinuous preorder on a compact topological space. We study the functional approaches representing optimal risk sharing identified with the so called multi-objective maximization problem and the supconvolution problem, with the aim of incorporating functional representations of not necessarily total preorders, essentially expressed by order preserving functions and multi-utility representations. We use these two notions in order to guarantee the existence of optimal solutions, and to this aim we appropriately refer to well known results in mathematical utility theory (for example, Rader’s theorem). The case of individual preferences expressed by translation invariant total preorders is also considered, completing fundamental results from the literature also extended to the case of comonotone super-additive and positively homogeneous utility functions. When comonotone allocations are considered, we limit the research of maximal elements with respect to the coalition preorder to the set of comonotone allocations, provided that monotonicity conditions with respect to second order stochastic dominance are imposed to the individual preorders. In all our framework, we deal with risks belonging to some space of nonnegative random variables on a common probability space and, as a natural application of all our considerations, we consider the Choquet Integral when the topology L∞ is considered. Come noto, il problema di risk sharing è un argomento che interessa sia aspetti teorici che applicativi. Originariamente introdotto in contesti di riassicurazione, attualmente è ampiamente utilizzato in una varietà di applicazioni finanziarie ed economiche. Il problema legato all’esistenza di allocazioni Pareto ottimali ed individualmente razionali, definite soluzioni ottime, è generalmente trattato in letteratura considerando l’usuale assioma di completezza sulle preferenze degli agenti. In questa tesi presentiamo diverse condizioni per l'esistenza di soluzioni ottime in un moderno approccio di preferenza caratterizzato dall'espressione delle preferenze individuali per mezzo di preordini non necessariamente totali e considerando un contesto topologico. Viene dimostrata l’equivalenza tra ottimalità e massimalità rispetto ad un preordine di coalizione, traducendo così il problema di trovare soluzioni ottime nel garantire l’esistenza di elementi massimali per un preordine non necessariamente totale. In questo quadro di riferimento, un "folk theorem" è di aiuto in quanto garantisce l’esistenza di un elemento massimale per un preordine superiormente semicontinuo definito su uno spazio topologico compatto. Vengono studiati approcci funzionali legati al problema di risk sharing, identificati con il problema di massimizzazione multi-obiettivo ed il problema di sup-convoluzione, con l’obiettivo di incorporare rappresentazioni funzionali di preordini non necessariamente totali, essenzialmente definite da funzioni order preserving e rappresentazioni di multi-utilità. Queste due notazioni vengono utilizzate in modo da garantire l’esistenza di soluzioni ottime, e a questo scopo ci riferiamo in modo appropriato a ben noti risultati in teoria dell’utilità (ad esempio, il teorema di Rader). Il caso di preferenze individuali espresse da preordini totali invarianti per traslazioni è anche considerato, a completamento di fondamentali risultati presenti in letteratura ed estesi anche al caso di funzioni di utilità che soddisfino alle proprietà di comonotona super-additività e positiva omogeneità. Quando si considerano allocazioni comonotone, ci limitiamo alla ricerca di elementi massimali rispetto al preordine di coalizione nell’insieme delle allocazioni comonotone, purchè vengano imposte condizioni di monotonia sui preordini individuali rispetto alla dominanza stocastica di secondo ordine. In tutto il nostro contesto di riferimento affrontiamo il caso di rischi appartenenti a spazi di variabili aleatorie non-negative definite su un comune spazio di probabilità e come naturale applicazione consideriamo l’integrale di Choquet nel caso venga considerata la topologia L∞.
XXVII Ciclo
1985
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Book chapters on the topic "Multi-objective maximization"

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Maity, Santi P., and Anal Paul. "On Joint Maximization in Energy and Spectral Efficiency in Cooperative Cognitive Radio Networks." In Multi-Objective Optimization, 141–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_6.

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Deist, Timo M., Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, and Peter A. N. Bosman. "Multi-objective Learning Using HV Maximization." In Lecture Notes in Computer Science, 103–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_8.

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Bucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. "Improving Multi-objective Evolutionary Influence Maximization in Social Networks." In Applications of Evolutionary Computation, 117–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8_9.

