Abstract
When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). Moreover, we propose a novel hybrid algorithm called the divisional model genetic algorithm (DMGA) to improve efficiency. By adding different methods, namely particle swarm optimization (PSO), pattern search (PS) and targeted mutation (TM) in three subpopulations to become “divisions,” each division has unique functionalities. With the collaboration of these three divisions, this method is considerably more efficient in solving multiple benchmark problems compared with other hybrid algorithms. These results reveal the superiority of DMGA in solving structural optimization problems.
Highlights
Offshore wind energy has become a pertinent source of renewable energy worldwide
Despite fatigue usually being the design-driving load case for jacket structures, this study focuses on the performance of optimization algorithms
The results indicate that the divisional model genetic algorithm (DMGA)’s mass descended slower than that of the pattern search (PS)
Summary
Offshore wind energy has become a pertinent source of renewable energy worldwide. The material cost of offshore wind substructures (OWSs) accounts for approximately 17% of the total cost of installation [1]. Reducing the mass of the structure is a primary objective of optimization for reducing cost. Safety constraints are a fundamental concern to make optimizing OWSs a multi-objective task. Because analyzing offshore wind turbines (OWTs) must consider multiple design loads, namely nonlinear wind, wave, and gravity loads, optimizing OWSs has been recognized as a highly nonlinear problem with complex computation [2]. Calculating objective function is based on finite element analysis (FEA) without an analytical form, seen as “black-box optimization.”
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