Academic literature on the topic 'Pareto front search algorithms'
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Journal articles on the topic "Pareto front search algorithms"
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Fallah-Mehdipour, E., O. Bozorg Haddad, and M. A. Mariño. "MOPSO algorithm and its application in multipurpose multireservoir operations." Journal of Hydroinformatics 13, no. 4 (2010): 794–811. http://dx.doi.org/10.2166/hydro.2010.105.
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The main reason for applying evolutionary algorithms in multi-objective optimization problems is to obtain near-optimal nondominated solutions/Pareto fronts, from which decision-makers can choose a suitable solution. The efficiency of multi-objective optimization algorithms depends on the quality and quantity of Pareto fronts produced by them. To compare different Pareto fronts resulting from different algorithms, criteria are considered and applied in multi-objective problems. Each criterion denotes a characteristic of the Pareto front. Thus, ranking approaches are commonly used to evaluate different algorithms based on different criteria. This paper presents three multi-objective optimization methods based on the multi-objective particle swarm optimization (MOPSO) algorithm. To evaluate these methods, bi-objective mathematical benchmark problems are considered. Results show that all proposed methods are successful in finding near-optimal Pareto fronts. A ranking method is used to compare the capability of the proposed methods and the best method for further study is suggested. Moreover, the nominated method is applied as an optimization tool in real multi-objective optimization problems in multireservoir system operations. A new technique in multi-objective optimization, called warm-up, based on the PSO algorithm is then applied to improve the quality of the Pareto front by single-objective search. Results show that the proposed technique is successful in finding an optimal Pareto front.
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Mahdi, Samir, and Brahim Nini. "Improved Memetic NSGA-II Using a Deep Neighborhood Search." International Journal of Applied Metaheuristic Computing 12, no. 4 (2021): 138–54. http://dx.doi.org/10.4018/ijamc.2021100108.
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Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.
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Savsani, Vimal, Vivek Patel, Bhargav Gadhvi, and Mohamed Tawhid. "Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm." Modelling and Simulation in Engineering 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/2034907.
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Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS) algorithm, which is based on the search technique of heat transfer search (HTS) algorithm. MOHTS employs the elitist nondominated sorting and crowding distance approach of an elitist based nondominated sorting genetic algorithm-II (NSGA-II) for obtaining different nondomination levels and to preserve the diversity among the optimal set of solutions, respectively. The capability in yielding a Pareto front as close as possible to the true Pareto front of MOHTS has been tested on the multiobjective optimization problem of the vehicle suspension design, which has a set of five second-order linear ordinary differential equations. Half car passive ride model with two different sets of five objectives is employed for optimizing the suspension parameters using MOHTS and NSGA-II. The optimization studies demonstrate that MOHTS achieves the better nondominated Pareto front with the widespread (diveresed) set of optimal solutions as compared to NSGA-II, and further the comparison of the extreme points of the obtained Pareto front reveals the dominance of MOHTS over NSGA-II, multiobjective uniform diversity genetic algorithm (MUGA), and combined PSO-GA based MOEA.
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Asadzadeh, Masoud, and Bryan Tolson. "Hybrid Pareto archived dynamically dimensioned search for multi-objective combinatorial optimization: application to water distribution network design." Journal of Hydroinformatics 14, no. 1 (2011): 192–205. http://dx.doi.org/10.2166/hydro.2011.098.
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Pareto archived dynamically dimensioned search (PA-DDS) has been modified to solve combinatorial multi-objective optimization problems. This new PA-DDS algorithm uses discrete-DDS as a search engine and archives all non-dominated solutions during the search. PA-DDS is also hybridized by a general discrete local search strategy to improve its performance near the end of the search. PA-DDS inherits the simplicity and parsimonious characteristics of DDS, so it has only one algorithm parameter and adjusts the search strategy to the user-defined computational budget. Hybrid PA-DDS was applied to five benchmark water distribution network design problems and its performance was assessed in comparison with NSGAII and SPEA2. This comparison was based on a revised hypervolume metric introduced in this study. The revised metric measures the algorithm performance relative to the observed performance variation across all algorithms in the comparison. The revised metric is improved in terms of detecting clear differences between approximations of the Pareto optimal front. Despite its simplicity, Hybrid PA-DDS shows high potential for approximating the Pareto optimal front, especially with limited computational budget. Independent of the PA-DDS results, the new local search strategy is also shown to substantially improve the final NSGAII and SPEA2 Pareto fronts with minimal additional computational expense.
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Tomita, Kouhei, Minami Miyakawa, and Hiroyuki Sato. "Adaptive Control of Dominance Area of Solutions in Evolutionary Many-Objective Optimization." New Mathematics and Natural Computation 11, no. 02 (2015): 135–50. http://dx.doi.org/10.1142/s1793005715400025.
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Controlling the dominance area of solutions (CDAS) relaxes the concept of Pareto dominance with an user-defined parameter S. CDAS with S < 0.5 expands the dominance area and improves the search performance of multi-objective evolutionary algorithms (MOEAs) especially in many-objective optimization problems (MaOPs) by enhancing convergence of solutions toward the optimal Pareto front. However, there is a problem that CDAS with an expanded dominance area (S < 0.5) generally cannot approximate entire Pareto front. To overcome this problem we propose an adaptive CDAS (A-CDAS) that adaptively controls the dominance area of solutions during the solutions search. Our method improves the search performance in MaOPs by approximating the entire Pareto front while keeping high convergence. In early generations, A-CDAS tries to converge solutions toward the optimal Pareto front by using an expanded dominance area with S < 0.5. When we detect convergence of solutions, we gradually increase S and contract the dominance area of solutions to obtain Pareto optimal solutions (POS) covering the entire optimal Pareto front. We verify the effectiveness and the search performance of the proposed A-CDAS on concave and convex DTLZ3 benchmark problems with 2–8 objectives, and show that the proposed A-CDAS achieves higher search performance than conventional non-dominated sorting genetic algorithm II (NSGA-II) and CDAS with an expanded dominance area.
