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Improved exponential distribution optimizer: enhancing global numerical optimization problem solving and optimizing machine learning paramseters
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DOI：10.1007/s10586-024-04753-4.png)
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Abstract
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The Exponential Distribution Optimizer (EDO) is a population-based optimizer inspired by exponential distribution theory. It's widely used in real-world scenarios due to its simple architecture and strong optimization capabilities. However, similar to comparable optimizers that rely on swarms, EDO can fall prey to local optima, suffer from premature convergence, and lack diversity when dealing with challenging optimization problems. To address these problems, we propose Modified EDO (MEDO), which combines the Salp Swarm Algorithm (SSA) with Quadratic Interpolation (QI). QI improves MEDO's local search efficiency and solution precision, while SSA helps prevent getting stuck in suboptimal solutions, serving as a global migration mechanism. These combined strategies enhance EDO's performance. Furthermore, MEDO aims to strike a balance between exploiting known information and exploring new possibilities. We evaluated the EDO approach on 27 benchmark functions, including the CEC 2015 and CEC 2022 sets, as well as the four engineering problems and extreme learning machine parameter tuning. Experimental results show that MEDO outperforms other optimizers in 77.78% of the test functions and achieves 97.3% accuracy in fine-tuning ELM parameters. Non-parametric statistical tests demonstrate that MEDO is highly competitive and superior to other algorithms used in research.
Keywords:
Salp swarm algorithm
Hybridization
Extreme learning machine
Parameter fine tunning
Quadratic interpolation
Exponential distribution optimizer
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