Abstract
Abstract Differential Evolution (DE) has been applied to many scientific and engineering problems for its simplicity and efficiency. However, the standard DE cannot be used in a binary search space directly. This paper proposes an adaptive binary Differential Evolution algorithm, or ABDE, that has a similar framework as the standard DE but with an improved binary mutation strategy in which the best individual participates. To further enhance the search ability, the parameters of the ABDE are slightly disturbed in an adaptive manner. Experiments have been carried out by comparing ABDE with two binary DE variants, normDE and BDE, and the most used binary search technique, GA, on a set of 13 selected benchmark functions and the classical 0-1 knapsack problem. Results show that the ABDE performs better than, or at least comparable to, the other algorithms in terms of search ability, convergence speed, and solution accuracy.
Highlights
Differential Evolution (DE), which was first proposed over 1994-1996 by Storn and Price at Berkeley, is a simple yet powerful evolutionary algorithm [1,2,3,4,5]
It is easy to use in optimizing problems. Most researchers focus their attention on DE in continuous optimization applications and a lot of improved variants of DE have been presented recently such as Self-adaptive Differential Evolution (SaDE)[16], Fuzzy Adaptive Differential Evolution (FADE)[17], Adaptive DE with Optional Archive(JADE)[18], jDE [19, 20] etc
In order to demonstrate the effects of our mutation strategy on improving the performance of ABDE, the ABDE without parameters adaptation (ABDE_W) was simulated
Summary
Differential Evolution (DE), which was first proposed over 1994-1996 by Storn and Price at Berkeley, is a simple yet powerful evolutionary algorithm [1,2,3,4,5]. For easy of hardware-implementation, continuous optimization problems are usually solved in a binary number space. Gong and Tuson proposed a binary Differential Evolution (BDE) algorithm in [23] where the continuous difference between two individuals in standard DE is represented by a hamming distance in the binary search space. We proposed a novel binary DE algorithm with parameters adaptation (ABDE) based on DE/best/1/bin strategy of the standard DE for both continuous and discrete problems optimization. Compared with Genetic Algorithm (GA), BDE and normDE, the proposed ABDE algorithm shows a better optimization performance on a set of test problems.
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 callMore From: International Journal of Computational Intelligence Systems
Disclaimer: 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.
