Multi-mode wave energy converter design optimisation using an improved moth flame optimisation algorithm

Mehdi Neshat, Nataliia Y. Sergiienko, Seyedali Mirjalili, Meysam Majidi Nezhad, Giuseppe Piras, Davide Astiaso Garcia

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-ofthe-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.

Original languageEnglish
Article number3737
Issue number13
Publication statusPublished - 1 Jul 2021


  • Bio-inspired
  • Evolutionary algorithms
  • Meta-heuristics
  • Moth Flame Optimisation
  • Optimisation algorithms
  • Power take-off
  • Renewable energy systems
  • Swarm intelligence
  • Wave energy converters


Dive into the research topics of 'Multi-mode wave energy converter design optimisation using an improved moth flame optimisation algorithm'. Together they form a unique fingerprint.

Cite this