ArticleScientific reports2024
Application of spiral enhanced whale optimization algorithm in solving optimization problems.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Mechanism and Optimization of Quasi-Static Compression Performance in Hexagonal Honeycomb Sandwich Structures.ACS omega · 2026Article
- A comparative analysis of meshless based simulation optimization models with metaheuristic algorithms for groundwater remediation.Scientific reports · 2025Article
- Quadratic interpolation optimization-based 2DoF-PID controller design for highly nonlinear continuous stirred-tank heater process.Scientific reports · 2025Article
- DBO-AWOA: An Adaptive Whale Optimization Algorithm for Global Optimization and UAV 3D Path Planning.Sensors (Basel, Switzerland) · 2025Article
- A Labor Division Artificial Gorilla Troops Algorithm for Engineering Optimization.Biomimetics (Basel, Switzerland) · 2025Article
- Multi-Strategy Improved Whale Optimization Algorithm and Its Engineering Applications.Biomimetics (Basel, Switzerland) · 2025Article
- Entanglement-assisted variational algorithm for discrete optimization problems.Communications physics · 2025Article
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Authors and funding
8 authors.
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Abstract
The Whale Optimization Algorithm (WOA) is regarded as a classic metaheuristic algorithm, yet it suffers from limited population diversity, imbalance between exploitation and exploration, and low solution accuracy. In this paper, we propose the Spiral-Enhanced Whale Optimization Algorithm (SEWOA), which incorporates a nonlinear time-varying self-adaptive perturbation strategy and an Archimedean spiral structure into the original WOA. The Archimedean spiral structure enhances the diversity of the solution space, aiding the algorithm in escaping local optima. The nonlinear time-varying self-adaptive optimization dynamic perturbation strategy improves the algorithm's local search capability and enhances solution accuracy. The effectiveness of the proposed algorithm is validated from multiple perspectives using CEC2014 test functions, CEC2017 test functions, and 23 benchmark test functions. The experimental results demonstrate that the enhanced Whale Optimization Algorithm significantly improves population diversity, balances global and local search, and enhances solution accuracy. Additionally, SEWOA exhibits excellent performance in solving three engineering design problems, showcasing its value and wide range of potential applications.
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