A modified wind driven optimization algorithm and its applications in hardware-software partitioning

The Wind Driven Optimization (WDO) algorithm is a metaheuristic technique inspired by atmospheric flow dynamics. Although structurally simple, WDO suffers from static search behavior and diversity loss caused by global wind-speed-based sorting. It also lacks fine-grained local search capabilities, w...

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Published in:Cluster computing Vol. 28; no. 16; p. 1032
Main Authors: Mao, Le-Le, Zain, Azlan Mohd, Zhou, Kai-Qing, Li, Xin
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2025
Springer Nature B.V
Subjects:
Ablation
Accuracy
Algorithms
Comparative studies
Computer Communication Networks
Computer Science
Convergence
Efficiency
Exploitation
Feature selection
Hardware
Heuristic methods
Operating Systems
Optimization algorithms
Optimization models
Optimization techniques
Parameter estimation
Parameter identification
Particle swarm optimization
Partitioning
Processor Architectures
Searching
Software
Task complexity
Wind speed
Hardware-software partitioning
Wind driven optimization
Function optimization
Knapsack problem
ISSN:1386-7857, 1573-7543
Online Access:Get full text
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Summary:The Wind Driven Optimization (WDO) algorithm is a metaheuristic technique inspired by atmospheric flow dynamics. Although structurally simple, WDO suffers from static search behavior and diversity loss caused by global wind-speed-based sorting. It also lacks fine-grained local search capabilities, which limit its performance in complex, high-dimensional, and constrained tasks. To address these issues, this study proposes an improved optimization model by integrating WDO, Beluga Whale Optimization (BWO), and the Golden Sine Algorithm (GSA). WDO removes wind-speed sorting to preserve diversity and enable multi-strategy fusion. Subsequently, WDO is integrated with BWO to enhance global search capabilities, while GSA is adopted for fine-grained local exploitation. Consequently, the integrated WDO-BWO-GSA approach is applied to estimate the optimal hardware-software (HW-SW) partitioning. Experiments on 24 benchmark functions including both ablation and comparative studies were conducted to evaluate the effectiveness of the proposed algorithm. The results confirmed that the integrated WDO-BWO-GSA significantly enhanced global exploration and local exploitation. Comparative results further demonstrated that the proposed integrated WDO-BWO-GSA outperformed WDO, BWO, Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and GSA, achieving average convergence speed improvements of 62.07%, 54.28%, 62.31%, 70.69%, 72.26%, and 62.19%, respectively. Additionally, on a HW-SW partitioning task, it surpassed the Complex-valued encoding WDO (CWDO) by 59.55% in convergence speed, while also delivering superior solution quality. These results confirmed the robustness and adaptability of the proposed integrated WDO-BWO-GSA in solving complex partitioning optimization problems.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-025-05745-8