Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

TLDR

The paper introduces the pigeon‑inspired optimization (PIO) algorithm and demonstrates its use for air robot path planning. The authors formulate the air robot path‑planning problem with threat resources and an objective function, then develop the PIO algorithm using map‑compass and landmark operators, and evaluate it against differential evolution. Experiments show that PIO is feasible, robust, and converges faster than DE, with superior global search performance across various cases.

Abstract

Purpose – The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems. Design/methodology/approach – The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted. Findings – The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases. Originality/value – In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.