Trajectory Design of Re-entry Vehicles using combined Pigeon Inspired Optimization and Orthogonal Collocation method

Sushnigdha, Gangireddy; Joshi, Ashok

doi:10.1016/j.ifacol.2018.05.110

Cited by 12 publications

(2 citation statements)

References 12 publications

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“…A variant of PIO called predator-prey PIO (PPPIO) was proposed to solve unmanned combat aerial vehicle three-dimensional (3D) path-planning problems in a dynamic environment; the comparative simulation results showed that the PPPIO algorithm was more efficient than basic PIO, PSO, and DE [5]. For more applications of PIO and a comparison of its performance with other bio-inspired algorithms, please refer to [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Theoretical analysis of the convergence property of a basic pigeon-inspired optimizer in a continuous search space

Zhang

Huang

et al. 2019

Sci. China Inf. Sci.

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The pigeon-inspired optimization (PIO) algorithm is a newly presented swarm intelligence optimization algorithm inspired by the homing behavior of pigeons. Although PIO has demonstrated effectiveness and superiority in numerous fields, particularly in practical engineering optimization, there have been few results concerning the theoretical foundations of PIO. This paper conducts convergence analysis of basic PIO in a continuous search space in two aspects. First, we analyze the convergence of each pigeon's expected position using a difference equation and prove that the average position of each pigeon in the swarm will converge to the same value. To further study the stochastic global convergence property of the pigeon swarm, we apply the martingale theory to investigate the basic PIO swarm sequence, and achieve a sufficient condition to guarantee global convergence of the basic PIO. Our theoretical analysis shows that this convergence depends upon the accumulation of the minimum probability with which the pigeon swarm jumps to the global-optimal region at each iteration. The mathematical methods proposed in this study, particularly the martingale technique, also provide a new effective approach for the theoretical analysis of bio-inspired algorithms in continuous optimization.

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Section: Introductionmentioning

confidence: 99%

Theoretical analysis of the convergence property of a basic pigeon-inspired optimizer in a continuous search space

Zhang

Huang

et al. 2019

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Chan [3] has developed an approximate method by the summation of infinite series. Sushnigdha and Joshi [4] presented an orthogonal collocation-based entry trajectory solution strategy using pigeon-inspired optimization (PIO). Li and Chou [5] presented a self-adaptive learning particle swarm optimization (PSO) for mobile robot path planning in 2D environments.…”

mentioning

confidence: 99%

Spacecraft formation reconfiguration trajectory planning with avoidance constraints using adaptive pigeon-inspired optimization

Hua

Huang

et al. 2019

Sci. China Inf. Sci.

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Genetic Algorithm-Based Re-Entry Trajectory Optimization and Footprint Determination for Lifting Body Spacecraft

Qian

Guo

Yang

2021

Lecture Notes in Electrical Engineering

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Trajectory Design of Re-entry Vehicles using combined Pigeon Inspired Optimization and Orthogonal Collocation method

Cited by 12 publications

References 12 publications

Theoretical analysis of the convergence property of a basic pigeon-inspired optimizer in a continuous search space

Theoretical analysis of the convergence property of a basic pigeon-inspired optimizer in a continuous search space

Spacecraft formation reconfiguration trajectory planning with avoidance constraints using adaptive pigeon-inspired optimization

Genetic Algorithm-Based Re-Entry Trajectory Optimization and Footprint Determination for Lifting Body Spacecraft

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