PID speed control of DC motor using meta-heuristic algorithms

Acharya, Bishwa Babu; Dhakal, Sandeep; Bhattarai, Aayush; Bhattarai, Nawraj

doi:10.11591/ijpeds.v12.i2.pp822-831

Cited by 11 publications

(9 citation statements)

References 21 publications

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“…Hassan et al 83 applied the ACO algorithm to optimise pressure vessels' optimum design. Acharya et al 84 optimized proportional–integral–derivative control scheme parameters to regulate the DC motor’s speed. Pham et al 85 used different metaheuristic optimization algorithms in the functionally graded sandwich porous beams’ optimum design.…”

Section: Related Workmentioning

confidence: 99%

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Azizi

Shishehgarkhaneh

Basiri

et al. 2023

Sci Rep

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In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov–Smirnov, Mann–Whitney, and Kruskal–Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.

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Section: Related Workmentioning

confidence: 99%

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Azizi

Shishehgarkhaneh

Basiri

et al. 2023

Sci Rep

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“…The reproduction procedure seeks to increase the candidate solutions quality. The replication procedure is dependent on the meta-heuristic algorithm utilized in the fuzzy modelling process [29], [88], [180].…”

Section: Fuzzy Meta-heuristic Algorithmsmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

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“…It has been demonstrated that DFO has outperformed these algorithms and is used as the optimiser in this work. This algorithm belongs to the broad family of swarm intelligence and evolutionary computation techniques and has been applied to a diverse set of problems including: medical imaging [23], solving diophantine equations [24], PID speed control of DC motor [25], optimising machine learning algorithms [26,27], training deep neural networks for false alarm detection in intensive care units [28], computer vision and quantifying symmetrical complexities [29], identifying animation key points from medialness maps [30], and the analysis of autopoiesis in computational creativity [31].…”

Section: Population-based Optimisermentioning

confidence: 99%

Evolutionary Optimisation of Beer Organoleptic Properties: A Simulation Framework

al-Rifaie

Cavazza

2022

Foods

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Modern computational techniques offer new perspectives for the personalisation of food properties through the optimisation of their production process. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. Furthermore, this work presents a solution discovery method that could be suitable for more complex, industrial setups. An evolutionary computation technique was used to map brewers’ desired organoleptic properties to their constrained ingredients to design novel recipes tailored for specific brews. While there exist several mathematical tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the predetermined quantities of ingredients, this work investigates an automated quantitative ingredient-selection approach. The process, which was applied to this problem for the first time, was investigated in a number of simulations by “cloning” several commercial brands with diverse properties. Additional experiments were conducted, demonstrating the system’s ability to deal with on-the-fly changes to users’ preferences during the optimisation process. The results of the experiments pave the way for the discovery of new recipes under varying preferences, therefore facilitating the personalisation and alternative high-fidelity reproduction of existing and new products.

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PID speed control of DC motor using meta-heuristic algorithms

Cited by 11 publications

References 21 publications

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Evolutionary Optimisation of Beer Organoleptic Properties: A Simulation Framework

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