Abstract
Engineering design optimization in real life is a challenging global optimization problem, and many meta-heuristic algorithms have been proposed to obtain the global best solutions. An excellent meta-heuristic algorithm has two symmetric search capabilities: local search and global search. In this paper, an improved Butterfly Optimization Algorithm (BOA) is developed by embedding the cross-entropy (CE) method into the original BOA. Based on a co-evolution technique, this new method achieves a proper balance between exploration and exploitation to enhance its global search capability, and effectively avoid it falling into a local optimum. The performance of the proposed approach was evaluated on 19 well-known benchmark test functions and three classical engineering design problems. The results of the test functions show that the proposed algorithm can provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence rate. The results of the engineering problems prove that the new approach is applicable to challenging problems with constrained and unknown search spaces.
Highlights
Real-world engineering design optimization problems are very challenging to find the global optimum of a highly complex and multiextremal objective function, involving many different decision variables under complex constraints [1,2]
GWO each provide the best results for three of these problems. This is due to the co-evolutionary technology that are adopted between the Butterfly Optimization Algorithm (BOA) and CE operators to enhance exploitation
These results prove that the BOA-CE algorithm has an excellent exploration which helps it to explore the promising regions of the search space
Summary
Real-world engineering design optimization problems are very challenging to find the global optimum of a highly complex and multiextremal objective function, involving many different decision variables under complex constraints [1,2]. Lbi ≤ xi ≤ ubi , i = 1, 2, · · · , n, (4). Lbi and ubi represent the lower and upper bound of the value of xi. Most of the constraints of the global optimization problem are nonlinear Such nonlinearity often results in a multimodal response landscape [3]. Many researchers have developed various derivative-free global optimization methods for it. These methods can be divided into two classes: deterministic methods and stochastic meta-heuristic algorithms [4,5].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
