Effective anti-submarine decision support system based on heuristic rank-based Dijkstra and adaptive threshold partitioning mechanism

Abstract

Submarines possess strong covert striking capabilities, making anti-submarine warfare (ASW) a global naval priority. The Hidden Markov Anti-Submarine Model (HMASM) finds crucial applications in dynamic and uncertain ASW. The model delineates ASW into two phases: partitioning and search path planning. Current partitioning algorithms often include cells leading to redundant and competitive search, determined empirically. Additionally, in HMASM, the path planning algorithms based on genetic algorithm (GA) are time-consuming and exhibits unstable performance. This paper proposes a novel solution to these issues. For partitioning, a Reassigned k-Nearest Neighbour (RKNN) algorithm is introduced, identifying and reallocating units causing repeated and competing searches. For search path planning, heuristic rules for Dijkstra's cost function and goal point selection transform the NP-hard problem of maximizing the expected number of detections (ED) into a deterministic algorithm-compatible form. A heuristic rank-based selection model, Rank-Based Dijkstra under the Hidden Markov Model (HMM-R-Dijkstra), considering distance and probability, is added to Dijkstra. Furthermore, an Adaptive Threshold Partitioning (ATP) dynamically monitors searcher exploration by setting variables and thresholds to determine optimal partitioning timing, preventing untimely and excessive partitioning. Combining RKNN, HMM-R-Dijkstra, and ATP forms R-HRD-ATP, optimizing all parameters using parallel structures. Through three comparative experiments, R-HRD-ATP's performance steadily improves. Experiments comparing R-HRD-ATP to GA, Sparrow Search Algorithm (SSA)-GA, and Ant Colony Optimization (ACO)-GA reveal performance enhancements of 25–70.99% and time savings of 56–295 times for our model. Importantly, no parameter adjustments are required. The success of R-HRD-ATP indicates that its heuristic rules can support the establishment of robust applications of deterministic path planning algorithms in HMASM.