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Schütze, Oliver, Marco Laumanns, Emilia Tantar, Carlos A. Coello Coello, and El-Ghazali Talbi. "Computing Gap Free Pareto Front Approximations with Stochastic Search Algorithms." Evolutionary Computation 18, no. 1 (2010): 65–96. http://dx.doi.org/10.1162/evco.2010.18.1.18103.
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Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of ɛ-dominance. Though bounds on the quality of the limit approximation—which are entirely determined by the archiving strategy and the value of ɛ—have been obtained, the strategies do not guarantee to obtain a gap free approximation of the Pareto front. That is, such approximations A can reveal gaps in the sense that points f in the Pareto front can exist such that the distance of f to any image point F(a), a ∈ A, is “large.” Since such gap free approximations are desirable in certain applications, and the related archiving strategies can be advantageous when memetic strategies are included in the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs, we give some numerical results to visualize the behavior of the different archiving strategies. Finally, we demonstrate the potential for a possible hybridization of a given stochastic search algorithm with a particular local search strategy—multi-objective continuation methods—by showing that the concept of ɛ-dominance can be integrated into this approach in a suitable way.
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Mashwani, Wali Khan. "Comprehensive Survey of the Hybrid Evolutionary Algorithms." International Journal of Applied Evolutionary Computation 4, no. 2 (2013): 1–19. http://dx.doi.org/10.4018/jaec.2013040101.
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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) is two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization problems (MOPs. The mathematical formulation of a MOP and some basic definitions for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs. The authors present literature review in subsections. Subsection 2.1 provides memetic multiobjective evolutionary algorithms. Subsection 2.2 presents the hybrid versions of well-known Pareto dominance based MOEAs. Subsection 2.4 summarizes some enhanced Versions of MOEA/D paradigm. Subsection 2.5 reviews some multimethod search approaches dealing optimization problems.
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CHEN, JIANYONG, QIUZHEN LIN, and QINGBIN HU. "APPLICATION OF NOVEL CLONAL ALGORITHM IN MULTIOBJECTIVE OPTIMIZATION." International Journal of Information Technology & Decision Making 09, no. 02 (2010): 239–66. http://dx.doi.org/10.1142/s0219622010003804.
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In this paper, a novel clonal algorithm applied in multiobjecitve optimization (NCMO) is presented, which is designed from the improvement of search operators, i.e. dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM) operator. The main notion of these approaches is to perform more coarse-grained search at initial stage in order to speed up the convergence toward the Pareto-optimal front. Once the solutions are getting close to the Pareto-optimal front, more fine-grained search is performed in order to reduce the gaps between the solutions and the Pareto-optimal front. Based on this purpose, a cooling schedule is adopted in these approaches, reducing the parameters gradually to a minimal threshold, the aim of which is to keep a desirable balance between fine-grained search and coarse-grained search. By this means, the exploratory capabilities of NCMO are enhanced. When compared with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that NCMO has remarkable performance.
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Wang, Mingzhao, Yuping Wang, and Xiaoli Wang. "A Space Division Multiobjective Evolutionary Algorithm Based on Adaptive Multiple Fitness Functions." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 03 (2016): 1659005. http://dx.doi.org/10.1142/s0218001416590059.
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The weighted sum of objective functions is one of the simplest fitness functions widely applied in evolutionary algorithms (EAs) for multiobjective programming. However, EAs with this fitness function cannot find uniformly distributed solutions on the entire Pareto front for nonconvex and complex multiobjective programming. In this paper, a novel EA based on adaptive multiple fitness functions and adaptive objective space division is proposed to overcome this shortcoming. The objective space is divided into multiple regions of about the same size by uniform design, and one fitness function is defined on each region by the weighted sum of objective functions to search for the nondominated solutions in this region. Once a region contains fewer nondominated solutions, it is divided into several sub-regions and one additional fitness function is defined on each sub-region. The search will be carried out simultaneously in these sub-regions, and it is hopeful to find more nondominated solutions in such a region. As a result, the nondominated solutions in each region are changed adaptively, and eventually are uniformly distributed on the entire Pareto front. Moreover, the complexity of the proposed algorithm is analyzed. The proposed algorithm is applied to solve 13 test problems and its performance is compared with that of 10 widely used algorithms. The results show that the proposed algorithm can effectively handle nonconvex and complex problems, generate widely spread and uniformly distributed solutions on the entire Pareto front, and outperform those compared algorithms.
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Shankar, K., and Akshay S. Baviskar. "Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization." International Journal of Intelligent Computing and Cybernetics 11, no. 1 (2018): 20–46. http://dx.doi.org/10.1108/ijicc-12-2016-0063.
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Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population. Findings The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm. Practical implications The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper. Social implications The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives. Originality/value This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.
Dissertations / Theses on the topic "Pareto front search algorithms"
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Hou, Ruizhe. "Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives." Scholar Commons, 2018. http://scholarcommons.usf.edu/etd/7169.
