Abstract
A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Although Reinforcement Learning (RL) is primarily developed for solving Markov decision problems, it can be used with some improvements to optimize mathematical functions. At the core of MORELA, a sub-environment is generated around the best solution found in the feasible solution space and compared with the original environment. Thus, MORELA makes it possible to discover global optimum for a mathematical function because it is sought around the best solution achieved in the previous learning episode using the sub-environment. The performance of MORELA has been tested with the results obtained from other optimization methods described in the literature. Results exposed that MORELA improved the performance of RL and performed better than many of the optimization methods to which it was compared in terms of the robustness measures adopted.
Highlights
If f ( x) is a function of decision variables, where x ∈ S, S is the feasible search space and S ⊆ Rn, an optimization problem can be defined as finding the value of xbest in S that makes f ( x) optimal for all x values
We present a MOdified REinforcement Learning Algorithm (MORELA) approach which differs from Reinforcement Learning (RL) based approaches by means of generating a sub-environment based on the best solution obtained so far which is saved to prevent the search being trapped at local optimums
The findings indicate that the MORELA showed remarkable performance for all of the test functions except F3, for which theoretical global optimum could not be found with the required accuracy, namely, 0
Summary
If f ( x) is a function of decision variables, where x ∈ S , S is the feasible search space and S ⊆ Rn , an optimization problem can be defined as finding the value of xbest in S that makes f ( x) optimal for all x values. C. Ozan et al 66 search approach [5], modified firefly algorithm [6] and hybrid heuristic methods [7] [8] [9] have been proposed to optimize any given mathematical function. Ozan et al 66 search approach [5], modified firefly algorithm [6] and hybrid heuristic methods [7] [8] [9] have been proposed to optimize any given mathematical function Apart from these applications, there are some attempts for solving engineering problems in different fields using RL based algorithms in the relevant literature. We present a MOdified REinforcement Learning Algorithm (MORELA) approach which differs from RL based approaches by means of generating a sub-environment based on the best solution obtained so far which is saved to prevent the search being trapped at local optimums.
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.
