Comparative study on metaheuristic algorithms for optimising wave energy converters

Abstract

The optimisation of WECs is a focus current research. With the increase in optimisation variables, traditional optimisation methods are insufficient to find the best design in a wide and complex solution space, and more advanced algorithms are required. In addition, the iterative optimisation of the WEC requires a reliable and accurate WEC simulation method to ensure the effectiveness of optimisation. Therefore, a MATLAB-APDL-AQWA united simulation system (MAA-system) is developed to calculate the WEC generation power accurately. Then, a memory mechanism is designed to avoid calculating the fitness of duplicate individuals in the search process by creating a memory base to store individual coding information and fitness information. Seven metaheuristic algorithms — the genetic algorithm, differential evolution, immune algorithm (IA), covariance matrix adaptation evolution strategy (CMA-ES), particle swarm optimisation, ant colony optimisation, and simulated annealing — with memory mechanisms are utilised to optimise the draft, power take-off parameter, and layout of WEC globally in random wave conditions. The performances of these algorithms in different search spaces are analysed. The results show CMA-ES is the best choice for solving the unimodal optimisation problem of WEC, and IA is more suitable for multimodal optimisation such as the WEC array layout.