Design of Tubular Permanent Magnet Generators for Vehicle Energy Harvesting by Means of Social Network Optimization

Grimaccia, Francesco; Gruosso, Giambattista; Mussetta, Marco; Niccolai, Alessandro; Zich, R.E.

doi:10.1109/tie.2017.2756599

Cited by 34 publications

(17 citation statements)

References 30 publications

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“…The adopted optimization algorithm is Social Network Optimization, a population-based evolutionary algorithm that takes its inspiration from the information sharing process of Online Social Networks [33]. The flow chart of SNO can be found in [34].…”

Section: Physical Model Methodologymentioning

confidence: 99%

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

et al. 2018

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An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new "mixed method" which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively).

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Section: Physical Model Methodologymentioning

confidence: 99%

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

et al. 2018

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“…It is a novel population-based algorithm that takes its inspiration from the information-sharing process in online social networks [30]. The algorithm has already been applied to other engineering problems in which its effectiveness has been proven [17].…”

Section: Social Network Optimizationmentioning

confidence: 99%

“…To perform this optimization, a promising evolutionary optimization algorithm, named Social Network Optimization (SNO) [17], has been used. SNO is a recently-developed population-based algorithm: in the literature, it has been applied to antenna optimization [18], with a comparison between SNO and PSO; it has been also applied to model parameter matching problems [19].…”

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confidence: 99%

Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization

et al. 2019

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Wireless Sensor Networks (WSN) have been widely adopted for years, but their role is growing significantly currently with the increase of the importance of the Internet of Things paradigm. Moreover, since the computational capability of small-sized devices is also increasing, WSN are now capable of performing relevant operations. An optimal scheduling of these in-network processes can affect both the total computational time and the energy requirements. Evolutionary optimization techniques can address this problem successfully due to their capability to manage non-linear problems with many design variables. In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed.Wireless Sensor Networks (WSN) are relevant system architectures that can be applied in a wide range of applications [1], from monitoring to tracking, visual surveillance, ranging also in many fields from industrial automation to agricultural systems, and target localization, both in military and civil sectors [2].Currently, WSN are becoming much more important because the senors' computational capabilities are growing, and thus, many in-network tasks can be performed. It has been noticed that processing the information inside the network is faster and safer than sending raw data to the final user [3]. Such in-network processing can drastically reduce the total computational time required after sensing tasks with a direct impact on power consumption [4].The network complex dynamics created by these in-network operations can be suitably managed by means of evolutionary optimization techniques [5]. For instance, the problem of sensor lifetime maximization has been successfully approached in [6] by means of genetic algorithms and in [7] with genetical swarm optimization. The problem of coverage in WSN has been solved in [8] with both the genetic algorithm and ant colony optimization. Another important problem of WSN, routing, has been widely approached by several authors: in [9], a specific energy protocol has been designed to improve the network lifetime, while in [10], it has been solved with genetical swarm optimization and in [11] with particle swarm optimization.The problem of task and resource allocation is a crucial problem in many frameworks. It is aimed at finding the optimal distribution of the tasks inside the network itself with reference to a specific goal. In the case of the deployment of multiple sensor devices with batteries, a fundamental goal is to

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“…Dorronsoro et al [10] used evolutionary algorithm to model and solve minimization problems. Grimaccia et al [11] used social network optimization to design generators for vehicle energy harvesting.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

Zhang

et al. 2017

Mathematical Problems in Engineering

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Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

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Design of Tubular Permanent Magnet Generators for Vehicle Energy Harvesting by Means of Social Network Optimization

Cited by 34 publications

References 30 publications

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

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