The identification of communities in complex networks is important to many fields including medicine, social science, national security, and marketing. A community structure facilitates the identification of hidden relations in networks that go beyond simple topological features. Current detection algorithms are centralized and scale very poorly with the number of nodes and edges present in the network. The use of these algorithms is prohibitive when applied to large-scale networks. In this paper, we propose a Self-Organized Community Identification Algorithm (SOCIAL) based on local calculations of node entropy that enables individual nodes to independently decide the community they belong to. In our context, node entropy is defined as the individual node's satisfaction with its current community. As nodes become more "satisfied'' (entropy decreases) the community structure of a network emerges. Our algorithm offers several advantages over existing approaches including near-linear performance, identification of community overlaps, and localized management of dynamic changes in the network.
{"title":"SOCIAL: A Self-Organized Entropy-Based Algorithm for Identifying Communities in Networks","authors":"Ben Collingsworth, R. Menezes","doi":"10.1109/SASO.2012.28","DOIUrl":"https://doi.org/10.1109/SASO.2012.28","url":null,"abstract":"The identification of communities in complex networks is important to many fields including medicine, social science, national security, and marketing. A community structure facilitates the identification of hidden relations in networks that go beyond simple topological features. Current detection algorithms are centralized and scale very poorly with the number of nodes and edges present in the network. The use of these algorithms is prohibitive when applied to large-scale networks. In this paper, we propose a Self-Organized Community Identification Algorithm (SOCIAL) based on local calculations of node entropy that enables individual nodes to independently decide the community they belong to. In our context, node entropy is defined as the individual node's satisfaction with its current community. As nodes become more \"satisfied'' (entropy decreases) the community structure of a network emerges. Our algorithm offers several advantages over existing approaches including near-linear performance, identification of community overlaps, and localized management of dynamic changes in the network.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114693760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Self-organisation is a promising solution for building complicated, large-scale software systems that must meet stringent adaptability and survivability requirements. At the same time, controlling self-organising software to ensure global system properties and functions is a difficult problem. This paper proposes a solution that uses architectural templates, or archetypes, replicated across a set of identical agents, and interpreted at runtime to control the agents' self-organising behaviour and results. The solution ensures, by construction, that any resulting software system meets a set of predefined goals, or constraints, while maintaining many of the self-organisation related advantages. A framework prototype was implemented and tested to show the viability of the proposed approach, in the context of a distributed data-mediation application.
{"title":"Controlling Self-Organising Software Applications with Archetypes","authors":"Bassem Debbabi, A. Diaconescu, P. Lalanda","doi":"10.1109/SASO.2012.21","DOIUrl":"https://doi.org/10.1109/SASO.2012.21","url":null,"abstract":"Self-organisation is a promising solution for building complicated, large-scale software systems that must meet stringent adaptability and survivability requirements. At the same time, controlling self-organising software to ensure global system properties and functions is a difficult problem. This paper proposes a solution that uses architectural templates, or archetypes, replicated across a set of identical agents, and interpreted at runtime to control the agents' self-organising behaviour and results. The solution ensures, by construction, that any resulting software system meets a set of predefined goals, or constraints, while maintaining many of the self-organisation related advantages. A framework prototype was implemented and tested to show the viability of the proposed approach, in the context of a distributed data-mediation application.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133760786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Message batching is a well-known optimization technique to maximize throughput of networked services. The manual configuration of the appropriate batching level is however a time consuming and not trivial task. Too low batching values can in fact render the system unstable in presence of high loads, excessively high batching values, on the other hand, can lead to high latency at low load, which may be unacceptable for delay sensitive applications. The problem is further exacerbated in presence of fluctuating workloads, as in these scenarios the optimal batching level varies dynamically over time, and pursuing optimal performances demands the employment of self-adaptive mechanisms. In this paper we study the problem of self-tuning the message batching level adopting an interdisciplinary approach that employs methodologies from control theory community to optimize the performance of Total Order Broadcast (TOB), a fundamental building block to build dependable distributed systems. Specifically, we introduce an innovative self-tuning algorithm based on extremum seeking optimization principles. We provide theoretical results on its convergence properties and an extensive experimental analysis aimed at assessing the actual effectiveness of the new algorithm in a state-of-the-art group communication system.