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Bucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. "Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks." In Applications of Evolutionary Computation, 221–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55849-3_15.

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Guo, Jian-bin, Fu-zan Chen, and Min-qiang Li. "A Multi-objective Optimization Approach for Influence Maximization in Social Networks." In Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018, 706–15. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3402-3_74.

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De, Sagar S., and Satchidananda Dehuri. "Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks." In Learning and Analytics in Intelligent Systems, 11–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39033-4_2.

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Haas, I., and S. Bekhor. "Multi-objective network design problem considering system time minimization and road safety maximization." In Transport Infrastructure and Systems, 931–38. CRC Press, 2017. http://dx.doi.org/10.1201/9781315281896-120.

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Yuce, Baris, and Ernesto Mastrocinque. "Supply Chain Network Design Using an Enhanced Hybrid Swarm-Based Optimization Algorithm." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, 95–112. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9644-0.ch003.

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Supply chain network design is one of the most important strategic issues in operations management. The main objective in designing a supply chain is to keep the cost as low as possible. However, the modelling of a supply chain requires more than single-objective such as lead-time minimization, service level maximization, and environmental impact maximization among others. Usually these objectives may cause conflicts such as increasing the service level usually causes a growth in costs. Therefore, the aim should be to find trade-off solutions to satisfy the conflicting objectives. The aim of this chapter is to propose a new method based on a hybrid version of the Bees Algorithm with Slope Angle Computation and Hill Climbing Algorithm to solve a multi-objective supply chain network design problem. A real case from the literature has been selected and solved in order to show the potentiality of the proposed method in solving a large scale combinatorial problem.
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Yuce, Baris, and Ernesto Mastrocinque. "Supply Chain Network Design Using an Enhanced Hybrid Swarm-Based Optimization Algorithm." In Supply Chain and Logistics Management, 266–83. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0945-6.ch013.

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Supply chain network design is one of the most important strategic issues in operations management. The main objective in designing a supply chain is to keep the cost as low as possible. However, the modelling of a supply chain requires more than single-objective such as lead-time minimization, service level maximization, and environmental impact maximization among others. Usually these objectives may cause conflicts such as increasing the service level usually causes a growth in costs. Therefore, the aim should be to find trade-off solutions to satisfy the conflicting objectives. The aim of this chapter is to propose a new method based on a hybrid version of the Bees Algorithm with Slope Angle Computation and Hill Climbing Algorithm to solve a multi-objective supply chain network design problem. A real case from the literature has been selected and solved in order to show the potentiality of the proposed method in solving a large scale combinatorial problem.
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Shojai, Ali Zolghadr, Jamal Shahrabi, and Masoud Jenabi. "An Integrated Bi-Objective Reverse Logistics Network Design for Remanufacturing." In Exploring Innovative and Successful Applications of Soft Computing, 281–316. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4785-5.ch015.

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Growing environmental and economical concern has led to increasing attention towards management of product return flows. An effective and efficient reverse logistics network enables companies to gain more profit and customer satisfaction. Consequently, the reverse logistics network design problem has become a critical issue. After a brief introduction to the basic concepts of reverse logistics, the authors formulate a new integrated multi-stage, multi-period, multi-product reverse logistics model for a remanufacturing system where the inventory is considered. Two objectives, minimization of the costs and maximization of coverage, are addressed. Since such network design problems belong to a class of NP-hard problems, a multi-objective genetic algorithm and a multi-objective evolutionary strategy algorithm are developed in order to find the set of non-dominated solutions. Finally, the model is tested on test problems with different sizes, and the proposed algorithms are compared based on the number, quality, and distribution of non-dominated solutions that belong to the Pareto front.
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Conference papers on the topic "Multi-objective maximization"

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Kandhway, Kundan. "Multi-Objective Information Maximization in a Social Network." In 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2023. http://dx.doi.org/10.1109/imcom56909.2023.10035644.

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Ishibuchi, Hisao, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. "Single-objective and multi-objective formulations of solution selection for hypervolume maximization." In the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570187.

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Hong, Wenjing, Guanzhou Lu, Peng Yang, Yong Wang, and Ke Tang. "A new evolutionary multi-objective algorithm for convex hull maximization." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256990.

