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

Abstract

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