BE-GWO: Binary extremum-based grey wolf optimizer for discrete optimization problems

Banaie-Dezfouli, Mahdis; Nadimi-Shahraki, Mohammad H.; Beheshti, Zahra

doi:10.1016/j.asoc.2023.110583

Cited by 14 publications

(5 citation statements)

References 65 publications

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“…Transfer functions are among the most widely used techniques because of their simplicity as an operator in the binary transformation [48]. Various transfer methods have been introduced from continuous search space to binary space, such as v-shaped [49], s-shaped [50], o-shaped [51], zshaped [52], x-shaped [53], cosine transfer function [54], and hyperbolic tangent transfer function [55]. In this study, the hyperbolic tangent transfer function is used to convert the continuous search space to a binary one since it has some advantages, such as a few control parameters, an efective convergence rate, and the simplicity of implementation.…”

Section: Binary Transformation Of the Original Fbimentioning

confidence: 99%

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

Ova,

Dogan,

Demirbas

2024

International Transactions on Electrical Energy Systems

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To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO.

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Section: Binary Transformation Of the Original Fbimentioning

confidence: 99%

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

Ova,

Dogan,

Demirbas

2024

International Transactions on Electrical Energy Systems

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“…Wrapper methods, on the other hand, use a classification model to create all subsets and corresponding classification models for all features, and score each subset using the classification model’s performance measure. These methods can use optimization approaches such as metaheuristic algorithms [ 53 , 54 ]. Embedded methods combine the advantages of both methods including the feature selection for the model-fitting step [ 37 , 40 ].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the distances between the distributions of each class within the overlapping areas were small, making accurate classification challenging. The accuracy achieved using the low-ranking feature combinations ranged from a minimum of 47.08 to a maximum of 54 Table 7. Classification results of the low-ranking six FPD n feature combinations.…”

Section: Optimal Sensor Selection Based On Dnfmentioning

confidence: 99%

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

Lee,

et al. 2023

Sensors

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Roll-to-roll manufacturing systems have been widely adopted for their cost-effectiveness, eco-friendliness, and mass-production capabilities, utilizing thin and flexible substrates. However, in these systems, defects in the rotating components such as the rollers and bearings can result in severe defects in the functional layers. Therefore, the development of an intelligent diagnostic model is crucial for effectively identifying these rotating component defects. In this study, a quantitative feature-selection method, feature partial density, to develop high-efficiency diagnostic models was proposed. The feature combinations extracted from the measured signals were evaluated based on the partial density, which is the density of the remaining data excluding the highest class in overlapping regions and the Mahalanobis distance by class to assess the classification performance of the models. The validity of the proposed algorithm was verified through the construction of ranked model groups and comparison with existing feature-selection methods. The high-ranking group selected by the algorithm outperformed the other groups in terms of training time, accuracy, and positive predictive value. Moreover, the top feature combination demonstrated superior performance across all indicators compared to existing methods.

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“…GWO has excellent results in solving combinatorial optimisation problems such as vehicle scheduling, path planning, and job shop scheduling problems [23][24][25]. Still, GWO algorithms have the problem of quickly falling into the local optimum problem [26]. Tawhid et al proposed a hybrid grey wolf optimisation genetic algorithm (HGWOGA), which balances the exploration and exploitation capabilities with the grey wolf optimisation algorithm, and then, after dividing the population, the crossover operator is used on the divided sub-populations to increase the diversity of the search; finally, the mutation operator of the genetic algorithm is applied to the whole population to avoid premature maturity [27].…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm

Hu,

Xiong,

Sun

et al. 2024

Applied Sciences

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The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm’s global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively.

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BE-GWO: Binary extremum-based grey wolf optimizer for discrete optimization problems

Cited by 14 publications

References 65 publications

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm

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