Abstract
One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.
Highlights
Introduction published maps and institutional affilIn approximate methods, the guarantee of finding global optimum solutions is sacrificed due to the computational complexity of hard optimization problems
The literature review shows the contribution of machine learning (ML) works to improve the performance of metaheuristics as well as the contribution of metaheuristics to improve the performance of ML
In order to determine if the integration of QL as a binary scheme selector improves the results of the MH, five versions of QL have been implemented with different fixes, which have been named as indicated in the Table 1
Summary
The guarantee of finding global optimum solutions is sacrificed due to the computational complexity of hard optimization problems. Approximate algorithms can be classified as specific heuristics and MH. Heuristics are techniques designed to solve a particular problem. MH are defined as upper-level general methodologies (templates), which can be used as guiding strategies for the design of underlying heuristics for solving a problem [1]. MH extends basic heuristic methods by including them in an iterative framework, augmenting their exploration and exploitation capabilities. MH needs to establish a good ratio between exploration and exploitation to be successful. That means that designing and applying good MH is to make a proper trade-off between these two iations
Sign-in/Register to view highlights & summary 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.
