Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.17.9.7
Chaotic Slime Mould Metaheuristic with Chebyshev Map for IoT Service Composition
Amal Halfaoui
; LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou-Bakr Belkaid, Tlemcen, Algeria
*
Fethallah Hadjila
; LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou-Bakr Belkaid, Tlemcen, Algeria
Zeyneb ElYebdri
; LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou-Bakr Belkaid, Tlemcen, Algeria
* Dopisni autor.
Sažetak
The rapid expansion of the Internet of Things (IoT) has led to a proliferation of services offering identical functionalities with heterogeneous capabilities and quality levels. Efficiently composing these IoT services to satisfy user requirements while maintaining system performance under strict Quality of Service constraints, such as energy consumption, remains a challenging combinatorial optimization problem. Metaheuristic algorithms have proven effective in addressing such large-scale optimization tasks. This paper first proposes an energy consumption model for IoT services. It then proposes two novel discrete versions of the Slime Mould Algorithm (SMA) specifically designed for IoT service composition. The first, called Discrete Slime Mould Algorithm (DSMA), adapts SMA to the discrete domain using adaptive fitness-based weights, probabilistic gene copying from the best solution, and an adaptive mutation mechanism. The second, called Chaotic Discrete Slime Mould Algorithm (CDSMA), further integrates Chebyshev chaotic maps for population initialization and weight updating to improve population diversity and accelerate convergence. Experimental results show that CDSMA consistently outperforms DSMA and other classical metaheuristics. CDSMA achieves an optimality rate of 96.33% with a small population size and 99.92% with a moderate population size. Furthermore, extensive experiments over 30 independent runs confirm that both proposed algorithms significantly outperform classical metaheuristics. CDSMA achieves 96.46% (±0.65) optimality, DSMA achieves 93.75% (±0.75), significantly outperforming SMA (84.74%), GWO (82.54%), HHO (82.87%), and WOA (80.15%) with statistical significance (t-test, p < 10-20).
Ključne riječi
Internet of Things (IoT); Energy consumption; IoT Service Composition; Slime Mould Algorithm (SMA); Chaotic Mapping; Chebyshev maps;
Hrčak ID:
352144
URI
Datum izdavanja:
14.10.2026.
Posjeta: 0 *