消息批处理是一种众所周知的优化技术，可以最大限度地提高网络服务的吞吐量。然而，手动配置适当的批处理级别是一项耗时且重要的任务。过低的批处理值实际上会导致系统在高负载下不稳定，过高的批处理值另一方面会导致低负载下的高延迟，这对于延迟敏感的应用程序来说可能是不可接受的。在工作负载波动的情况下，问题会进一步恶化，因为在这些情况下，最佳批处理水平会随时间动态变化，而追求最佳性能需要采用自适应机制。本文采用跨学科的方法研究了消息批处理级别的自调优问题，该方法采用控制理论社区的方法来优化Total Order Broadcast (TOB)的性能，TOB是构建可靠分布式系统的基本组成部分。具体来说，我们介绍了一种基于极值寻优原理的创新自调优算法。我们提供了关于其收敛特性的理论结果和广泛的实验分析，旨在评估新算法在最先进的群通信系统中的实际有效性。
{"title":"An Extremum Seeking Algorithm for Message Batching in Total Order Protocols","authors":"Diego Didona, D. Carnevale, S. Galeani, P. Romano","doi":"10.1109/SASO.2012.33","DOIUrl":"https://doi.org/10.1109/SASO.2012.33","url":null,"abstract":"Message batching is a well-known optimization technique to maximize throughput of networked services. The manual configuration of the appropriate batching level is however a time consuming and not trivial task. Too low batching values can in fact render the system unstable in presence of high loads, excessively high batching values, on the other hand, can lead to high latency at low load, which may be unacceptable for delay sensitive applications. The problem is further exacerbated in presence of fluctuating workloads, as in these scenarios the optimal batching level varies dynamically over time, and pursuing optimal performances demands the employment of self-adaptive mechanisms. In this paper we study the problem of self-tuning the message batching level adopting an interdisciplinary approach that employs methodologies from control theory community to optimize the performance of Total Order Broadcast (TOB), a fundamental building block to build dependable distributed systems. Specifically, we introduce an innovative self-tuning algorithm based on extremum seeking optimization principles. We provide theoretical results on its convergence properties and an extensive experimental analysis aimed at assessing the actual effectiveness of the new algorithm in a state-of-the-art group communication system.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129784380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For maintaining high performance and minimizing power consumption, adaptive, heterogeneous many-core architectures can be adapted at runtime to changing environmental requests or conditions as well as to changes resulting from the dynamics of the workload itself. However, the huge complexity of such architectures makes their optimization very challenging at runtime. This challenge is therefore addressed within this paper by an Organic Computing approach for realizing a proactive, self-optimizing system behavior within adaptive, heterogeneous systems using a light-weight Learning Classifier System and a Run Length Encoding Markov predictor. The first realizes a self-optimizing behavior, freeing the user from the burden of optimizing the system manually, and the latter captures the system behavior, permits prediction of future system states, and therefore permits exploiting regular behavior for further improving the overall system performance. Using the use case of optimizing the overall system performance, results showed that the proactive, self-optimizing system achieved a performance improvement of 11.3% in comparison to a non-optimizing system.
