prairie dog optimization algorithmaudio signal factormerit-seeking abilitylens opposition-based learning strategyengineering design problemsThe prairie dog optimization (PDO) algorithm is a metaheuristic optimization algorithm that simulates the daily behavior of prairie dogs. The prairie dog groups have a ...
(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA ...
GLOBAL optimizationINFORMATION sharingENGINEERING designMETAHEURISTIC algorithmsPRAIRIE dogsThe prairie dog optimization (PDO) algorithm is a metaheuristic optimization algorithm that simulates the daily behavior of prairie dogs. The prairie dog groups have a unique mode of information exchange. They d...
In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to ...
The Multi-Objective Prairie Dog Optimization (MOPDO) algorithm is introduced which considers the makespan time and the execution time as the major objective while allocating resources in IoT. The proposed MOPDOA effectively allocates the resource to the Virtual Machines (VMs) b...
Binary opposition Prairie dog optimization algorithmCellular automataMixed opposition-based learningdetection datasets are highly likely to contain numerous redundant, irrelevant, and noisy features that slow the performance of the machine learning techniques and classifiers tha......
Prairie Dog optimization algorithm (PDOA)Resource schedulingDwarf Mongoose optimization algorithm (DMOA)The fog computing paradigm is better for creating delay-sensitive applications in Internet of Things (IoT). As the fog devices are resource constrained, the deployment of diversified IoT applications ...
Prairie dog optimizationOptimal scheduling of BSCS minimizes total costs, includes charging/discharging, battery degradation, swapping income, and service delay cost.The uncertainties of various EV parameters are addressed using the 2m-PEM approach.The proposed approach handles dynamic conditions, including...
Given the NP-hard nature of the problem, we propose a multi-objective optimization algorithm, the Decoding-priority-based Self-adaptive Prairie Dog Optimizer (DSPDO). The encoding and decoding procedures are specifically designed with a constraint-oriented solution repair strategy. Chaotic mapping is ...
This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight...