A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

Wang, Zhenwu; Qin, Chao; Wan, Benting; Song, William Wei

doi:10.3390/e23070874

Cited by 47 publications

(19 citation statements)

References 146 publications

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“…However, the more efficient and safer solution is to perform the tuning during simulations. There are different algorithms that may be employed for such a task [63]. In this paper, the nature-inspired algorithms were examined.…”

Section: Nature Inspired Optimization Algorithm-the Flower Pollinatio...mentioning

confidence: 99%

Speed Control Based on State Vector Applied for Electrical Drive with Elastic Connection

et al. 2022

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The paper is focused on issues related to the control of electrical drives with oscillations of state variables. The main problem deals with the construction of the mechanical part, which contains elastic elements used as a coupling between the motor machine and the load. In such cases, strict tracking of the reference trajectory is difficult, so damping of the disturbances is necessary. For this purpose, the full state vector of the object is applied as the feedback signal for the speed controller. This method is efficient and relatively easy to implement (including the hardware part). However, the control accuracy is dependent on the quality of the parameters identification and the invariance of the object. Thus, two adaptive structures are proposed for the two-mass system. Moreover, selected coefficients were optimized using metaheuristic algorithms (symbiotic organism search and flower pollination algorithm). After presentation of the preliminaries and mathematical background, tests were conducted, and the numerical simulations are shown. Finally, the experimental verification for the 0.5 kW DC machines was performed. The results confirm the theoretical concept and the initial assumptions: the state controller leads to the precise control of the drive with a long shaft; recalculation of the parameters can improve the work of the drive under changes of time constants; modern design tools are appropriate for this application.

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Section: Nature Inspired Optimization Algorithm-the Flower Pollinatio...mentioning

confidence: 99%

Speed Control Based on State Vector Applied for Electrical Drive with Elastic Connection

et al. 2022

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“…Metaheuristics are approximate methods applicable to various optimization problems [3,4]. Many metaheuristics are inspired by natural phenomena, such as evolution theory, the collective behaviour of groups of animals, the laws of physics or the behaviour and lifestyle of human beings.…”

Section: Introductionmentioning

confidence: 99%

A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

2022

IJIES

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This paper proposes a modified honey badger algorithm (MHBA) for solving the optimal power flow (OPF) problem. This problem is a highly non-linear, non-convex and complex optimization problem with several decision variables and constraints. The original honey badger algorithm (HBA) has the problem of trapping in local optima due to the loss of population diversity, especially in solving complex optimization problems. Therefore, the MHBA aims at sufficient improvement in finding the optimal solution and feasibility. Opposition-based learning strategy (OBL) is integrated with the MHBA to preserve the diversity of the population and enhance the convergence toward the optimal solution. The effectiveness of the MHBA algorithm is evaluated on five objective functions of the OPF problem namely, total generation fuel cost minimization, active power and reactive power transmission losses minimization, voltage deviation and voltage stability enhancement. The performance of the proposed algorithm is tested and validated on the IEEE 30-bus test system. The proposed MHBA is compared with the HBA and other nature-inspired optimization algorithms reported in the literature. The results indicate that the proposed MHBA algorithm has the superiority to jump out of the local optimal and better convergence in solving the OPF problem. This is due to the strategy used in the algorithm which helps in maintaining the population diversity and provides a proper balance between exploration and exploitation.

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“…These solvers compute local optima, which is sufficient under the tacit assumption that only minor adaptations are made and the problem is convex in the region of interest. As far as the model-based optimization is concerned, in principle also other optimization methods can be applied, as e.g., derivative free methods [7,8], population-based methods [9], or nature-inspired algorithms [10]. While for the solution of the optimization problems efficient algorithms are available, a major practical problem is the mismatch between the model and the true behavior of the plant.…”

Section: Introductionmentioning

confidence: 99%

Handling Measurement Delay in Iterative Real-Time Optimization Methods

Mukkula

Engell

2021

Processes

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This paper is concerned with the real-time optimization (RTO) of chemical plants, i.e., the optimization of the steady-state operating points during operation, based on inaccurate models. Specifically, modifier adaptation is employed to cope with the plant-model mismatch, which corrects the plant model and the constraint functions by bias and gradient correction terms that are computed from measured variables at the steady-states of the plant. This implies that the sampling time of the iterative RTO scheme is lower-bounded by the time to reach a new steady-state after the previously computed inputs were applied. If analytical process measurements (PAT technology) are used to obtain the steady-state responses, time delays occur due to the measurement delay of the PAT device and due to the transportation delay if the samples are transported to the instrument via pipes. This situation is quite common because the PAT devices can often only be installed at a certain distance from the measurement location. The presence of these time delays slows down the iterative real-time optimization, as the time from the application of a new set of inputs to receiving the steady-state information increases further. In this paper, a proactive perturbation scheme is proposed to efficiently utilize the idle time by intelligently scheduling the process inputs taking into account the time delays to obtain the steady-state process measurements. The performance of the proposed proactive perturbation scheme is demonstrated for two examples, the Williams–Otto reactor benchmark and a lithiation process. The simulation results show that the proposed proactive perturbation scheme can speed up the convergence to the true plant optimum significantly.

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A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

Cited by 47 publications

References 146 publications

Speed Control Based on State Vector Applied for Electrical Drive with Elastic Connection

Speed Control Based on State Vector Applied for Electrical Drive with Elastic Connection

A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

Handling Measurement Delay in Iterative Real-Time Optimization Methods

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