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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Cendekia : Jurnal Pendidikan Matematika JTIK (Jurnal Teknik Informatika Kaputama) JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Abdimas Singkerru JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Kocenin Jurnal Pengabdian Kepada Masyarakat
Joko Riyono
Universitas Trisakti
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Convergence and Empirical Performance of Tanh-Based Adaptive Particle Swarm Optimization Joko Riyono; Aina Latifa Riyana Putri; Sofia Debi Puspa; Supriyadi Supriyadi; Christina Eni Pujiastuti; Fayza Nayla Riyana Putri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7247

Abstract

Particle Swarm Optimization (PSO) is a widely used population-based optimization method but faces challenges in premature convergence, leading to suboptimal solutions. To address this issue, this study proposes a Tanh-Based Acceleration Coefficient PSO (TB-PSO), where the acceleration coefficients are modified using the hyperbolic tangent (tanh) function. The smooth and continuous behavior of tanh enables gradual coefficient updates, limits excessive particle velocities, and maintains swarm diversity, thereby improving convergence stability and balancing exploration and exploitation. The convergence theorem analysis confirms that TB-PSO meets stability criteria before being evaluated on unimodal and multimodal benchmark functions in 10 and 30 dimensions. Its performance is compared against several PSO variants, including TVAC-PSO, SCAC-PSO, NDAC-PSO, and SAC-PSO. In the 10-dimensional experiments, TB-PSO achieves the best overall final ranking based on the average and standard deviation of best solution, ranking first for functions f₃ and f₅, second for f₂ with only a marginal difference from the best-performing method, and remaining competitive for f₁ and f₄. These results indicate superior solution quality and stable convergence. For the 30-dimensional benchmark functions, TB-PSO ranks first for f₂, second for f₅, and third for f₁, f₃, and f₄ based on the same evaluation criteria. Although its ranking decreases compared to the 10-dimensional case, TB-PSO remains competitive, reflecting the increased complexity of high-dimensional optimization problems. Overall, the results demonstrate that the tanh-based acceleration coefficient modification effectively enhances PSO performance, particularly in lower-dimensional search spaces, while maintaining robustness in higher-dimensional scenarios.
Co-Authors Aina Latifa Riyana Putri Christina Eni Pujiastuti Christina Eni Pujiastuti Dody Prayitno E. Shintadewi Julian Fani Puspitasari Fayza Nayla Riyana Putri Fayza Nayla Riyana Putri Rosyida Permatasari Sofia Debi Puspa Sofia Debi Puspa Supriyadi Supriyadi Syaifudin