Abstract
In this paper, we propose a triple comparison-based interactive differential evolution (IDE) algorithm and a differential evolution (DE) algorithm. The comparison of target vector and trial vector supports a local fitness landscape for IDE and DE algorithms to conduct a memetic search. In addition to the target vector and trial vector used in canonical IDE and DE algorithm frameworks, we conduct a memetic search around whichever vector has better fitness. We use a random number from a normal distribution generator or a uniform distribution generator to perturb the vector, thereby generating a third vector. By comparing the target vector, the trial vector, and the third vector, we implement a triple comparison mechanism in IDE and DE algorithms. Our proposed triple comparison-based IDE and DE algorithms show significantly better optimisation performance arising from the evaluation results. We also investigate potential issues arising from our proposal and discuss some open topics and future opportunities.
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 Ad Hoc and Ubiquitous Computing
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.