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Latin hypercube designs (LHDs) have broad applications in constructing computer experiments and sampling for Monte-Carlo integration due to its nice property of having projections evenly distributed on the univariate distribution of each input variable. The LHDs have been combined with some commonly used computer experimental design criteria to achieve enhanced design performance. For example, the Maximin-LHDs were developed to improve its space-filling property in the full dimension of all input variables. The MaxPro-LHDs were proposed in recent years to obtain nicer projections in any subspace of input variables. This thesis integrates both space-filling and projection characteristics for LHDs and develops new algorithms for constructing optimal LHDs that achieve nice properties on both criteria based on using the Pareto front optimization approach. The new LHDs are evaluated through case studies and compared with traditional methods to demonstrate their improved performance.
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Deng, Qichen. "Antenna Optimization in Long-Term Evolution Networks." Thesis, KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-119147.
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The aim of this master thesis is to study algorithms for automatically tuning antenna parameters to improve the performance of the radio access part of a telecommunication network and user experience. There are four dierent optimization algorithms, Stepwise Minimization Algorithm, Random Search Algorithm, Modied Steepest Descent Algorithm and Multi-Objective Genetic Algorithm to be applied to a model of a radio access network. The performances of all algorithms will be evaluated in this thesis. Moreover, a graphical user interface which is developed to facilitate the antenna tuning simulations will also be presented in the appendix of the report.
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Knowles, Joshua D. "Local-search and hybrid evolutionary algorithms for Pareto optimization." Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394429.
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Ismaïli, Anisse. "Algorithms for Nash-equilibria in Agent Networks and for Pareto-efficiency in State Space Search : Generalizations to Pareto-Nash in Multiple Objective Games." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066148.
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Un agent est un élément qui décide une action. Par ce formalisme très général on peut aussi bien désigner deux enfants jouant à pierre-papier-ciseaux, des êtres humains choisissant des produits sur un marché, un logiciel de routage calculant un plus court chemin sur Internet pour transporter des informations sur des routes numériques encombrées, qu’une enchère combinatoire automatique pour vendre des liens commerciaux et rapportant des milliards à google. Les chercheurs en théorie de la décision algorithmique et en théorie des jeux algorithmique – des mathématiciens et informaticiens – aiment à penser que ces exemples concrets peuvent être modélisés au moyen de systèmes décisionnels rationnels, aussi complexe la réalité soit-elle. Les systèmes décisionnels modernes trouvent leur complexité dans plusieurs dimensions. D’une part, les préférences d’un agent peuvent être complexes à représenter avec de simples nombres réels, alors que de multiples objectifs conflictuels interviennent dans chaque décision. D’une autre part, les interactions entre agents font que les récompenses de chacun dépendent des actions de tous, rendant difficile la prédiction des actions individualistes résultantes. L’objet de cette thèse en théorie algorithmique des systèmes décisionnels interactifs (jeux) est de poursuivre des efforts de recherche menés sur ces deux sources de complexité, et in fine, de considérer les deux complexités dans un même modèle<br>An agent is an entity that decides an action. By using this abstraction, it is possible to model two children playing rock-paper-scissors, a software computing a shortest path on the internet for packet-routing on congest numerical networks, as well as an automatic combinatorial auction that sells commercial links in order to make google earn billions. The researchers in algorithmic decision theory and algorithmic game theory (mathematicians and computer scientists) like to think that these real-life examples can be modelled by mean of agents in an interaction decision system, no matter how complex is reality. The modern interactive decision systems find their complexity in multiple aspects. Firstly, the preferences of an agent can be complex to model with real numbers when there are multiple conflicting objectives resulting from every decision. Secondly, the interactions between agents are such that the payoff of every individual depends of the actions of all, making difficult the prediction of the resulting action-profile. This thesis aims at pursuing research efforts lead on these two sources of complexity, in order to consider ultimately both aspects in the same model
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Amouzgar, Kaveh. "Multi-objective optimization using Genetic Algorithms." Thesis, Högskolan i Jönköping, Tekniska Högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19851.
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In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (GA) are reviewed. Two algorithms, one for single objective and the other for multi-objective problems, which are believed to be more efficient are described in details. The algorithms are coded with MATLAB and applied on several test functions. The results are compared with the existing solutions in literatures and shows promising results. Obtained pareto-fronts are exactly similar to the true pareto-fronts with a good spread of solution throughout the optimal region. Constraint handling techniques are studied and applied in the two algorithms. Constrained benchmarks are optimized and the outcomes show the ability of algorithm in maintaining solutions in the entire pareto-optimal region. In the end, a hybrid method based on the combination of the two algorithms is introduced and the performance is discussed. It is concluded that no significant strength is observed within the approach and more research is required on this topic. For further investigation on the performance of the proposed techniques, implementation on real-world engineering applications are recommended.
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Constantinou, Demetrakis. "Ant colony optimisation algorithms for solving multi-objective power-aware metrics for mobile ad hoc networks." Thesis, University of Pretoria, 2010. http://hdl.handle.net/2263/25981.
