Comparative Performance Analysis of Slime Mould Algorithm For Efficient Design of Proportional–Integral–Derivative Controller

İzci, Davut; Ekinci, Serdar

doi:10.5152/electrica.2021.20077

Cited by 87 publications

(44 citation statements)

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“…Several examples of such algorithms for PID controller design can be encountered in the literature due to this advantage. Some of them are: gravitational search algorithm (Duman et al, 2011), Harris hawks optimization algorithm (Ekinci et al, 2020b), kidney-inspired algorithm (Hekimoğlu, 2019b), flower pollination algorithm (Potnuru et al, 2019), grey wolf optimization algorithm (Bhatnagar and Gupta, 2018), invasive weed optimization algorithm (Khalilpour et al, 2011), genetic algorithm (El-Deen et al, 2015), stochastic fractal search algorithm (Khanam and Parmar, 2017), teaching–learning-based optimization (Mishra et al, 2020), ant colony optimization (Kouassi et al, 2020), swarm learning process (Pongfai et al, 2020), particle swarm optimization (Sabir and Khan, 2014), water cycle algorithm (Mohamed et al, 2020), sine cosine algorithm (Agarwal et al, 2018b) and its improved version (Ekinci et al, 2019) along with slime mould algorithm (Izci and Ekinci, 2021) and hybrid atom search optimization with simulated annealing algorithm (Eker et al, 2021). Further improvement on PID controller design can still be achieved despite the promising results obtained by the abovementioned algorithms since there is a dizzying effort in terms of development of new metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

İzci

2021

Transactions of the Institute of Measurement and Control

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This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

İzci

2021

Transactions of the Institute of Measurement and Control

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“…The novel improved slime mould algorithm (ISMA) was proposed as a better tool to maintain the terminal voltage of an automatic voltage regulator (AVR) system and controlling the output speed of a direct current (DC) motor. The AVR and the DC motor systems have so far been heavily adopted as real-world engineering problems for assessing the performance of metaheuristic algorithms (Izci and Ekinci, 2021). The DC motor is a second order system whereas the AVR is a higher order complex system, thus, they are good candidates to observe the performance of the metaheuristic algorithms from a wider perspective.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed ISMA based FOPID controller for DC motor speed control system was compared with other available and effective approaches of manta ray foraging optimization (Ekinci et al, 2021b), chaotic atom search optimization algorithm (Hekimoğlu, 2019a), atom search optimization (Hekimoğlu, 2019a), stochastic fractal search algorithm (Saini et al, 2020), grey wolf optimization (Agarwal et al, 2018) based FOPID controllers and Lévy flight distribution with Nelder–Mead algorithm (Izci, 2021), Harris–Hawks optimization (Ekinci et al, 2020b), Henry gas solubility optimization (Ekinci et al, 2021a), SMA (Izci and Ekinci, 2021), atom search optimization (Hekimoğlu, 2019a), grey wolf optimization (Agarwal et al, 2018), stochastic fractal search algorithm (Bhatt et al, 2019), kidney-inspired algorithm (Hekimoğlu, 2019b), sine–cosine algorithm (Agarwal et al, 2017), invasive weed optimization algorithm (Khalilpour et al, 2011), and particle swarm optimization (Khalilpour et al, 2011) based PID controllers.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

İzci

Ekinci

Zeynelgil

et al. 2021

Transactions of the Institute of Measurement and Control

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This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

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“…SMA has a unique search mode, which keeps the algorithm from falling into local optima, and has superior global exploration capability. The approach has been applied in real-world optimization problems like feature selection [ 36 ], parameters optimization of the fuzzy system [ 37 ], multilevel threshold image segmentation [ 38 ], control scheme [ 39 ], and parallel connected multistacks fuel cells [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid SSA and SMA with Mutation Opposition‐Based Learning for Constrained Engineering Problems

Wang

Liu

et al. 2021

Computational Intelligence and Neuroscience

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Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms’ advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition-based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique’s practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real-world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation.

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Comparative Performance Analysis of Slime Mould Algorithm For Efficient Design of Proportional–Integral–Derivative Controller

Cited by 87 publications

References 0 publications

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

A Hybrid SSA and SMA with Mutation Opposition‐Based Learning for Constrained Engineering Problems

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