Advancements in pigeon-inspired optimization and its variants

Abstract

The returning of homing pigeons to their lofts from remote and unfamiliar locations with great accuracy remains a mystery. Pigeon-inspired optimization (PIO), which is a novel mono-objective continuous optimization algorithm, is inspired by the hidden mechanism behind the remarkable navigation capacity of homing pigeons. Since their development, PIO and its variants have been widely applied to various fields ranging from combinatorial optimization to multi-objective optimization in many areas, such as aerospace, medicine, and energy. This study aims to review the modifications of PIO from four aspects of improvement measures, namely, component replacement, operation addition, structure adjustment, and application expansion. It also summarizes the problems of existing research and plots the course of future effort.

A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

“…This algorithm is based on homing behavior of pigeons that used simplified concept of route following either to detect target or coming back to home. The pigeons use earth Pigeon's homing behavior mechanism [9]. magnetic field, sun and landmarks for their complete journey as navigation tools.…”

“…The flying direction of the moving bird is tuned by relative orientation mapped by two basic operators [7,8]. Figure 1 shows the basic approach used by the pigeons to map the route to destination and coming back to home [9].…”

A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks

“…In the loft model, the objects of the study is the particle which is virtualized from pigeons in the navigation process. 10,11 Initial setting of the parameters are set, for example, the population of pigeons is ps , the pigeons’ search in D -dimensional space, the number of running is set to 100, the current iteration is iter , the total number of iterations is Ite r max = 600 , in the first stage iterations are maxIteratio n 1 = 300 , in the second phase iterations are maxIteratio n 2 = 300 , and the position and velocity of every pigeon are recorded as below ( j = 1 , 2 , 3 , . . . , ps )…”

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

“…Although PIO has demonstrated its effectiveness and superiority in numerous fields (particularly in practical engineering optimization) and has received extensive attention from researchers, the theoretical foundations of PIO-including convergence analysis and parameter-setting principles-still remain weak [13]. Currently, theoretical studies of PIO are mainly based on empirical and intuitive statistical results, and rigorous mathematical arguments are lacking [13].…”

“…Although PIO has demonstrated its effectiveness and superiority in numerous fields (particularly in practical engineering optimization) and has received extensive attention from researchers, the theoretical foundations of PIO-including convergence analysis and parameter-setting principles-still remain weak [13]. Currently, theoretical studies of PIO are mainly based on empirical and intuitive statistical results, and rigorous mathematical arguments are lacking [13]. Among theoretical studies of PIO, convergence analysis is a key problem of great significance that concerns the effect of essential factors on pigeon-swarm dynamics and the conditions under which pigeon swarms converge to certain constant positions [14][15][16].…”