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A mobile ad hoc network (MANET) is an infrastructure-less multi-hop network where each node communicates with other nodes directly or indirectly through intermediate nodes. Thus, all nodes in a MANET basically function as mobile routers participating in some routing protocol required for deciding and maintaining the routes. Since MANETs are infrastructure-less, self-organizing, rapidly deployable wireless networks, they are highly suitable for applications such as military tactical operations, search and rescue missions, disaster relief operations, and target tracking. Building such ad-hoc networks poses a significant technical challenge because of energy constraints and specifically in relation to the application of wireless network protocols. As a result of its highly dynamic and distributed nature, the routing layer within the wireless network protocol stack, presents one of the key technical challenges in MANETs. In particular, energy efficient routing may be the most important design criterion for MANETs since mobile nodes are powered by batteries with limited capacity and variable recharge frequency, according to application demand. In order to conserve power it is essential that a routing protocol be designed to guarantee data delivery even should most of the nodes be asleep and not forwarding packets to other nodes. Load distribution constitutes another important approach to the optimisation of active communication energy. Load distribution enables the maximisation of the network lifetime by facilitating the avoidance of over-utilised nodes when a route is in the process of being selected. Routing algorithms for mobile networks that attempt to optimise routes while at- tempting to retain a small message overhead and maximise the network lifetime has been put forward. However certain of these routing protocols have proved to have a negative impact on node and network lives by inadvertently over-utilising the energy resources of a small set of nodes in favour of others. The conservation of power and careful sharing of the cost of routing packets would ensure an increase in both node and network lifetimes. This thesis proposes simultaneously, by using an ant colony optimisation (ACO) approach, to optimise five power-aware metrics that do result in energy-efficient routes and also to maximise the MANET's lifetime while taking into consideration a realistic mobility model. By using ACO algorithms a set of optimal solutions - the Pareto-optimal set - is found. This thesis proposes five algorithms to solve the multi-objective problem in the routing domain. The first two algorithms, namely, the energy e±ciency for a mobile network using a multi-objective, ant colony optimisation, multi-pheromone (EEMACOMP) algorithm and the energy efficiency for a mobile network using a multi-objective, ant colony optimisation, multi-heuristic (EEMACOMH) algorithm are both adaptations of multi-objective, ant colony optimisation algorithms (MOACO) which are based on the ant colony system (ACS) algorithm. The new algorithms are constructive which means that in every iteration, every ant builds a complete solution. In order to guide the transition from one state to another, the algorithms use pheromone and heuristic information. The next two algorithms, namely, the energy efficiency for a mobile network using a multi-objective, MAX-MIN ant system optimisation, multi-pheromone (EEMMASMP) algorithm and the energy efficiency for a mobile network using a multi-objective, MAX- MIN ant system optimisation, multi-heuristic (EEMMASMH) algorithm, both solve the above multi-objective problem by using an adaptation of the MAX-MIN ant system optimisation algorithm. The last algorithm implemented, namely, the energy efficiency for a mobile network using a multi-objective, ant colony optimisation, multi-colony (EEMACOMC) algorithm uses a multiple colony ACO algorithm. From the experimental results the final conclusions may be summarised as follows:<ul><li> Ant colony, multi-objective optimisation algorithms are suitable for mobile ad hoc networks. These algorithms allow for high adaptation to frequent changes in the topology of the network. </li><li> All five algorithms yielded substantially better results than the non-dominated sorting genetic algorithm (NSGA-II) in terms of the quality of the solution. </li><li> All the results prove that the EEMACOMP outperforms the other four ACO algorithms as well as the NSGA-II algorithm in terms of the number of solutions, closeness to the true Pareto front and diversity. </li></ul><br>Thesis (PhD)--University of Pretoria, 2010.<br>Computer Science<br>unrestricted
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Faccioli, Rodrigo Antonio. "Algoritmo híbrido multi-objetivo para predição de estrutura terciária de proteínas." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-15052007-153736/.
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Muitos problemas de otimização multi-objetivo utilizam os algoritmos evolutivos para encontrar as melhores soluções. Muitos desses algoritmos empregam as fronteiras de Pareto como estratégia para obter tais soluções. Entretando, conforme relatado na literatura, há a limitação da fronteira para problemas com até três objetivos, podendo tornar seu emprego insatisfatório para os problemas com quatro ou mais objetivos. Além disso, as propostas apresentadas muitas vezes eliminam o emprego dos algoritmos evolutivos, os quais utilizam tais fronteiras. Entretanto, as características dos algoritmos evolutivos os qualificam para ser empregados em problemas de otimização, como já vem sendo difundido pela literatura, evitando eliminá-lo por causa da limitação das fronteiras de Pareto. Assim sendo, neste trabalho se buscou eliminar as fronteiras de Pareto e para isso utilizou a lógica Fuzzy, mantendo-se assim o emprego dos algoritmos evolutivos. O problema escolhido para investigar essa substituição foi o problema de predição de estrutura terciária de proteínas, pois além de se encontrar em aberto é de suma relevância para a área de bioinformática.<br>Several multi-objective optimization problems utilize evolutionary algorithms to find the best solution. Some of these algoritms make use of the Pareto front as a strategy to find these solutions. However, according to the literature, the Pareto front limitation for problems with up to three objectives can make its employment unsatisfactory in problems with four or more objectives. Moreover, many authors, in most cases, propose to remove the evolutionay algorithms because of Pareto front limitation. Nevertheless, characteristics of evolutionay algorithms qualify them to be employed in optimization problems, as it has being spread out by literature, preventing to eliminate it because the Pareto front elimination. Thus being, this work investigated to remove the Pareto front and for this utilized the Fuzzy logic, remaining itself thus the employ of evolutionary algorithms. The choice problem to investigate this remove was the protein tertiary structure prediction, because it is a open problem and extremely relevance to bioinformatic area.
