Abstract
Metaheuristic algorithms are constructed to solve optimization problems, but they cannot solve all the problems with best solutions. This work proposes a novel self-adaptive metaheuristic optimization algorithm, named Optimal Stochastic Process Optimizer (OSPO), which can solve different kinds of optimization problems with promising performance. Specifically, OSPO regards the procedure of optimization as a realization of stochastic process, and with the help of Subjective Probability Distribution Function (SPDF) and Receding Sampling Strategy proposed in this paper, OSPO can control the exploration-exploitation property online by the adaptive modification of the parameters in SPDF. This adaptive exploration-exploitation property of OSPO contributes to dealing with different kinds of problems; thus, it makes OSPO have the potential to solve at least a vast majority of optimization problems. The proposed algorithm is first benchmarked on uni-modal, multi-modal and composite test functions both in low and high dimensions. The results are verified by comparative studies with seven well-performed metaheuristic algorithms. Then, 21 real-world optimization problems are used to further investigate the effectiveness of OSPO. The winners of CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS algorithm, sCMAgES algorithm, EnMODE algorithm and COLSHADE algorithm are used as four comparative algorithms in real-world optimization problems. The analysis of simulations demonstrates that OSPO is able to provide very competitive performance compared to the comparative meta-heuristics both in benchmark functions and in real-world optimization problems; thus, the potential of OSPO to solve at least a vast majority of optimization problems is verified. A corresponding MATLAB APP demo is available on https://github.com/JiahongXu123/OSPO-algorithm.git.
Highlights
Nature is the source of metaheuristic algorithms, and researchers tend to mimic different creatures or natural phenomena to get all kinds of metaheuristic algorithms to solve various optimization problems
A novel metaheuristic algorithm with adaptive explorationexploitation property named Optimal Stochastic Process Optimizer (OSPO) is proposed in this study
The OSPO algorithm regards the optimization procedure as the realization of optimizing stochastic process, and OSPO constructs optimal sample paths to seek the global optimum of the optimization problem
Summary
Nature is the source of metaheuristic algorithms, and researchers tend to mimic different creatures or natural phenomena to get all kinds of metaheuristic algorithms to solve various optimization problems. Many researchers focus on coming up with new metaheuristics which have new exploration-exploitation properties In this way, people can choose an algorithm with suitable habit (the fittest exploration-exploitation property) to deal with a specific optimization problem (a specific habitat) in order to obtain best solutions. We propose a novel adaptive metaheuristic algorithm called Optimal Stochastic Process Optimizer (OSPO), which uses positive thinking to control exploration-exploitation property directly during the optimization. The adaptive modification of parameters in Subjective Probability Distribution Function (SPDF) makes the sample path of the stochastic process become a searching trajectory, and the Receding Sampling Strategy resets the exploration-exploitation property in order to find the global optimum of the problem. The simulation results of benchmark functions and real-world optimization problems demonstrate that, thanks to the adaptive modification of exploration-exploitation property, OSPO can solve different problems with competitive performance compared to other metaheuristic algorithms.
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.
