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A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
- Publication Year :
- 2019
-
Abstract
- Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this work, we explore the optimisation of WEC arrays consisting of a three-tether buoy model called CETO. Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys in farms and also the power-take-off (PTO) parameters for each buoy. The search space for these parameters is complex and multi-modal. Moreover, the evaluation of each parameter setting is computationally expensive -- limiting the number of full model evaluations that can be made. To handle this problem, we propose a new hybrid cooperative co-evolution algorithm (HCCA). HCCA consists of a symmetric local search plus Nelder-Mead and a cooperative co-evolution algorithm (CC) with a backtracking strategy for optimising the positions and PTO settings of WECs, respectively. Moreover, a new adaptive scenario is proposed for tuning grey wolf optimiser (AGWO) hyper-parameter. AGWO participates notably with other applied optimisers in HCCA. For assessing the effectiveness of the proposed approach five popular Evolutionary Algorithms (EAs), four alternating optimisation methods and two modern hybrid ideas (LS-NM and SLS-NM-B) are carefully compared in four real wave situations (Adelaide, Tasmania, Sydney and Perth) with two wave farm sizes (4 and 16). According to the experimental outcomes, the hybrid cooperative framework exhibits better performance in terms of both runtime and quality of obtained solutions.<br />Information Sciences (2020)
- Subjects :
- FOS: Computer and information sciences
Information Systems and Management
position optimisation
Computer science
Evolutionary algorithm
adaptive gray wolf optimiser
power take off system
Scale (descriptive set theory)
02 engineering and technology
Theoretical Computer Science
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Wave farm
Local search (optimization)
Neural and Evolutionary Computing (cs.NE)
wave energy converters
Power take-off
business.industry
Backtracking
05 social sciences
cooperative co-Evolution algorithms
050301 education
Computer Science - Neural and Evolutionary Computing
renewable energy
Computer Science Applications
Renewable energy
Control and Systems Engineering
020201 artificial intelligence & image processing
business
0503 education
Algorithm
Software
Energy (signal processing)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....a06c41cee06a7ab873045ae4d805a61b