An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

Yang, Wenbiao; Xia, Kewen; Li, Tiejun; Xie, Min; Zhao, Ya-ning

doi:10.3390/sym13020244

Cited by 13 publications

(5 citation statements)

References 63 publications

(68 reference statements)

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“…Table VII shows the proposed WWO algorithm's parameters to perform the dynamic optimal dispatch. The GA, NSGA-II, PSO, and TSO algorithms with their parameters in Tables VIII-XI are used for comparison to validate the WWO algorithm-based proposal [24]- [25], [41]- [45].…”

Section: Simulation Resultsmentioning

confidence: 99%

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

Huynh,

Ho,

Pham

et al. 2024

IEEE Access

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A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response to dynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-ahead load forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of population size, parameter tuning, exploitation and exploration, convergence speed, as well as optimization mechanism. It leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is applied to the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solved by day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs, ensuring optimal decision-making in the face of varying load scenarios. With the competition of various dayahead load forecasting techniques in the microgrid, a multi-variate linear regression (MLR) model shows its advantage features, being more transparent, more effective, and more robust than other techniques, especially transparent explainability, as well as simple and fast in model training. These are requirements to achieve the result of day-ahead load forecasting. Thus, the MLR model is proposed to forecast day-ahead load in the microgrid in this paper. The simulation results show that the percentage error (PE) between the MLR modelbased forecasted and actual load powers is always less than 4.42%, the mean absolute percentage error (MAPE) of the forecasting result is 3.33%, and the execution time is 49 (s). These achievements meet the accurate and fast requirements. They are completely competitive with the results of using other techniques such as convolutional neural networks (CNN) and long short-term memory (LSTM), especially in the execution time. This has contributed to improving the efficiency of the dynamic optimal dispatch in the microgrid. Then, the diesel generation, battery energy storage, and total microgrid generation costs are 68.76 ($), 5.09 ($), and 73.85 ($) respectively by using the WWO algorithm which are better than those by using a genetic algorithm (GA), a non-dominated sorting genetic algorithm-II (NSGA-II), a particle swarm optimization (PSO) algorithm, and a transient search optimization (TSO) algorithm in the microgrid. The findings offer valuable insights for microgrid operators, energy planners, and policymakers seeking sustainable and cost-effective solutions for distributed energy resource management.INDEX TERMS Dynamic optimal dispatch, day-ahead load forecasting, microgrid, water wave optimization algorithm

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Section: Simulation Resultsmentioning

confidence: 99%

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

Huynh,

Ho,

Pham

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Therefore, it is still necessary to propose new algorithms or improve existing algorithms to eliminate their defects. These algorithms have been used to solve several real-world problems in both continuous and discrete spaces such as feature selection [58][59][60][61], scheduling and planning [62], disease diagnosis [63], engineering problems [64], photovoltaic energy generation systems [65,66], economic dispatch problems [67], global optimization [68][69][70], community detection [71][72][73], and motion estimation [74,75]. Among swarm intelligence algorithms, the moth flame optimization (MFO) algorithm has attracted noticeable interest for optimization purposes.…”

Section: Related Workmentioning

confidence: 99%

MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

et al. 2021

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The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally.

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“…In metaheuristic algorithms, the diversity of initial populations can significantly affect the convergence speed and solution accuracy of intelligent algorithms [68]. However, in EHO, the lack of a priori information about the search space tends to generate the initial population using random initialization, which imposes some limitations on the update strategy of the search agents.…”

Section: Gaussian Perturbation-based Specular Reflection Learning For Initializing Populationsmentioning

confidence: 99%

Gaussian Perturbation Specular Reflection Learning and Golden‐Sine‐Mechanism‐Based Elephant Herding Optimization for Global Optimization Problems

Duan

Liu

et al. 2021

Computational Intelligence and Neuroscience

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Elephant herding optimization (EHO) has received widespread attention due to its few control parameters and simple operation but still suffers from slow convergence and low solution accuracy. In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden-sine-mechanism-based EHO (SRGS-EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best-positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS-EHO is also applied to solve two classic engineering problems with a constrained search space (pressure-vessel design problem and tension-/compression-string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production.

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An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

Cited by 13 publications

References 63 publications

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

Gaussian Perturbation Specular Reflection Learning and Golden‐Sine‐Mechanism‐Based Elephant Herding Optimization for Global Optimization Problems

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