Advanced orthogonal opposition‐based learning‐driven dynamic salp swarm algorithm: Framework and case studies

Salp swarm algorithm (SSA) is a relatively new bio-inspired meta-heuristic optimization that mimics the navigating and foraging behavior of salps in oceans. This paper presents an orthogonal quasi-opposition-based learning-driven dynamic SSA (OBDSSA) for solving global problems. The proposed methodology integrates learning (OQOBL) tactic (DL) strategy with to improve its performance. OQOBL technique used enrich exploration development capability canonical help salp chain jump out local optimum, while DL mechanism applied basic approach expand neighborhood searching capabilities search agents. To investigate operators OBDSSA algorithm, 18 widely benchmark functions parameter extraction problem photovoltaic (PV) model have been experimented upon. comparisons reveal outperforms all competitors, including standard SSA, variants, other state-of-the-art algorithms. Finally, developed path planning obstacle avoidance (PPOA) tasks autonomous mobile robots (AMR) satisfactory results are obtained.