Robustness is one of the most significant issues when designing evolutionary algorithms. These algorithms should be able to resist against fluctuating responses through the search process. In this paper, we aim at imp...
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The performance of the most existing classical evolutionary multiobjective optimization (EMO) algorithms, especially for Pareto-based EMO algorithms, generally deteriorates over the number of objectives in solving man...
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With the increasing complexity of real-world optimization problems, many challenges appear to evolutionary algorithms (EAs). When solving these time-consuming or high-complexity problems, although EAs can guarantee th...
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Clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper a two-level clustering approach is presented, combining a traditional gradient-based clustering technique with a...
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ISBN:
(纸本)9781450353557
Clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper a two-level clustering approach is presented, combining a traditional gradient-based clustering technique with an evolutionary optimization technique based on the Gravitational Search Algorithm (GSA), and targeting to improved performance in large-scale WSNs (where typical approaches usually lead to performance degradation). The proposed protocol initially creates energy-balanced multi-hop clusters, where the energy of the sensors increases progressively as getting closer to the cluster head (CH). In the second phase of the protocol an appropriate GSA-based evolutionary algorithm is executed in order to assign groups of CHs to specific 'gateways' for the final data forwarding to the base station (BS). The GSA fitness function is adequately defined taking in account both the distance from the CHs to the gateways and the BS as well as the residual energy of the gateways. Simulation results show the high performance of the proposed scheme as well as its superiority over the native GSA-based approach presented in the literature.
A multidisciplinary design, analysis and optimization (MDAO) tool for designing composite aircraft with performance adaptive aeroelastic wings is presented in this paper. The MDAO framework is applied for designing a ...
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Pulse compression is a very computationally intensive part of radar signal processing. This paper studies the design tradeoffs that are available when an optimisation algorithm is used to design non-linear frequency m...
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The concept of a large margin is central to support vector machines and it has recently been adapted and applied for nearest neighbour classification. In this paper, a modification of this method is proposed in order ...
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A cloud-distributed optimization algorithm applicable to large scale, constrained, multiobjective, optimization problems, such as steamflood redistribution, is presented. The proposed algorithm utilizes the so-called ...
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ISBN:
(纸本)9781510841970
A cloud-distributed optimization algorithm applicable to large scale, constrained, multiobjective, optimization problems, such as steamflood redistribution, is presented. The proposed algorithm utilizes the so-called Metamodel Assisted evolutionary Algorithm (MAEA) as its algorithmic basis. MAEAs use a generic implementation of an evolutionary algorithm as their main optimization engine and advanced machine learning techniques as metamodels. Metamodels are utilized through the application of an inexact pre-evaluation phase during the optimization, which substantially decreases the evaluations of the problem specific forward model. Additionally, a unification of global search (GS) and local search (LS) is achieved via the use of Lamarckian learning principles applied on top of a MAEA creating, in essence, a Metamodel Assisted Memetic Algorithm (MAMA). MAMAs profit from the abilities of MAEAs to explore the most promising regions of the design space without being trapped in local optima while also utilizing the efficiency of deterministic methods to further refine promising solutions located during GS. At the end of each EA generation, the most promising members of the current populations are selected to undergo LS using a gradient-based method. Further, integration with scalable cloud-distributed computing allows MAMAs (CD-MAMA) to perform rapid and simultaneous evaluation of tens of thousands of operating plans. The proposed algorithm has been statistically validated on two mathematical test cases and, subsequently, used to optimize a field undergoing steamflood under two different oil-price scenarios. Thus, demonstrating that, cloud-distributed MAMAs coupled with efficient reservoir models, allow for steamflood injection redistribution optimization in affordable, by industry, wallclock times (hours). For the field in question production comes from poorly consolidated sands within the Antelope Shale member of the Miocene Monterey formation with porosity averaging 30
MOEA/D is a novel multiobjective evolutionary algorithm based on decomposition approach, which has attracted much attention in recent years. However, when tackling the problems with irregular (e.g., disconnected or de...
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The problems related with urban fires brings destruction, casualties and economic issues to the country. On aiming to decrease the risk specially on populous areas, the development of classification and/or forecasting...
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