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Soft Computing
Published in: Soft Computing 8/2021

07-02-2021 | Methodologies and Application

Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm

Authors: Neetesh Kumar, Navjot Singh, Deo Prakash Vidyarthi

Published in: Soft Computing | Issue 8/2021

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Abstract

Redheaded Agama lizards attack their prey in a well-organized manner. This work models the dynamic foraging behaviour of Agama lizards and their effective way of capturing prey into a mathematical model named as artificial lizard search optimization (ALSO) algorithm. The idea is based on a recent study in which the researchers reported that the lizards control the swing of their tails in a measured manner to redirect angular momentum from their bodies to their tails, stabilizing body attitude in the sagittal plane. A balanced lumping (between body and tail angles) plays a significant role in capturing the prey in a shot. In formulating the optimization problem, a swarm of lizard are considered that are hunting for the prey. To study the performance of the proposed ALSO, it has been simulated. A comparative study is done with some well-known nature-inspired optimization techniques on classical unimodal, multimodal and other benchmark functions. Further, the algorithm is also tested on an object detection application. The result proves the effectiveness of the proposed ALSO algorithm over other nature-inspired state of the art.
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Metadata
Title
Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm
Authors
Neetesh Kumar
Navjot Singh
Deo Prakash Vidyarthi
Publication date
07-02-2021
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 8/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-021-05606-7

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