Abstract
This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.
Highlights
In the era of advanced technological development, the complexity of implementing real-time processes and applications requires effective metaheuristic optimization tuning techniques to meet the global demands with more reliability [1]
The traditional optimization techniques determine the local minima based on the analytical calculations using the relevant processes models, which generally produce a single optimal solution at each run [2,3]
The sine cosine algorithm (SCA) faces the problem of premature convergence in both stages, and this issue is solved by combining the chaotic-based search area mechanism and the cultural algorithm technique termed chaotic cultural SCA [18]
Summary
In the era of advanced technological development, the complexity of implementing real-time processes and applications requires effective metaheuristic optimization tuning techniques to meet the global demands with more reliability [1]. Most metaheuristic optimization techniques identify the optimum solution area by incorporating the two critical search phases: exploration and exploitation [12]. The exploration stage performs the function of finding the desired local optimal solution in the global search area. The exploitation phase will serve the different possible solutions in the desired search area [13] This process improves the effective search area selection and local optima improvement. The SCA faces the problem of premature convergence in both stages, and this issue is solved by combining the chaotic-based search area mechanism and the cultural algorithm technique termed chaotic cultural SCA [18]. Some hybrid optimization techniques were developed by combining SCA with other algorithms to improve the local search problems. Using the SCA’s faster local search mechanism and best position identification ability of the AOA, a new hybridized arithmetic-trigonometric optimization algorithm (ATOA) is designed to faster.
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