Evaluating dual-strategy salp swarm optimization on synthetic benchmarks and ALS disease progression

The Salp Swarm Algorithm (SSA) is a well-established swarm intelligence metaheuristic whose performance is fundamentally constrained by two structural deficiencies: a passive follower update in which each salp averages only with its immediate predecessor, causing slow propagation of global-best information and insufficient directed exploitation; and a complete absence of stagnation detection or diversity-recovery, leaving the swarm permanently trapped once population diversity collapses. This paper proposes two philosophically distinct and structurally complementary variants that directly target each deficiency. Weighted Follower Guidance (SSA-WFG) replaces the standard uniform follower update with a rank-based, socially aware rule: front-rank followers receive a strong attraction toward the global best solution, accelerating exploitation, while rear-rank followers retain conservative movement as a diversity reservoir – a heterogeneous structure absent from the original SSA. Dynamic Swarm Restructuring (SSA-DSR) augments the standard SSA with an event-triggered stagnation-recovery mechanism the original algorithm entirely lacks: when a stagnation counter exceeds a threshold, the lowest-fitness salps are re-initialized to random positions while elite solutions are preserved, injecting targeted diversity precisely when standard dynamics have failed. Both modifications preserve the O (L N d) time complexity of the original SSA. Evaluated on 23 classical benchmark functions and the CEC 2020 suite, SSA-WFG achieves the best Friedman rank on classical landscapes (Wilcoxon p < 0. 05), while SSA-DSR attains the highest average rank on 9 of 10 CEC 2020 functions, confirming complementary, landscape-dependent superiority over standard SSA. On the PRO-ACT Amyotrophic Lateral Sclerosis (ALS) progression dataset, both variants reduce MLP mean squared error by approximately 71. 5% relative to standard SSA, with run-to-run standard deviations two to three orders of magnitude smaller. Following wrapper-based feature selection, SSA-DSR exhibits exceptional biomarker reproducibility (std 1. 1 features vs. 6. 7 for standard SSA across 21 independent runs). The proposed dual-strategy framework provides a principled and efficient methodology for metaheuristic optimization on high-dimensional, noisy real-world problems where gradient-based methods and unmodified SSA are inadequate.