MABFDMO: An Adaptive Dwarf Mongoose Optimization Algorithm for Multi-Objective Task Scheduling in Cloud Computing

Olanrewaju Lawrence Abraham

doi:10.1016/j.suscom.2026.101393

MABFDMO: An Adaptive Dwarf Mongoose Optimization Algorithm for Multi-Objective Task Scheduling in Cloud Computing

2026-05-12

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Olanrewaju Lawrence Abraham *

M

Md Asri Ngadi

J

Johan Bin Mohamad Sharif

M

Mohd Kufaisal Mohd Sidik

O

Ogunyinka Taiwo Kolawole

DOI：10.1016/j.suscom.2026.101393

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• An adaptive multi-objective dwarf mongoose optimization algorithm (MABFDMO) is proposed for cloud task scheduling. • MABFDMO simultaneously optimizes makespan and cost under constrained cloud environments. • Adaptive babysitter foraging and boundary feedback mechanisms enhance exploration–exploitation balance. • Pareto-based archive maintenance ensures well-distributed and stable non-dominated solutions. • Extensive simulations on GoCJ and HPC2N workloads demonstrate consistent performance gains over benchmark algorithms. • Statistical t-test analysis and convergence trend visualization confirm the robustness and scalability of MABFDMO.

Keywords:

Dwarf Mongoose Optimization

Multi-Objective Task Scheduling

Cloud Computing

Makespan Optimization

Cost Optimization

MABFDMO: An Adaptive Dwarf Mongoose Optimization Algorithm for Multi-Objective Task Scheduling in Cloud Computing

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