Next Article in Journal
An Optimization Algorithm Inspired by the Phase Transition Phenomenon for Global Optimization Problems with Continuous Variables
Previous Article in Journal
Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
Open AccessArticle

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
*
Author to whom correspondence should be addressed.
Algorithms 2017, 10(4), 120; https://doi.org/10.3390/a10040120
Received: 9 September 2017 / Revised: 11 October 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and “black-box” forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods. View Full-Text
Keywords: nature based optimization; artificial bee colony; firefly algorithm; cuckoo search; bat algorithm; flower pollination algorithm; grey wolf optimizer nature based optimization; artificial bee colony; firefly algorithm; cuckoo search; bat algorithm; flower pollination algorithm; grey wolf optimizer
Show Figures

Figure 1

MDPI and ACS Style

Saad, A.E.H.; Dong, Z.; Karimi, M. A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design. Algorithms 2017, 10, 120.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop