The majority of real-world problems involve not only finding the optimal solution, but also this solution must satisfy one or more constraints. Differential evolution (DE) algorithm with constraints handling has been ...
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The Univariate Marginal Distribution Algorithm (UMDA), a popular estimation of distribution algorithm, is studied from a run time perspective. On the classical OneMax benchmark function, a lower bound of Ω(μ√n + n ...
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Ant colony algorithm is a kind of bionic evolutionary algorithm, which is widely used in the field of optimization. Membrane computing is a new computing model, which has the characteristics of distributed, maximal pa...
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In this paper, we propose a novel approach to ensuring safety while planning and controlling an operation of swarms of drones. We derive the safety constraints that should be verified both during the mission planning ...
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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.
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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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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