Performance Evaluation of PDO Algorithm through Benchmark Functions and MLP Training

Eker, Erdal; Kayrı, Murat; Ekinci, Serdar; KACMAZ, Mehmet Ali

doi:10.5152/electr.2023.22179

Cited by 2 publications

(3 citation statements)

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“…This set consists of difficult problems. The goal of these problems is to highlight the optimization capability and competitive aspect of the algorithm by compelling it to reach the global solution [15]. For the alternative algorithms that CapSA will compete with, we selected current and efficient algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Metaheuristic algorithms consist of three different mechanisms: the initialization phase, containing the candidate solution set; the exploitation phase, which divides the search space into regions to concentrate on narrow areas; and the exploration phase, which scans the entire space, selects, and improves the best solutions obtained in the exploitation phase. The success of metaheuristic algorithms is directly proportional to the strength of the balance between the exploitation and exploration phases [15]. The CapSA algorithm considered in this paper is one of the current and efficient metaheuristic algorithms produced in 2021 [16].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

EKER

2023

EJT

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The purpose of this paper is to demonstrate the superiority of the Capuchin Search Algorithm (CapSA), a metaheuristic, in competitive environments and its advantages in optimizing engineering design problems. To achieve this, the CEC 2019 function set was used. Due to the challenging characteristics of the CEC 2019 function set in reaching a global solution, it effectively showcases the algorithm's quality. For this comparison, sea-horse optimizer (SHO), grey wolf optimizer (GWO), sine-cosine algorithm (SCA), and smell agent optimization (SAO) were chosen as current and effective alternatives to the CapSA algorithm. Furthermore, the gear train design problem (GTD) was selected as an engineering design problem. In addition to the CapSA algorithm, a hybrid of SCA and GWO algorithm (SC-GWO) and genetic algorithm (GA) were chosen as alternatives for optimizing this problem. The performance superiority and optimization power of the CapSA algorithm were assessed using statistical metrics and convergence curves, then compared with alternative algorithms. Experimental results conclusively demonstrate the significant effectiveness and advantages of the CapSA algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

EKER

2023

EJT

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“…The second criterion aims to assess the effectiveness of the digging and the quality of the available food sources. The renewed position for the burrow building is modeled as follows where 𝑃 𝑏𝑒𝑠𝑡,𝑗 denotes the coined global best solution, 𝜌 specifies the food source alert parameter which is set to 0.1 kHz [32].…”

Section: Prairie Dog Optimization (Pdo) Algorithmmentioning

confidence: 99%

Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

Izci,

Ekinci

2023

IJRCS

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The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation.

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Performance Evaluation of PDO Algorithm through Benchmark Functions and MLP Training

Cited by 2 publications

References 0 publications

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

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