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Pimenta, Adinovam Henriques de Macedo. "Geração genética multiobjetivo de bases de conhecimento fuzzy com enfoque na distribuição das soluções não dominadas." Universidade Federal de São Carlos, 2014. https://repositorio.ufscar.br/handle/ufscar/8574.
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Previous issue date: 2014-12-02<br>Não recebi financiamento<br>The process of building the knowledge base of fuzzy systems has benefited extensively
of methods to automatically extract the necessary knowledge from data sets that represent
examples of the problem. Among the topics investigated in the most recent research is the
matter of balance between accuracy and interpretability, which has been addressed by means
of multi-objective genetiv algorithms, NSGA-II being on of the most popular. In this scope,
we identified the need to control the diversity of solutions found by these algorithms, so that
each solution would balance the Pareto frontier with respect to the goals optimized by the
multi-objective genetic algorithm. In this PhD thesis a multi-objective genetic algorithm,
named NSGA-DO, is proposed. It is able to find non dominated solutions that balance
the Pareto frontier with respect optimization of the objectives. The main characteristicof
NSGA-DO is the distance oriented selection of solutions. Once the Pareto frontier is found,
the algorithm uses the locations of the solutions in the frontier to find the best distribution of
solutions. As for the validation of the proposal, NSGA-DO was applied to a methodology
for the generation of fuzzy knowledge bases. Experiments show the superiority of NSGADO
when compared to NSGA-II in all three issues analyzed: dispersion, accuracy and
interpretability.<br>A construção da base de conhecimento de sistemas fuzzy tem sido beneficiada intensamente
por métodos automáticos que extraem o conhecimento necessário a partir de conjuntos de
dados que representam exemplos do problema. Entre os tópicos mais investigados nas pesquisas
recentes está a questão do balanceamento entre acuidade e interpretabilidade, que
têm sido abordada por meio dos algoritmos genéticos multiobjetivo, sendo o NSGA-II um
dos mais populares. Neste escopo, identificou-se a necessidade do controle da distribuição
das soluções encontradas por estes algoritmos, a fim de que cada solução possa equilibrar
a fronteira de Pareto com relação aos objetivos otimizados pelo algoritmo genético multiobjetivo.
Neste sentido, desenvolveu-se neste projeto de doutorado um algoritmo genético
multiobjetivo, chamado NSGA-DO, capaz de encontrar soluções não dominadas que equilibram
a fronteira de Pareto nos objetivos a serem otimizados. A principal característica do
NSGA-DO é a seleção de soluções orientada à distância. Uma vez encontrada a fronteira
de Pareto, o algoritmo usa a localização das soluções nesta fronteira para encontrar a melhor
distribuição das soluções. Para a validação da proposta, aplicou-se o NSGA-DO em
uma metodologia para a geração de bases de conhecimento fuzzy. Experimentos realizados
comprovaram a superioridade do NSGA-DO com relação ao NSGA-II nos três quesitos
analisados: dispersão, acurácia e interpretabilidade.
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Zhu, Nanhao. "Simulation and optimization of energy consumption in wireless sensor networks." Phd thesis, Ecole Centrale de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-01002108.
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Les grandes évolutions de la technique de systèmes embarqués au cours des dernières années ont permis avec succès la combinaison de la détection, le traitement des données, et diverses technologies de communication sans fil tout en un nœud. Les réseaux de capteurs sans fil (WSN) qui se composent d'un grand nombre de ces nœuds ont attiré l'attention du monde entier sur les établissements scolaires et les communautés industrielles, puisque leurs applications sont très répandues dans des domaines tels que la surveillance de l'environnement, le domaine militaire, le suivi des événements et la détection des catastrophes. En raison de la dépendance sur la batterie, la consommation d'énergie des réseaux de capteurs a toujours été la préoccupation la plus importante. Dans cet article, une méthode mixte est utilisée pour l'évaluation précise de l'énergie sur les réseaux de capteurs, ce qui inclut la conception d'un environnement de SystemC simulation base au niveau du système et au niveau des transactions pour l'exploration de l'énergie, et la construction d'une plate-forme de mesure d'énergie pour les mesures de nœud banc d'essai dans le monde réel pour calibrer et valider à la fois le modèle de simulation énergétique de nœud et le modèle de fonctionnement. La consommation d'énergie élaborée de plusieurs différents réseaux basés sur la plate-forme de nœud sont étudiées et comparées dans différents types de scénarios, et puis des stratégies globales d'économie d'énergie sont également données après chaque scénario pour les développeurs et les chercheurs qui se concentrent sur la conception des réseaux de capteurs efficacité énergétique. Un cadre de l'optimisation basée sur un algorithme génétique est conçu et mis en œuvre à l'aide de MATLAB pour les réseaux de capteurs conscients de l'énergie. En raison de la propriété de recherche global des algorithmes génétiques, le cadre de l'optimisation peut automatiquement et intelligemment régler des centaines de solutions possibles pour trouver le compromis le plus approprié entre la consommation d'énergie et d'autres indicateurs de performance. Haute efficacité et la fiabilité du cadre de la recherche des solutions de compromis entre l'énergie de nœud, la perte de paquets réseau et la latence ont été prouvés par réglage paramètres de l'algorithme CSMA / CA de unslotted (le mode non-beacon de IEEE 802.15.4) dans notre simulation basé sur SystemC via une fonction de coût de la somme pondérée. En outre, le cadre est également disponible pour la tâche d'optimisation basée sur multi-scénarios et multi-objectif par l'étude d'une application médicale typique sur le corps humain.
