A Binary Grasshopper Algorithm Applied to the Knapsack Problem

Pinto, Hernán; Peña, Álvaro; Causa, Leonardo; Valenzuela, Matías A.; Villavicencio, Gabriel

doi:10.1007/978-3-319-91189-2_14

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Pinto et al [77] developed a Binary GOA (BGOA) based on the percentile concept for solving the Multidimensional Knapsack Problem (MKP). The performance of BGOA was validated using OR-Library benchmarks MKP instances in comparison with Binary Artificial Algae Algorithm (BAAA) and K-Means Transition Ranking (KMTR).…”

Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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“…In the latest years, the Grasshopper Optimization Algorithm (GOA) has been broadly studied and applied in numerous fields due to the following reasons: i) its simple implementation, ii) reasonably good optimization capability, and iii) relatively incredible overall performance in realizing complicated troubles. GOA had been proposed to solve various optimization problems in many domains in previously published works, such as constrained and unconstrained test functions (Neve et al, 2017;Saremi et al, 2017), knapsack problem (Pinto et al, 2019), task assignment problem(L. , hand posture estimation problem (Saremi et al, 2020), power management (Juhari et al, 2019;Jumani et al, 2019;Rajput et al, 2017;Talaat et al, 2020), electric load scheduling in smart grids (Jamil & Mittal, 2020;Ullah et al, 2019Ullah et al, , 2020, hydrothermal scheduling (Zeng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Novel Variants of Grasshopper Optimization Algorithm to Solve Numerical Problems and Demand Side Management in Smart Grids

Badr

Saafan

Abdelsalam

et al. 2023

Preprint

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The grasshopper optimization algorithm (GOA), which is one of the recent metaheuristic optimization algorithms, mimics the natural movements of grasshoppers in swarms seeking food sources. Some deficiencies have existed in the original GOA such as slow convergence speed, and the original GOA may get quickly stuck into local solutions facing some complex. For tackling these drawbacks of the original GOA, enhanced versions of GOA have been proposed to deal with the optimization problems more effectively. In the current study, two strategies have been integrated into GOA: the grouping mechanism of non-linear ‘c’ parameters and the mutation mechanism. Moreover, two different groups of non-linear ‘c’ parameters have been suggested in the grouping mechanism. Incorporating the grouping mechanism into GOA can update the grasshoppers’ positions within a limited local area, whereas the diversity of agents can be improved by integrating the mutation mechanism. Eight Novel-Variants GOA (NVGOAs) are proposed to address the deficiencies of the original GOA. Where two variants NVGOA1_1 and NVGOA2_1 represent the impact of each proposed group of ‘c’ parameters. Another two variants NVGOA3 and NVGOA4 represent the impact of the mutation mechanism with two different values of probability. Moreover, four variants: NVGOA1_2, NVGOA1_3, NVGOA2_2, and NVGOA2_3 represent the combination of the two proposed mechanisms. First, the comparison between the performance of the proposed variants and the original GOA has been conducted. Then, for validation of the efficiency of the proposed NVGOAs, the performance of the best-recorded NVGOA variants has been tested against the 29 CEC-2017 benchmark functions and compared with six state-of-the-art optimization algorithms based on the mean and the standard deviation metrics. Moreover, the Wilcoxon Signed-Rank test has been employed to exhibit the efficiency of the proposed variants. As well comparative analysis with previous enhancements of GOA has been conducted against the best-recorded NVGOA variants. Also, conducting a dimension study between the best-recorded chaotic previous variants against the best-recorded proposed NVGOA variants has revealed the superiority of NVGOAs. The results of all these analyses demonstrated the success and efficiency of the proposed NVGOA variants to solve numerical optimization problems. Concerning demand side management in smart grids, the proposed NVGOA variants have been applied to schedule the loads in three areas: residential, commercial, and industrial to decrease the daily operating costs and peak demand. The results show that the peak demand is reduced by 23.9%, 17.6%, and 9.2% in residential areas, commercial areas, and industrial areas respectively. Also, the operating cost decreased by 7.25%, 9.2%, and 18.89% in residential, commercial, and industrial areas, respectively. Finally, the overall results show that the proposed NVGOA algorithms are effective solutions to address the flaws of the original version of GOA and can get high-quality solutions for different optimization problems.

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“…Hichem et al proposed a Novel Binary GOA (NBGOA) by modeling position vectors as binary vectors in [ 34 ]. Pinto et al [ 35 ] developed a binary GOA based on the percentile concept for solving the Multidimensional Knapsack Problem (MKP). Moreover, BGOA-M, a binary GOA algorithm based on the mutation operator, was introduced for the FS problem [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

Wang

Chu

Tian

et al. 2022

Entropy

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The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm’s exploration capability and the solution’s quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm’s validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.

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A Binary Grasshopper Algorithm Applied to the Knapsack Problem

Cited by 7 publications

References 12 publications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Novel Variants of Grasshopper Optimization Algorithm to Solve Numerical Problems and Demand Side Management in Smart Grids

Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

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