TEAM Workshop Problem 25: A Multiobjective Analysis

Lebensztajn, Luiz; Coulomb, Jean‐Louis

doi:10.1109/tmag.2004.824545

Cited by 24 publications

(11 citation statements)

References 6 publications

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“…To better analyze GSO performances over a magnetic application, the authors tested it on a benchmark optimization test case known as TEAM problem 25, whose definition is reported in [26]. Related results of benchmark analysis are reported in Figure 4 which shows the average cost function values found during iterations, highlighting a faster speed of convergence of the considered GSO implementations with respect to GA and PSO.…”

Section: Harvesting Device Optimizationmentioning

confidence: 99%

Optimization of an energy harvesting buoy for coral reef monitoring

Pirisi

Grimaccia

Mussetta

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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The sustainable management of coastal and offshore ecosystems, such as for example coral reef environments, requires an energy efficient collection of accurate data across various temporal and spatial scales. To suitably address the energy supply of marine sensors, in this paper a novel energy harvesting device is proposed, based on a Tubular Permanent MagnetLinear Generator (TPM-LiG). The application is related to the sea wave energy conversion for small sensorized buoy. The optimization process is developed by means of evolutionary computation techniques. The advantage of these algorithms is in the wide exploration of the variables space and in the effective exploitation of the fitness function. The algorithm has been tested on a benchmark case and then applied to the optimization of a power-buoy prototype which has been realized in laboratory with potential significant implications in future marine environment applications

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Section: Harvesting Device Optimizationmentioning

confidence: 99%

Optimization of an energy harvesting buoy for coral reef monitoring

Pirisi

Grimaccia

Mussetta

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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“…The number of points (n) is equal to 10 along this line. Two others functions were proposed on [13]. Both functions are also calculated on the 10 prescribed points along the line e-f and measure local errors: …”

Section: The Analyzed Problemsmentioning

confidence: 99%

“…From previous experience, L3 was not taken into account in the optimization process, because its impact on objective functions is not important. A finite element field computation was performed to calculate the objective functions and a kriging model was used to replace the objective functions, as proposed in [13]. We solved this problem to observe the MS2PSO ability to solve a multiobjective problem with more than 2 objectives.…”

Section: The Analyzed Problemsmentioning

confidence: 99%

Particle Swarm Optimization and strength pareto to solve multiobjective optimization problems

Barbosa

Coelho

Lebensztajn

2013

JAE

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Many attempts have been made to extend Particle Swarm Optimization to solve Multiobjective Problems. In this paper, the Strength Pareto concept, proposed by Zitzler, is incorporated to a Particle Swarm Optimization to build a new approach to solve Multiobjective Optimization Problems, the Multiobjective Strength Pareto Particle Swarm Optimization. Numerical results are reported to validate the proposed method to solve some unconstrained benchmarks problems and a constrained design of a brushless direct current motor.

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“…Moreover, the authors proposed also two different adaptive rules, namely dynamic and self-adaptive, in order to combine in the most effective way the properties of the GA and the PSO approaches also for unknown problems. In particular, the proposed method has been tested and validated here on a benchmark magnetic case called TEAM 25 problem [28], as shown in [29].…”

Section: Automated Design Proceduresmentioning

confidence: 99%

Novel Speed Bumps Design and Optimization for Vehicles' Energy Recovery in Smart Cities

Pirisi¹,

Grimaccia

Mussetta

et al. 2012

Energies

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Recently the technology development and increasing amounts of investment in renewables has led to a growing interest towards design and optimization of green energy systems. In this context, advanced Computational Intelligence (CI) techniques can be applied by engineers to several technical problems in order to find out the best structure and to improve efficiency in energy recovery. This research promises to give new impulse to using innovative unconventional renewable sources and to develop the so-called Energy Harvesting Devices (EHDs). In this paper, the optimization of a Tubular Permanent Magnet-Linear Generator for energy harvesting from vehicles to grid is presented. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment and transportation systems. Finally, an experimental validation of the designed EHD prototype is presented.

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TEAM Workshop Problem 25: A Multiobjective Analysis

Cited by 24 publications

References 6 publications

Optimization of an energy harvesting buoy for coral reef monitoring

Optimization of an energy harvesting buoy for coral reef monitoring

Particle Swarm Optimization and strength pareto to solve multiobjective optimization problems

Novel Speed Bumps Design and Optimization for Vehicles' Energy Recovery in Smart Cities

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