Integrating Grasshopper Optimization Algorithm with Local Search for Solving Data Clustering Problems

El-Shorbagy, M. A.; Ayoub, A. Y.

doi:10.2991/ijcis.d.210203.008

Cited by 13 publications

(5 citation statements)

References 37 publications

(40 reference statements)

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“…It is used to solve the single objective and multi objective optimization problems, and it was tested for constrained and unconstrained test functions. The results show that the GOA algorithm could provide very competitive results, the good robustness and the superior performance of this algorithm was confirmed compared with other optimization algorithms [14][15][16][17][18][19][20]. But, at the same time, the GOA algorithm has some shortcomings: (1) original linear convergence parameter causes the processes of exploration and exploitation unbalanced; (2) unstable convergence speed; and (3) easy to fall into the local optimum [21,22].…”

Section: Introductionmentioning

confidence: 74%

“…The simulation results for the different best parameter values obtained by the GOA optimization algorithm are presented in Figures (8)(9)(10)(11)(12)(13)(14), where it becomes obvious that the search of this GOA algorithm converges quickly to the optimum values (optimal solution) of the proposed set of parameters of the buried pipeline.…”

Section: Resultsmentioning

confidence: 99%

“…All tests have completed under the same conditions and parameters values. Generally, for the training/testing methodology, the data set is divided into training set (80%) and testing set (20%) [14,39,[42][43][44][45][46]. DIAGNOSTYKA, Vol.…”

Section: Discussionmentioning

confidence: 99%

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Optimization of induced voltage on a buried pipeline from HV power lines using grasshopper algorithm (GOA)

Bouallag¹,

Djejidel²,

Bessedik³

2021

Diagnostyka

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The buried metallic pipeline which parallels to the HV power line is subject to induced voltages from the AC currents flowing in the conductors, these voltages can affect the operating personnel, pipeline associated equipment, and the pipeline integrity. This paper analyses the induced voltage and current on the buried pipeline running parallel to HV power lines. It also presents an optimization procedure of different parameters that affect the level of the induced voltage in the pipeline during normal operating conditions. A comparison study between the proposed optimization algorithms (GOA, GE, DE and PSO) is done with a maximization of a given objective function. The simulation results establish that the GOA algorithm provides a faster convergence and better solution than the other optimization algorithms. Thus, the statistical analysis according to Friedman's rank test confirmed the superiority of this proposed algorithm. Furthermore, the results show that the parameters optimization of the metallic pipeline is an effective approach to provide the best performance for mitigation which is generally sufficient to reduce the induced voltage experienced by the buried metallic pipeline to enforce the safety limit.

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Section: Introductionmentioning

confidence: 74%

Section: Resultsmentioning

confidence: 99%

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Optimization of induced voltage on a buried pipeline from HV power lines using grasshopper algorithm (GOA)

Bouallag¹,

Djejidel²,

Bessedik³

2021

Diagnostyka

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“…In (El-Shorbagy & Ayoub, 2021), El-Shorbagy and Ayoub integrated a local search (LS) strategy into GOA to solve the data clustering problem. The experimental results and statistical analysis confirmed that the combination between GOA and LS outperformed mentioned techniques in the work.…”

Section: Bairathi and Gopalanimentioning

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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“…e most popular hybrid SIAs are hybrid cultural-trajectory-based search [66], hybrid of the ant colony and firefly algorithms (HAFA) [67], hybrid harmony search-cuckoo search (HS/ CS) algorithm [68], hybrid particle swarm optimizationgenetic algorithm (PSO/GA) [69], hybrid krill herd-biogeography-based optimization (KHBBO) algorithm [70], hybrid cat swarm optimization (CSO) [71], hybrid tissue membrane systems (TMS) and the evolution strategy with covariance matrix adaptation (CMA-ES) [72], hybrid grasshopper optimization algorithm-local search (GOA/LS) [73], krill herd-differential evolution (KHDE) [74], hybrid grasshopper optimization algorithm-genetic algorithm (GOA/GA) [75], hybrid bat algorithm with harmony search (BHS) [76], etc.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Fruit Fly Algorithm for Solving Engineering Design Problems

El-Shorbagy

2022

Complexity

Self Cite

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The aim of this article is to present a chaotic fruit fly algorithm (CFFA) as an optimization approach for solving engineering design problems (EDPs). In CFFA, the fruit fly algorithm (FFA), which is recognized for its durability and efficiency in addressing optimization problems, was paired with the chaotic local search (CLS) method, which allows for local exploitation. CFFA will be set up to work in two phases: in the first, FFA will be used to discover an approximate solution, and in the second, chaotic local search (CLS) will be used to locate the optimal solution. As a result, CFFA can address difficulties associated with the basic FFA such as falling into local optima, an imbalance between exploitation and exploration, and a lack of optimum solution acquisition (i.e., overcoming the drawback of premature convergence and increasing the local exploitation capability). The chaotic logistic map is employed in the CLS because it has been demonstrated to be effective in improving the quality of solutions and giving the best performance by many studies. The proposed algorithm is tested by the set of CEC’2005 special sessions on real parameter optimization and many EDPs from the most recent test suite CEC’2020. The results have demonstrated the superiority of the proposed approach to finding the global optimal solution. Finally, CFFA′s results were compared to those of earlier research, and statistical analysis using Friedman and Wilcoxon's tests revealed its superiority and capacity to tackle this type of problem.

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Integrating Grasshopper Optimization Algorithm with Local Search for Solving Data Clustering Problems

Cited by 13 publications

References 37 publications

Optimization of induced voltage on a buried pipeline from HV power lines using grasshopper algorithm (GOA)

Optimization of induced voltage on a buried pipeline from HV power lines using grasshopper algorithm (GOA)

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

Chaotic Fruit Fly Algorithm for Solving Engineering Design Problems

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