Comparative study of ten metaheuristic algorithms for development of a novel Quantum Whale Optimization Algorithm research article
Pritam Kumar Pakhiraa, Hachen Alib, Fleming Akhtarc, Ali Akbar Shaikhb, Seyedali Mirjalilid e
- aThe University of Burdwan
- bUniversity of Burdwan
- cThe University of Burdwan Department of Mathematics
- dVSB - Technical University of Ostrava
- eTorrens University Australia - Fortitude Valley Campus Brisbane
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Abstract
This dataset contains the comprehensive run results and statistical test outputs for ten metaheuristic algorithms. The algorithms were rigorously evaluated across three distinct problem domains to validate their performance, convergence, and robustness, supporting the findings of our accompanying research paper. Date:2026-06-21(YYYY-MM-DD)Software: MATLAB Version R2024aEnvironment: Windows 11 HomeCPU: Intel Core I5 1335U Mobile Series ProcessorRam: 16GB LPDDR5Storage: 512GB SSD # Problem Domains1. Classical Benchmark Functions: Standard mathematical optimization functions used for baseline performance evaluation.2. CEC2022 Benchmark Suite: Modern, complex, rotated, and shifted benchmark functions from the IEEE Congress on Evolutionary Computation.3. Inventory Problem: Application of a production inventory model with nonlinear power demand, nonlinear holding cost and carbon emission. # Evaluated Metaheuristics1. Quantum Whale Optimization Algorithm (QWOA, our proposed approach)2. Whale Optimization Algorithm (WOA)3. Quantum encoding Whale Optimization Algorithm (QeWOA)4. Improved Whale Optimization Algorithm (IWOA)5. Improved Whale Optimization Algorithm+ (IWOA+)6. Particle Swarm Optimization (PSO)7. Quantum Particle Swarm Optimization (QPSO)8. Gravitational Search Algorithm (GSA)9. Differential Evolution (DE)10. Fast Evolutionary Programming (FEP) # Data Format & StructureAll the data are in excel format and self explanatory. The directory structure is given below: \QWOA_Paper_Numerical_Data│ README.txt│├───Benchmark_Data│ ├───CEC2022_Data│ │ CEC2022_n_Time.xlsx│ ││ ├───Classical_Data│ │ Classical_n_Time.xlsx│ ││ ├───Iteration_Data│ │ Iteration_CEC2022.xlsx│ │ Iteration_Classical.xlsx│ ││ └───Statistical_Data│ Kruskal-Wallis_CEC2022.xlsx│ Kruskal-Wallis_Classical.xlsx│└───Example_1_Data Example_1_ANOVA_Data.xlsx Example_1_Iteration_Data.xlsx Example_1_Kruskal-Wallis_Data.xlsx Example_1_Run_Data.xlsx Example_1_Sensitivity_Data.xlsx