{"title":"Realizing a Proactive, Self-Optimizing System Behavior within Adaptive, Heterogeneous Many-Core Architectures","authors":"David Kramer, Wolfgang Karl","doi":"10.1109/SASO.2012.26","DOIUrl":"https://doi.org/10.1109/SASO.2012.26","url":null,"abstract":"For maintaining high performance and minimizing power consumption, adaptive, heterogeneous many-core architectures can be adapted at runtime to changing environmental requests or conditions as well as to changes resulting from the dynamics of the workload itself. However, the huge complexity of such architectures makes their optimization very challenging at runtime. This challenge is therefore addressed within this paper by an Organic Computing approach for realizing a proactive, self-optimizing system behavior within adaptive, heterogeneous systems using a light-weight Learning Classifier System and a Run Length Encoding Markov predictor. The first realizes a self-optimizing behavior, freeing the user from the burden of optimizing the system manually, and the latter captures the system behavior, permits prediction of future system states, and therefore permits exploiting regular behavior for further improving the overall system performance. Using the use case of optimizing the overall system performance, results showed that the proactive, self-optimizing system achieved a performance improvement of 11.3% in comparison to a non-optimizing system.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129861658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We have used the Proto spatial computing language to create teaming algorithms based on random chain formation. Our algorithms are self-stabilizing, scale easily from less than ten robots to thousands of robots, and are highly robust against dynamic changes in perception and communication, arena size, teaming goals, adding and removing robots, and even mobility dimension. In this paper, we describe our approach, give details on our algorithms and their self-* properties, and present simulations validating the algorithms.
{"title":"Self-Stabilizing Robot Team Formation with Proto: IEEE Self-Adaptive and Self-Organizing Systems 2012 Demo Entry","authors":"J. Beal, J. Cleveland, K. Usbeck","doi":"10.1109/SASO.2012.43","DOIUrl":"https://doi.org/10.1109/SASO.2012.43","url":null,"abstract":"We have used the Proto spatial computing language to create teaming algorithms based on random chain formation. Our algorithms are self-stabilizing, scale easily from less than ten robots to thousands of robots, and are highly robust against dynamic changes in perception and communication, arena size, teaming goals, adding and removing robots, and even mobility dimension. In this paper, we describe our approach, give details on our algorithms and their self-* properties, and present simulations validating the algorithms.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131145049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Musliner, J. Rye, T. Woods, Tom Marble, Kevin Raison
Today's computer systems are under relentless attack from cyber attackers armed with sophisticated vulnerability search and exploit development toolkits. Under DARPA's Clean-slate design of Resilient, Adaptive, Survivable Hosts (CRASH) program, we are developing FUZZBUSTER to provide self-adaptive immunity from these and other cyber threats. This poster describes the most up-to-date results from the millions of fuzz-testing operations FUZZBUSTER has conducted, as well as its results in self-adapting to mitigate the vulnerabilities it finds.
{"title":"Automatic Self-Adaptation to Mitigate Software Vulnerabilities: A Fuzzbuster Progress Report (Extended Abstract for Poster)","authors":"D. Musliner, J. Rye, T. Woods, Tom Marble, Kevin Raison","doi":"10.1109/SASO.2012.46","DOIUrl":"https://doi.org/10.1109/SASO.2012.46","url":null,"abstract":"Today's computer systems are under relentless attack from cyber attackers armed with sophisticated vulnerability search and exploit development toolkits. Under DARPA's Clean-slate design of Resilient, Adaptive, Survivable Hosts (CRASH) program, we are developing FUZZBUSTER to provide self-adaptive immunity from these and other cyber threats. This poster describes the most up-to-date results from the millions of fuzz-testing operations FUZZBUSTER has conducted, as well as its results in self-adapting to mitigate the vulnerabilities it finds.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114561174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul L. Snyder, G. Valetto, J. Fernandez-Marquez, G. Serugendo
Investigations of self-organizing mechanisms, often inspired by phenomena in natural or societal systems, have yielded a wealth of techniques for the self-adaptation of complex, large- and ultra-large-scale software systems. The principled design of self-adaptive software using principles of self-organization remains challenging. Several studies have approached this problem by proposing design patterns for self-organization. In this paper, we present the results of applying a catalog of biologically inspired design patterns to Mycoload, a self-organizing system for clustering and load balancing in decentralized service networks. We reverse-engineered Mycoload, obtaining a design that isolates instances of several patterns. This exercise allowed us to identify additional reusable self-organization mechanisms, which we have also abstracted out as design patterns: SPECIALIZATION, which we present here for the first time, and a generalized form of COLLECTIVE SORT. The pattern-based design also led to a better understanding of the relationships among the multiple self-organizing mechanisms that together determine the emegent dynamics of Mycoload.
