Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

Abstract

In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.

An intelligent optimization algorithm with a deep learning‐enabled block‐based motion estimation model

“…Lastly, the APO algorithm is applied to optimally tune the hyperparameters of the BiLSTM model, which helps to improve the complete motion estimation efficacy. The APO method was inspired by the warning behaviour and movement of the Anas platyrhynchos flock (Zhang et al, 2020).…”

“…Lastly, the APO algorithm is applied to optimally tune the hyperparameters of the BiLSTM model, which helps to improve the complete motion estimation efficacy. The APO method was inspired by the warning behaviour and movement of the Anas platyrhynchos flock (Zhang et al, 2020). To solve the optimization problem, every duck is considered as an individual particle that could move easily, such as when ducks flock.…”

(Retracted) Anas platyrhynchos optimizer with deep belief network-based sarcasm detection and classification model

“…During the warning performance, the fly in danger function recognized by chosen probability Pc was presented. 19 An essential procedure of warning performance was established as follows.…”

Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse

“…However, the solution speed and accuracy of the current commonly used PSO are largely dependent on the value of the inertia weight and learning factor. When multi-objective optimization problems are performed [10], it is difficult to easily obtain better values of inertia weights and learning factors [27]. For this reason, related researchers have proposed a bare-bone PSO (BPSO) that uses Gaussian distribution sampling to replace the inertial weight parameters and learning factor parameters in the standard PSO [28], which solves the problem of inconvenient parameter setting in the original algorithm [29].…”