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Singh, Gobind Preet. "Pricing Financial Option as a Multi-Objective Optimization Problem Using Firefly Algorithms." 2016. http://hdl.handle.net/1993/31618.
Full textAbstract:
An option, a type of a financial derivative, is a contract that creates an opportunity for a market player to avoid risks involved in investing, especially in equities. An investor desires to know the accurate value of an option before entering into a contract to buy/sell the underlying asset (stock). There are various techniques that try to simulate real market conditions in order to price or evaluate an option. However, most of them achieved limited success due to high uncertainty in price behavior of the underlying asset. In this study, I propose two new Firefly variant algorithms to compute accurate worth for European and American option contracts and compare them with popular option pricing models (such as Black-Scholes-Merton, binomial lattice, Monte-Carlo, etc.) and real market data.
In my study, I have first modelled the option pricing as a multi-objective optimization problem, where I introduced the pay-off and probability of achieving that pay-off as the main optimization objectives. Then, I proposed to use a latest nature-inspired algorithm that uses the bioluminescence of Fireflies to simulate the market conditions, a first attempt in the literature. For my thesis, I have proposed adaptive weighted-sum based Firefly algorithm and non-dominant sorting Firefly algorithm to find Pareto optimal solutions for the option pricing problem. Using my algorithm(s), I have successfully computed complete Pareto front of option prices for a number of option contracts from the real market (Bloomberg data). Also, I have shown that one of the points on the Pareto front represents the option value within 1-2 % error of the real data (Bloomberg).
Moreover, with my experiments, I have shown that any investor may utilize the results in the Pareto fronts for deciding to get into an option contract and can evaluate the worth of a contract tuned to their risk ability. This implies that my proposed multi-objective model and Firefly algorithm could be used in real markets for pricing options at different levels of accuracy. To the best of my knowledge, modelling option pricing problem as a multi-objective optimization problem and using newly developed Firefly algorithm for solving it is unique and novel.<br>October 2016
Book chapters on the topic "Pareto front search algorithms"
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López-Pintado, Orlenys, Marlon Dumas, Maksym Yerokhin, and Fabrizio Maria Maggi. "Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes." In Lecture Notes in Business Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85440-9_6.
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AbstractThe allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.
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Millard, Alan G., David R. White, and John A. Clark. "Searching for Pareto-optimal Randomised Algorithms." In Search Based Software Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33119-0_14.
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Kumar, R., and P. K. Singh. "Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_14.
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Drugan, Madalina M., and Dirk Thierens. "Path-Guided Mutation for Stochastic Pareto Local Search Algorithms." In Parallel Problem Solving from Nature, PPSN XI. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15844-5_49.
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Dubois-Lacoste, Jérémie, Manuel López-Ibáñez, and Thomas Stützle. "Pareto Local Search Algorithms for Anytime Bi-objective Optimization." In Evolutionary Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29124-1_18.
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Everingham, Mark, Henk Muller, and Barry Thomas. "Evaluating Image Segmentation Algorithms Using the Pareto Front." In Computer Vision — ECCV 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47979-1_3.
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Legriel, Julien, Colas Le Guernic, Scott Cotton, and Oded Maler. "Approximating the Pareto Front of Multi-criteria Optimization Problems." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12002-2_6.
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Shukla, Pradyumn Kumar. "In Search of Proper Pareto-optimal Solutions Using Multi-objective Evolutionary Algorithms." In Computational Science – ICCS 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72590-9_154.
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Sosa Hernández, Víctor Adrián, Oliver Schütze, Günter Rudolph, and Heike Trautmann. "The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume." In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01128-8_13.
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Huang, Jiangnan, Zixi Chen, and Nicolas Dupin. "Comparing Local Search Initialization for K-Means and K-Medoids Clustering in a Planar Pareto Front, a Computational Study." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85672-4_2.
Full textConference papers on the topic "Pareto front search algorithms"
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Schütze, Oliver, Marco Laumanns, Emilia Tantar, Carlos A. Coello Coello, and El-ghazali Talbi. "Convergence of stochastic search algorithms to gap-free pareto front approximations." In the 9th annual conference. ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277130.
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Xia, Tingting, and Mian Li. "An Efficient Multi-Objective Robust Optimization Method by Sequentially Searching From Nominal Pareto Solutions." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22155.
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Abstract Multi-objective optimization problems (MOOPs) with uncertainties are common in engineering design problems. To find the robust Pareto fronts, multi-objective robust optimization methods with inner-outer optimization structures generally have high computational complexity, which is always an important issue to address. Based on the general experience, robust Pareto solutions usually lie somewhere near the nominal Pareto points. Starting from the obtained nominal Pareto points, the search process for robust solutions could be more efficient. In this paper, we propose a method that sequentially approaching to the robust Pareto front (SARPF) from the nominal Pareto points. MOOPs are solved by the SARPF in two optimization stages. The deterministic optimization problem and the robustness metric optimization problem are solved in the first stage, and nominal Pareto solutions and the robust-most solutions can be found respectively. In the second stage, a new single-objective robust optimization problem is formulated to find the robust Pareto solutions starting from the nominal Pareto points in the region between the nominal Pareto front and the robust-most points. The proposed SARPF method can save a significant amount of computation time since the optimization process can be performed in parallel at each stage. Vertex estimation is also applied to approximate the worst-case uncertain parameter values which can save computational efforts further. The global solvers, NSGA-II for the multi-objective case and genetic algorithm (GA) for the single-objective case, are used in corresponding optimization processes. Two examples with comparison to a previous method are presented for the applicability and efficiency demonstration.