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Fu, Xiaoyun, Rishabh Rajendra Bhatt, Samik Basu, and A. Pavan. "Multi-Objective Submodular Optimization with Approximate Oracles and Influence Maximization." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671756.

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Teh, Jiashen, Yeong Chin Koo, Ching-Ming Lai, and Yu-Huei Cheng. "Maximization of wind energy utilization through a multi-objective optimization framework." In TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8227836.

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Bucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. "Evaluating surrogate models for multi-objective influence maximization in social networks." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3208238.

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Belaiche, Leila, Laid Kahloul, Saber Benharzallah, and Yousra Hafidi. "Multi-Objective Optimization-Based Approach for Throughput Maximization in Reconfigurable Manufacturing Systems." In 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT). IEEE, 2018. http://dx.doi.org/10.1109/isiict.2018.8613718.

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Naranjani, Yousef, and Jian-Qiao Sun. "Multi-Objective Optimal Airfoil Design for Cargo Aircrafts." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67930.

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The airfoil/wing design is probably the most important part of an aircraft design. A practical aerodynamic design of airfoil requires optimal performance on a wide range of operating conditions. These requirements are often found to be conflicting and demand designer expertise for satisfactory results, not to mention the computational burden of the simulations. Although there exists many studies on direct and inverse design of airfoils, less attention has been paid to simultaneous consideration of multiple objectives. In this paper, a multi-objective optimal airfoil design procedure is presented. PARSEC parametrization method has been utilized to express the airfoil geometry in terms of twelve physical parameters. The aerodynamic performance is obtained by 2D panel method using XFOIL package. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been applied for airfoil geometry design because it is efficient and keeps the diversity among the solution set. The objective functions and constraints are chosen to enhance the flight performance at takeoff, cruise, and landing conditions for a long range cargo aircraft. Objectives include maximization of lift to drag ratio (CL/CD), maximization of rate of change of lift to attack angle (dCL/dα) for having increased lift at takeoff/landing condition and minimization of pitching moment CM2. Two applied constraints are CL > CLmin at operating condition and thickness ≤ %25. Each evaluation is consist of finding the optimal operating angle of attack and reporting the corresponding objective values. The quality of the solution at various generations has been studied to guarantee the convergence of the solution. Like any other multi-objective optimization problem (MOP), the solution would be a set of Pareto optimal configurations. Although having multiple solutions gives us a better understanding of the problem, only one configuration should be chosen by the designer. A post processing technique is also used to help the decision maker to choose the most appropriate compromise in the solution set. The method is found to be effective in finding efficient set of airfoils. The simulation is also found to be effective because it can be done on a regular personal computer. It should be noticed that the method can be easily applied to other airfoil design applications by simply modifying the objective functions and the constraints.
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Nobile, Enrico, Francesco Pinto, and Gino Rizzetto. "Multi-Objective Shape Optimization of Convective Wavy Channels." In ASME 2005 Summer Heat Transfer Conference collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems. ASMEDC, 2005. http://dx.doi.org/10.1115/ht2005-72635.

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In this paper we describe a procedure for the multi-objective shape optimization of periodic wavy channels, representative of the repeating module of an ample variety of heat exchangers. The two objectives considered are the maximization of heat transfer rate and minimization of friction factor. Since there is no a single optimum to be found, we use a Multi-Objective Genetic Algorithm and the so-called Pareto’s dominance concept. The optimization of the periodic channel is obtained, by means of an unstructured Finite Element solver, for a fluid of Prandtl number Pr = 0.7, assuming fully developed velocity and temperature fields, and steady laminar conditions. For the two-dimensional case, the geometry is parameterized either by means of linear-piecewise profiles, or NURBS, and their control points represent the design variables. The three-dimensional channels are obtained by simple extrusion of the two-dimensional geometries. The results obtained are very encouraging, and the procedure described can be applied, in principle, to even more complex problems.
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Sun, Jili, Zheng Chen, Hao Yu, Peng Qian, Dahai Zhang, and Yulin Si. "Multi-Objective Offshore Wind Farm Wake Redirection Optimization for Power Maximization and Load Reduction." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867822.

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