The Hybrid Particle Swarm Optimization-Whale Optimization Algorithm-Sailfish Optimizer (HPSO-WOA-SFO) is proposed for solving multi-obstacle discrete road path planning. This paper proposes to utilize the advantage of the two-population update iteration of the sailfish algorithm to integrate the PSO and WOA into the SFO to enhance its exploitation ability and exploration ability, respectively. Meanwhile, the two communication mechanisms between the two populations of the SFO are studied in depth, and their algorithmic advantages and application scenarios are analyzed. Comparative experiments with four representative path planning algorithms and ablative experiments involving HPSO-WOA-SFO are conducted. The results demonstrate that, on average, HPSO-WOA-SFO outperforms the comparative algorithms by 21.40% in terms of global optimal convergence accuracy and is 10.71% faster in terms of convergence speed. Moreover, the proposed algorithm rapidly escapes local optima and enhances global optimality by 17.47% when trapped in local optima during the optimization process.