Weighted average algorithm: A novel meta-heuristic optimization algorithm based on the weighted average position concept

Jun Cheng

doi:10.1016/j.knosys.2024.112564

Weighted average algorithm: A novel meta-heuristic optimization algorithm based on the weighted average position concept

2024-12-01

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DOI：10.1016/j.knosys.2024.112564

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To address the ever-increasing complexity of real-world engineering challenges, meta-heuristic algorithms have been extensively studied and applied. However, balancing exploration and exploitation remains a significant challenge. In this paper, a novel meta-heuristic optimization algorithm based on the weighted average position concept, and named weighted average algorithm (WAA), is proposed and implemented. In this algorithm, the weighted average position for the whole population is first established at each iteration. Subsequently, WAA applies one of two movement strategies to balance exploration and exploitation, determined by a parameter function that depends on random constants and iteration numbers. To validate the effectiveness and reliability of WAA, it is applied to various optimization challenges, amongst which unconstrained benchmark functions and constrained engineering challenges. Based on the Friedman and Wilcoxon analyses, it can be concluded that the proposed algorithm obtained the best performance for the considered benchmark functions and engineering problems. The WAA is applied to prediction and optimization of surface waviness in Wire Arc Additive Manufacturing (WAAM) components. First, two prediction models relating WAAM process parameters and surface waviness are established based on an Artificial Neural Network (ANN) optimized by WAA, and Particle Swarm Optimization (PSO), respectively. The WAA optimized ANN (WAA/ANN) model outperforms both the standard ANN models and those optimized by PSO. Finally, leveraging the developed WAA/ANN prediction model, WAA and other optimization algorithms are applied to minimize waviness of a WAAM component, with WAA exhibiting promising performance. Source codes of WAA are publicly available at https://nl.mathworks. com/matlabcentral/fileexchange/174020-a-weighted-average-algorithm.

Keywords:

Meta-heuristic optimization

Weighted average position

Engineering challenges

Optimized artificial neuron network

Waviness optimization

Weighted average algorithm: A novel meta-heuristic optimization algorithm based on the weighted average position concept

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