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Wang, Xuesong, Jinju Sun, Peng Song, Youwei He, and Da Xu. "Three-Dimensional Blade Shape Optimization for a Transonic Axial Flow Compressor Through Incorporating Surrogate Model and Sequential Sampling." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75448.
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High level aerodynamic performance has been always expected for the axial flow compressors, and it is the consistent goal for axial flow compressor research. To achieve such a goal, the incorporation of CFD with optimization algorithm and surrogate model in blade geometry optimization has become a common practice and been used extensively. But the conventional surrogate model based on merely initial sampling often deviates from the real optimization problem during optimization process and then brings the optimizer to search locally, leading to the compromised optimal results. There are yet much to do to improve such optimization design method. An optimization method of surrogate model being updated by sequential sampling strategy is developed to achieve global optimal design geometry and permit high-level of aerodynamic performance for axial flow compressor. Preliminary Kriging surrogate model is constructed with a small number of selected DOE samplings, where the multiple optimization objective functions are obtained based on CFD simulations. The optimization is performed on the surrogate model with NSGA-II optimization algorithm and Pareto fronts successively obtained. To improve the surrogate model, the MSE (Mean Squared Error) criteria is used to select the refinement point from the newest Pareto front, and it is used to update and improve the surrogate model gradually during the optimization. Such adaptive feature of the surrogate model has enabled the optimizer to search globally. The method is used to optimize transonic Rotor 37 at design flow rate, where the blade shape is varied simultaneously in terms of sweep and lean, and the geometry is optimized. In the converged Pareto front, abundant candidate designs with significant performance gains are produced. Three points over the Pareto front are selected and analyzed to take an insight into the optimization effectiveness. Overall performance curves of optimized geometries are predicted over the entire flow range and they are significantly improved compared with the original ones. Significant overall performance gains arising from the blade optimization are supported by the improved flow behavior. The overall pressure ratio or efficiency gains of the optimized blades are attributed to the significant improvement in the radial distribution of aerodynamic parameters. Further research shows that the shock structure is changed and separation zone is reduced with the optimized blades, which are the major reasons for the improvement of the aerodynamic performance of optimized blades.
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Astrua, Pio, Stefano Piola, Andrea Silingardi, and Federico Bonzani. "Multi-Objective Constrained Aero-Mechanical Optimization of an Axial Compressor Transonic Blade." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68993.
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This paper presents a flexible and effective optimization approach to design an axial compressor transonic blade for heavy duty gas turbines. The design goals are to improve design efficiency, choke margin and off-design performance while maintaining mass flow in design point as well as structural integrity. The new blade has to provide a wide operating range and to satisfy tight geometrical constraints. A database of aero-mechanical calculation results is obtained for three operating conditions. A number of 3D flow simulations are performed using a CFD solver with endwall boundary layer simplified model (thin layer) to reduce computational costs. The optimization process adopts a set of artificial neural networks (ANN) trained for each operating condition and a random walking search algorithm to determine the multi-objective Pareto Front. ANN enables speed up of the optimization process and allows high flexibility in choosing criteria for optimum member selection. Random walking algorithm gives a fast and effective method to predict the multi-dimensional Pareto Front.
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Husain, Afzal, and Kwang-Yong Kim. "Enhanced Multi-Objective Optimization of a Microchannel Heat Sink Using Multiple Surrogates Modeling." In ASME 2009 7th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2009. http://dx.doi.org/10.1115/icnmm2009-82120.
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A liquid flow microchannel heat sink has been studied and optimized with the help of three-dimensional numerical analysis and multiple surrogate methods. Two objective functions, thermal resistance and pumping power have been selected to assess the performance of the microchannel heat sink. The design variables related to the microchannel top and bottom widths, depth and fin width, which contribute to objective functions, have been identified and design space has been explored through some preliminary calculations. Design of experiments was performed and a three-level full factorial design was selected to exploit the design space. The numerical solutions obtained at these design points were utilized to construct surrogate models namely Response Surface Approximations and Kriging. A hybrid multi-objective evolutionary algorithm coupled with surrogate models and a gradient-based search algorithm is applied to find global Pareto-optimal solutions. Since, the surrogate models are highly problem-dependent, the accuracy of the two surrogate models has been discussed in view of their predictions at on- and off-Pareto-optimal front. The trade-off analysis was performed in view of the two competing objectives. The Pareto-optimal sensitivity (change in value along the Pareto-optimal front) of the design variables has been found out to economically compromise with the design variables contributing relatively less to the objective functions. The application of the multiple surrogate methods not only improves quality of multi-objective optimization but also gives the feedback of the fidelity of the model near the optimum region.
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Samad, Abdus, Kwang-Yong Kim, and Ki-Sang Lee. "Multi Objective Optimization of a Turbomachinery Blade Using NSGA-II." In ASME/JSME 2007 5th Joint Fluids Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/fedsm2007-37434.
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This work presents numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis. Reynolds-averaged Navier-Stokes (RANS) equations with k-ε turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.
