Termite thermoregulation optimization (TTO): an HPC-oriented nature-inspired metaheuristic for global optimization
Journal of Supercomputing, cilt.82, sa.14, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 82 Sayı: 14
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11227-026-08846-2
- Dergi Adı: Journal of Supercomputing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Termite thermoregulation optimization, Nature-inspired metaheuristics, HPC, Optimization, Parallel processing
- Atatürk Üniversitesi Adresli: Evet
Özet
Metaheuristic algorithms have gained significant attention due to their ability to approximate global optima in complex optimization problems. In this study, a novel nature-inspired optimization method, termite thermoregulation optimization (TTO), is proposed based on the thermoregulation behavior of termites during nest construction. The algorithm models the dynamic balance between exploration and exploitation through a unified adaptive mechanism controlled by the Termite Colony Index, which integrates population diversity and convergence progress. TTO further incorporates directional derivative estimation, bioconvection-inspired oscillatory dynamics, and stochastic diffusion to enhance search capability. The proposed method is evaluated under a unified and fair experimental protocol using identical function-evaluation budgets across all algorithms. Performance is assessed on continuous benchmark functions (Sphere, Rosenbrock, Ackley, Griewank, Schwefel) and discrete Traveling Salesperson Problem instances from the Traveling Salesperson Problem Library, including berlin52, eil51, and kroA100 using a representation-level adaptation based on random-key encoding. Statistical analyses, including multiple independent runs and non-parametric tests, are conducted to ensure robustness and reliability. Experimental results demonstrate that TTO achieves competitive performance compared with established methods such as PSO, ACO, GA, SA, and LSHADE, although its relative convergence accuracy varies across problem types and dimensional settings. Because each TTO iteration performs an independent directional probe and candidate evaluation for every population member, its computational workload increases with population size and becomes particularly important when objective evaluation involves high-dimensional models, engineering simulations, or other computationally expensive processes. The individual-level independence of these operations provides a direct basis for parallel evaluation. In addition, the parallel implementation of TTO is investigated within a high-performance computing framework. The parallel results demonstrate workload-dependent behavior. Moderate speedup was obtained under sufficiently large population sizes, while parallel efficiency decreased as the number of worker processes increased. The identical objective values obtained across worker configurations confirm that the process-based implementation preserves solution quality. These findings position TTO as a parallelizable optimization framework for continuous and combinatorial problems, while its current multicore scalability remains limited by process-management and communication overhead.