{"title":"Augmenting the Repertoire of Design Patterns for Self-Organized Software by Reverse Engineering a Bio-Inspired P2P System","authors":"Paul L. Snyder, G. Valetto, J. Fernandez-Marquez, G. Serugendo","doi":"10.1109/SASO.2012.23","DOIUrl":"https://doi.org/10.1109/SASO.2012.23","url":null,"abstract":"Investigations of self-organizing mechanisms, often inspired by phenomena in natural or societal systems, have yielded a wealth of techniques for the self-adaptation of complex, large- and ultra-large-scale software systems. The principled design of self-adaptive software using principles of self-organization remains challenging. Several studies have approached this problem by proposing design patterns for self-organization. In this paper, we present the results of applying a catalog of biologically inspired design patterns to Mycoload, a self-organizing system for clustering and load balancing in decentralized service networks. We reverse-engineered Mycoload, obtaining a design that isolates instances of several patterns. This exercise allowed us to identify additional reusable self-organization mechanisms, which we have also abstracted out as design patterns: SPECIALIZATION, which we present here for the first time, and a generalized form of COLLECTIVE SORT. The pattern-based design also led to a better understanding of the relationships among the multiple self-organizing mechanisms that together determine the emegent dynamics of Mycoload.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122880532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Animesh Srivastava, Niloy Ganguly, F. Peruani, Bivas Mitra
Resilience analysis of the popular P2P networks (like Gnutella) has emerged as an important research issue for the network community. Most of the contemporary studies primarily focused on the estimation of percolation threshold and disruption on the largest connected component due to node removal. However, real-world networks exhibit intrinsically degree-degree correlation, which makes the behavior of a real-world network distinctly different from a random network. We believe that the proper exploitation of the degree-degree correlation information can be helpful in healing the damage caused on P2P systems by the attacks. In order to investigate, we first develop an analytical framework to study the impact of degree-degree correlation on the resilience of real-world networks at different levels (node isolation, network density and component level). Our analysis shows that the Facebook-like network, which exhibits positive degree-degree correlation are much more robust than the negatively correlated network such as Gnutella. We capitalize on this observation and propose a lightweight correlation driven local link-rewiring mechanism that can improve the resilience of a Gnutella-like network against malicious node-perturbations. We substantiate our claims with the help of rigorous simulations on the real-world Gnutella topology and Facebook social graph as well as synthetic network datasets.
{"title":"Can Degree Correlation Help to Design Resilient Superpeer Networks?","authors":"Animesh Srivastava, Niloy Ganguly, F. Peruani, Bivas Mitra","doi":"10.1109/SASO.2012.19","DOIUrl":"https://doi.org/10.1109/SASO.2012.19","url":null,"abstract":"Resilience analysis of the popular P2P networks (like Gnutella) has emerged as an important research issue for the network community. Most of the contemporary studies primarily focused on the estimation of percolation threshold and disruption on the largest connected component due to node removal. However, real-world networks exhibit intrinsically degree-degree correlation, which makes the behavior of a real-world network distinctly different from a random network. We believe that the proper exploitation of the degree-degree correlation information can be helpful in healing the damage caused on P2P systems by the attacks. In order to investigate, we first develop an analytical framework to study the impact of degree-degree correlation on the resilience of real-world networks at different levels (node isolation, network density and component level). Our analysis shows that the Facebook-like network, which exhibits positive degree-degree correlation are much more robust than the negatively correlated network such as Gnutella. We capitalize on this observation and propose a lightweight correlation driven local link-rewiring mechanism that can improve the resilience of a Gnutella-like network against malicious node-perturbations. We substantiate our claims with the help of rigorous simulations on the real-world Gnutella topology and Facebook social graph as well as synthetic network datasets.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132970853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last decade, numerous structured overlay networks were proposed as a scalable infrastructure to build large-scale distributed systems under dynamic environments. These overlays were touted to be fault-tolerant and self-managing, yet, as we show in this paper, they fall short of handling some extreme scenarios they envision. These scenarios include bootstrapping, and underlying network partitions and mergers. We argue that handling such extreme scenarios is fundamental to providing a fault-tolerant and self-managing system, and thus, structured overlay networks should intrinsically be able to handle them. In this paper, we present ReCircle, an overlay algorithm that apart from performing periodic maintenance to handle churn like any other overlay, can merge multiple structured overlay networks. We show how such an algorithm can be used for decentralized bootstrapping. ReCircle does not have any extra cost during normal maintenance compared to an isolated overlay maintenance algorithm. Furthermore, the algorithm is tunable to tradeoff between bandwidth consumption and time to convergence during extreme events like bootstrapping and handling network partitions and mergers. We evaluate the algorithm extensively under various scenarios through simulation and experimentation on Planet Lab.
