Spider wasp optimizer: a novel meta-heuristic optimization algorithm

Abdel‐Basset, Mohamed; Mohamed, Reda; Jameel, Mohammed; Abouhawwash, Mohamed

doi:10.1007/s10462-023-10446-y

Cited by 74 publications

(8 citation statements)

References 70 publications

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“…The SWO has three control parameters, namely, the TR tradeoff rate, the CR crossover probability, and the N SWO m minimum population size. The values given in the original article, [54] 0.3 for TR, 0.2 for CR, and 20 for N SWO m are used.…”

Section: Swomentioning

confidence: 99%

“…The time utilization of WLS, SWO, RECAA, FFA, KOA, and RIME algorithms are given by Equations ( 33)-( 38), respectively. The computational time depends on the number of populations (N WLS ) and number of dimensions (D) in WLS algorithm, [53] number of populations (N SWO ), number of dimensions (D), and t SWO,max the maximum number of functions evaluation, in SWO algorithm, [54] number of populations (N RECAA ), number of dimensions (D), n s number of smart-cells, and n ne the number of neighbors per smart cell in RECAA algorithm, [56] number of populations (N FFA ), number of dimensions (D), and T FFA maximum number of iterations in FFA algorithm, [57] number of populations (N KOA ) and T KOA maximum number of iterations in KOA algorithm, [58] and lastly the number of populations (N RIME ) in RIME algorithm. [59] The computational time results in seconds for 30 runs in solving the PV parameter extraction problem are given in Table 14.…”

Section: Computational Timementioning

confidence: 99%

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Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Çetinbaş

2024

Global Challenges

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This study presents the parameter extraction of single, double, and triple‐diode photovoltaic (PV) models using the weighted leader search algorithm (WLS). The primary objective is to develop models that accurately reflect the characteristics of PV devices so that technical and economic benefits are maximized under all constraints. For this purpose, 24 models, 6 for two different PV cells, and 18 for six PV modules, whose experimental data are publicly available, are developed successfully. The second objective of this research is the selection of the most suitable algorithm for this problem. It is a significant challenge since the evaluation process requires using advanced statistical tools and techniques to determine the reliable selection. Therefore, seven brand‐new algorithms, including WLS, the spider wasp optimizer, the shrimp and goby association search, the reversible elementary cellular automata, the fennec fox optimization, the Kepler optimization, and the rime optimization algorithms, are tested. The WLS has yielded the smallest minimum, average, RMSE, and standard deviation among those. Its superiority is also verified by Friedman and Wilcoxon signed‐rank test based on 144 pairwise comparisons. In conclusion, it is demonstrated that the WLS is a superior algorithm in PV parameter extraction for developing accurate models.

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Section: Swomentioning

confidence: 99%

Section: Computational Timementioning

confidence: 99%

Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Çetinbaş

2024

Global Challenges

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“…Other notable examples include the Shuffled Frog-Leaping Algorithm (SFLA) [44], which simulates the memeinspired problem-solving strategy of frogs, and the Slime Mould Algorithm (SMA) [45], derived from the nutrient foraging behavior of slime molds. The Spider Wasp Optimization (SWO) [46] and the Border Collie Optimization (BCO) [47] also take cues from the predatory tactics of spider wasps and the herding strategy of border collies, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Four Vector Intelligent Metaheuristic andDE for Solving Complex and Engineering DesignOptimization Problems

fakhouri,

Ishtaiwi,

Makhadmeh

et al. 2024

Preprint

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Metaheuristic algorithms play a pivotal role in addressing complex and nonlinear optimization challenges. However, traditional optimizers often struggle to locate the global optimum in intricate problem spaces, necessitating the development of hybrid methodologies. This paper introduces FVIMDE, a cutting-edge hybrid optimization algorithm that amalgamates the innovative Four Vector Intelligent Metaheuristic (FVIM) with the proven robustness of Differential Evolution (DE). Designed to adeptly maneuver through the complex terrains of various optimization and engineering design problems, FVIMDE is tested and evaluated over three well-known benchmark suites—CEC2017, CEC2022, and a specially set of 50 benchmark functions. statistacel tests has been calculated including mean, standard deviation and the wilcoxon sum rank test. Further FVIMDE has been compared with state-of-art optimizers. Subsequent applications of FVIMDE to five intricate structural engineering challenges further validate its effectiveness. Comparative analyses against several leading optimization algorithms highlight the superior adaptability and robustness of FVIMDE, showcasing its exceptional performance and significant improvements over traditional methods in diverse optimization environments.

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“…In addition, there are also several studies trying to improve the heuristic algorithms of transport systems. Abdel-Basset et al presented a new nature-inspired metaheuristic algorithm named the spider wasp optimization (SWO) algorithm, which was based on replicating the hunting, nesting, and mating behaviors of the female spider wasps in nature [37]. Kaya et al aimed comparison of the performance of seven metaheuristic training algorithms in the neuro-fuzzy training for maximum power point tracking (MPPT), including particle swarm optimization (PSO), harmony search (HS), cuckoo search (CS), artificial bee colony (ABC) algorithm, bee algorithm (BA), differential evolution (DE) and flower pollination algorithm (FPA) [38].…”

Section: Literature Reviewsmentioning

confidence: 99%

Optimization of Taxi Allocation for Minimizing CO2 Emissions Based on Heuristics Algorithms

et al. 2023

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Recently, the rapid climate change caused by increasing CO2 emissions has become a global concern. Efficient transportation systems are necessary to reduce CO2 emissions in cities. Taxi services are an essential part of the transportation system, both in urban areas with high demand and in rural areas with inadequate public transportation. Inefficient taxi services cause problems such as increased idle times, resulting in increased CO2 emissions. This study proposes a taxi allocation model that minimizes taxi idle time costs for efficient taxi service operation. We also propose three heuristic algorithms to solve the proposed model. At last, we conduct a case study by using real taxi data in Nagaoka, Japan. By comparing the three algorithms, the dynamic greedy algorithm produced the best result in terms of idle time cost and CPU time. The findings indicate that by minimizing idle time costs and reducing the number of taxis, it is possible to achieve a significant 81.84% reduction in CO2 emissions within the transportation sector. Further, in order to estimate the idle time costs the sensitivity of demand is considered.

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Spider wasp optimizer: a novel meta-heuristic optimization algorithm

Cited by 74 publications

References 70 publications

Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Hybrid Four Vector Intelligent Metaheuristic andDE for Solving Complex and Engineering DesignOptimization Problems

Optimization of Taxi Allocation for Minimizing CO2 Emissions Based on Heuristics Algorithms

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