Fusing Exploration and Exploitation: Hybrid Fireworks–Whale Optimization for Multi-Objective Independent Task Scheduling in Cloud Environments

Task scheduling in cloud computing environments is a complex, multi-objective optimization problem that requires balancing conflicting goals such as minimizing execution time and cost while maximizing resource utilization and throughput. Metaheuristic algorithms have proven effective for tackling such problems due to their adaptability and global search capabilities. In this work, we propose a novel family of hybrid scheduling algorithms that combine the strengths of two well-established metaheuristics: the Fireworks Algorithm (FWA)—known for its diverse exploration of the search space—and the Whale Optimization Algorithm (WOA)—recognized for its effective local exploitation. Uniquely, we design four distinct hybridization strategies: Sequential Hybrid, Parallel Hybrid, FWA-Encircling Hybrid, and WOA-Spark Hybrid, each integrating FWA and WOA in a fundamentally different manner. To the best of our knowledge, this is the first systematic study to explore multiple hybridization pathways between two metaheuristic algorithms for cloud task scheduling. Comprehensive experiments on synthetic benchmark workloads as well as the real-world GoCJ trace demonstrate that our hybrid algorithms outperform recent advanced FWA (OBDFWA), modified WOA (MWOA), and the high-performing hybrid GA–GWO methods in key metrics, such as makespan, resource utilization, and cost. Our methods achieve 2.5% to 7.5% improvements in fitness scores. Statistical validation using Friedman's rank test and the Nemenyi post-hoc test further confirms the superiority and robustness of our hybrids. Overall, this work introduces a comprehensive and pioneering hybridization framework for multi-objective cloud task scheduling.