{"title":"Dealing with Bootstrapping, Maintenance, and Network Partitions and Mergers in Structured Overlay Networks","authors":"T. Shafaat, A. Ghodsi, Seif Haridi","doi":"10.1109/SASO.2012.36","DOIUrl":"https://doi.org/10.1109/SASO.2012.36","url":null,"abstract":"In the last decade, numerous structured overlay networks were proposed as a scalable infrastructure to build large-scale distributed systems under dynamic environments. These overlays were touted to be fault-tolerant and self-managing, yet, as we show in this paper, they fall short of handling some extreme scenarios they envision. These scenarios include bootstrapping, and underlying network partitions and mergers. We argue that handling such extreme scenarios is fundamental to providing a fault-tolerant and self-managing system, and thus, structured overlay networks should intrinsically be able to handle them. In this paper, we present ReCircle, an overlay algorithm that apart from performing periodic maintenance to handle churn like any other overlay, can merge multiple structured overlay networks. We show how such an algorithm can be used for decentralized bootstrapping. ReCircle does not have any extra cost during normal maintenance compared to an isolated overlay maintenance algorithm. Furthermore, the algorithm is tunable to tradeoff between bandwidth consumption and time to convergence during extreme events like bootstrapping and handling network partitions and mergers. We evaluate the algorithm extensively under various scenarios through simulation and experimentation on Planet Lab.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multidisciplinary optimization (MDO) problems are a specific class of optimization problem where the number of variables and disciplines involved is to important to directly apply classical optimization methods. Most of the existing approaches concentrate on separating the problem in distinct sub problems and using standard optimization methods on these sub problems while trying to maintain consistency among the variables shared by the sub problems. Basically these methods try to help the user to find an optimization process which reduces the complexity of the problem. However, a shortcoming of these MDO methods is that they require a strong expert knowledge of both the problem to be solved and the method which is applied, in order to obtain interesting results.
{"title":"An Adaptive Multi-Agent System for Integrative Multidisciplinary Design Optimization","authors":"Tom Jorquera, J. Georgé, Christine Régis","doi":"10.1109/SASO.2012.42","DOIUrl":"https://doi.org/10.1109/SASO.2012.42","url":null,"abstract":"Multidisciplinary optimization (MDO) problems are a specific class of optimization problem where the number of variables and disciplines involved is to important to directly apply classical optimization methods. Most of the existing approaches concentrate on separating the problem in distinct sub problems and using standard optimization methods on these sub problems while trying to maintain consistency among the variables shared by the sub problems. Basically these methods try to help the user to find an optimization process which reduces the complexity of the problem. However, a shortcoming of these MDO methods is that they require a strong expert knowledge of both the problem to be solved and the method which is applied, in order to obtain interesting results.","PeriodicalId":126067,"journal":{"name":"2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：