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Feng, Xiaolong, Daniel Wa¨ppling, Hans Andersson, Johan O¨lvander, and Mehdi Tarkian. "Multi-Objective Optimization in Industrial Robotic Cell Design." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28488.
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It has become a common practice to conduct simulation-based design of industrial robotic cells, where Mechatronic system model of an industrial robot is used to accurately predict robot performance characteristics like cycle time, critical component lifetime, and energy efficiency. However, current robot programming systems do not usually provide functionality for finding the optimal design of robotic cells. Robot cell designers therefore still face significant challenge to manually search in design space for achieving optimal robot cell design in consideration of productivity measured by the cycle time, lifetime, and energy efficiency. In addition, robot cell designers experience even more challenge to consider the trade-offs between cycle time and lifetime as well as cycle time and energy efficiency. In this work, utilization of multi-objective optimization to optimal design of the work cell of an industrial robot is investigated. Solution space and Pareto front are obtained and used to demonstrate the trade-offs between cycle-time and critical component lifetime as well as cycle-time and energy efficiency of an industrial robot. Two types of multi-objective optimization have been investigated and benchmarked using optimal design problem of robotic work cells: 1) single-objective optimization constructed using Weighted Compromise Programming (WCP) of multiple objectives and 2) Pareto front optimization using multi-objective generic algorithm (MOGA-II). Of the industrial robotics significance, a combined design optimization problem is investigated, where design space consisting of design variables defining robot task placement and robot drive-train are simultaneously searched. Optimization efficiency and interesting trade-offs have been explored and successful results demonstrated.
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Ndao, Sidy, Yoav Peles, and Michael K. Jensen. "A Genetic Algorithm Based Multi-Objective Thermal Design Optimization of Liquid Cooled Offset Strip Fin Heat Sinks." In ASME 2009 Heat Transfer Summer Conference collocated with the InterPACK09 and 3rd Energy Sustainability Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/ht2009-88039.
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A genetic algorithm based multi-objective thermal design optimization of liquid cooled offset strip fin heat sinks is presented. Using water and HFE-7000 as coolants, Matlab’s genetic algorithm and direct search toolbox functions were utilized to determine the optimal thermal design of the offset strip fin heat sink based on the total thermal resistance and power consumption under constant pressure drop. For a relatively small fin length, the total thermal resistance decreases with increasing fin length and aspect ratio α. For larger fin lengths, the total thermal resistance increases with increasing fin length whereas the power consumption continuously increases with increasing fin length and aspect ratio α for a given pressure drop. A plot of the Pareto front indicates a trade-off between the total thermal resistance and pumping power consumption.
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Chi, Zhongran, Haiqing Liu, and Shusheng Zang. "Multi-Objective Optimization of the Impingement-Film Cooling Structure of a HPT Endwall Using Conjugate Heat Transfer CFD." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56559.
Full textAbstract:
This paper discusses the approach of cooling design optimization of a HPT endwall with 3D Conjugate Heat Transfer (CHT) CFD applied. This study involved the optimization of the spacing of impingement jet array and the exit width of shaped holes, which were different for each cooling cavity. The optimization objectives were to reduce the wall temperature level and also to increase the aerodynamic performance of the gas turbine. The optimization methodology consisted of an in-house parametric design & CFD mesh generation tool, a CHT CFD solver, a database of wall temperature distributions, a metamodel, and a genetic algorithm (GA) for evolutionary multi-objective optimization. The CFD tool was validated against experimental data of an endwall at CHT conditions. The metamodel, which could efficiently predict the aerodynamic loss and the wall temperature distribution of a new individual based on the database, was developed and coupled with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to accelerate the optimization process. Through optimization search, the Pareto front of the problem was found costing only tens of CFD runs. By comparing with additional CFD results, it was demonstrated that the design variables in the Pareto front successfully reached the optimal values. The optimal spacing of each impingement array was decided accommodating the local thermal load while avoiding jet lift-off of film coolant. It was also suggested that using cylindrical film holes near throat could benefit both aerodynamics and cooling.
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Zhou, Shengtao, Frank Lemmer, Wei Yu, Po Wen Cheng, Chao Li, and Yiqing Xiao. "Optimization of the Dynamic Response of Semi-Submersibles: Influence of the Mooring System." In ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/iowtc2019-7553.
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Abstract The design and manufacturing cost of substructures is a major component of the total expenditure for a floating wind project. Applying optimization techniques to hull shape designs has become an effective way to reduce the life-cycle cost of a floating wind system. The mooring system is regarded as the component with the highest risk, mainly due to the poor accessibility. This paper extends the previous work by investigating the influences of the mooring design on the optimization process of a semisubmersible substructure. Two optimization loops are set up. In the first loop, only the main dimensions of a semi-submersible platform are parameterized without considering mooring lines (keep a constant mooring design). Nevertheless, the second loop introduces additional variables of the mooring lines. The objective is to minimize the tower-top displacement, fairlead fatigue damage, which are calculated by the in-house nonlinear dynamic simulation code SLOW, and the manufacturing cost of platform and mooring lines. The multi-objective optimization algorithm NSGA-II is employed to search for the optimal designs within the defined design space. The design space and the Pareto fronts are compared between the two optimizations. It is found that, although the mooring design does not have a significant impact on the platform design space, one obtains a different optimal set (Pareto front) if the mooring design and mooring loads are introduced into the platform optimization process. The results of this study are expected to give a better understanding in the relationship between platform and mooring design and serve as a basis for the optimization process of semi-submersible floating wind turbines.