Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Tenant-based Optimization Algorithm: A Human-inspired Metaheuristic for Global Optimization and Engineering Design Problems. |
| Συγγραφείς: |
Montazeri, Zeinab, Dehghani, Mohammad, Mahdi, Ali Jafer, Smerat, Aseel, Humod, Abdulrahim Thiab, Malik, Om Parkash, Eguchi, Kei |
| Πηγή: |
International Journal of Intelligent Engineering & Systems; 2026, Vol. 19 Issue 6, p1072-1093, 22p |
| Θεματικοί όροι: |
Global optimization, Engineering design, Mathematical models, Optimization algorithms, Benchmark problems (Computer science), Metaheuristic algorithms |
| Περίληψη: |
A new human-based metaheuristic algorithm called the Tenant-Based Optimization Algorithm (TBOA) is introduced and designed. The main idea of this algorithm is inspired by the strategies of a tenant when searching for a suitable house in different areas of a city. This behavior includes two essential stages: a global search across various city regions to identify promising areas, and a precise local search within a selected region to choose the best option. These two strategies are modeled in the algorithm as the exploration and exploitation phases, respectively, creating an effective balance between global and local search. The theoretical concepts of the algorithm are fully explained and implemented in the form of a population-based mathematical model. By simulating the wide search of tenants, the exploration phase allows an effective investigation of the search space and helps to prevent premature convergence. On the other hand, the exploitation phase focuses on a promising region and accelerates the convergence process toward the optimal solution. To evaluate its performance, the TBOA algorithm is tested on 23 standard benchmark functions, including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. The results are compared with nine well-known metaheuristic algorithms. The simulation results clearly show that TBOA achieves the first-best rank in all 23 benchmark functions, demonstrating a 100% success rate, and provides stable and superior performance compared to the competing algorithms. Furthermore, to examine the effectiveness of the algorithm in real-world applications, TBOA is applied to four constrained engineering design problems. The results of these experiments also indicate that TBOA obtains the first-best rank in all four engineering design problems, achieving a 100% success rate when compared with the competing algorithms. Overall, the findings of this research demonstrate that the TBOA algorithm, with its strong capabilities in exploration, exploitation, and maintaining a proper balance between them, can be considered a powerful, stable, and reliable optimizer. Its conceptual simplicity, ease of implementation, and superior performance on both benchmark and real-world problems make it a suitable candidate for solving a wide range of optimization problems in science and engineering. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |