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

delete2025-10-17
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PRE
AI
L
Le-Le Mao
A
Azlan Mohd Zain
K
Kai-qing ZHOU
X
Xin Li *
DOI:10.1007/s10586-025-05745-8delete
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Abstract

Abstract

En 中文
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.
Keywords:
Wind driven optimization
Function optimization
Knapsack problem
Hardware-software partitioning

Journal

C
Cluster Computing
IF:
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Papers:
691
Citations:
1

Organization

S
D
Department of Information Engineering
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142
Papers: 75
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C
College of Mathematics and Computer Science
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54
Papers: 28
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C
College of Intelligence and Computing
Scholars:
149
Papers: 67
Citations: 1
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