Solving the Set Covering Problem Using Spotted Hyena Optimizer and Autonomous Search

Soto, Ricardo; Crawford, Broderick; Vega, Emanuel; Gómez, Álvaro; Gómez–Pulido, Juan A.

doi:10.1007/978-3-030-22999-3_73

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…Ricardo et al 20 proposed a modified SHO by incorporating autonomous search (AS) to solve set covering problems. The advantage of this proposed SHO-SA is the good convergence speed.…”

Section: Related Workmentioning

confidence: 99%

“…Apart from this success, SHO suffers from premature convergence in local optima due to a lack of exploration for some complex multimodal optimization problems. In order to resolve the issue of obtaining a better exploration strength and maintaining a good balance between exploration (diversification) and exploitation (intensification), several modified versions of SHO algorithms such as SHO-AS 20 , ISHO 21 , MSHO 22 , OBL-MO-SHO 23 , STS-SHO 24 were developed in the recent times. Though these modified SHO algorithms perform better than the classical SHO algorithm in different optimization problems, they still suffer premature convergence in local optima while solving some complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…From the Wilcoxon signed-rank test, we can clearly see every individual comparison between QSHO and the other algorithms. QSHO has won for19,17,23,20,20,28, and 28 functions compared with SHO, ISHO, MSHO, OBL-MO-SHO, STS-SHO, QSSA, and ChOA. Finally, QSHO has failed for eight functions, eleven functions, five functions, eight functions, and eight functions compared with SHO, ISHO, MSHO, OBL-MO-SHO, and STS-SHO, respectively.…”

mentioning

confidence: 99%

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QSHO: Quantum Spotted Hyena Optimizer for Global Optimization

Si,

Miranda,

Nandi

et al. 2024

Preprint

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Spotted Hyena Optimizer (SHO) is a population-based metaheuristic algorithm inspired by the spotted hyenas’ social behavior, and it is developed for solving global optimization problems. SHO has shown superior performance over its competitive metaheuristic algorithms in solving benchmark function optimization problems and engineering design problems. However, it suffers premature convergence in local optima due to its lack of exploration while solving multi-modal optimization problems. This article proposes an improved SHO, namely quantum SHO (QSHO), inspired by quantum computing. The QSHO implements a quantum computing mechanism to promote its exploration ability. The novel method is tested on well-known IEEE CEC2013 and IEEE CEC2017 benchmark suits with 30 and 50 dimensions and four real-world engineering optimization problems. The results of QSHO are compared with that of Classical SHO, improved SHO (ISHO), Modified SHO (MSHO), Oppositional SHO with mutation operator (OBL-MO-SHO), SHO with space transformation search (STS-SHO), Quantum Salp Swarm Algorithm (QSSA), and Chimp Optimization Algorithm (ChOA). The results are analyzed using the Wilcoxon Signed Rank Test (WSRT) and Friedman Test. The empirical results show that QSHO statistically outperforms other compared algorithms for both benchmark problem suits with both 30 and 50 dimensions. In addition, we have assessed the QSHO in four real-world engineering optimization problems and the QSHO statistically outperforms the competitive algorithms.

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“…Ricardo et al 20 proposed a modified SHO by incorporating autonomous search (AS) to solve set covering problems. The advantage of this proposed SHO-SA is the good convergence speed.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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QSHO: Quantum Spotted Hyena Optimizer for Global Optimization

Si,

Miranda,

Nandi

et al. 2024

Preprint

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“…In 2019, Jia et al hybridized SHO algorithm with another popular metaheuristic algorithm termed as simulated annealing, to unravel feature selection problem by considering 20 standard datasets. Recently, Soto et al contributed the notion about set covering problem by the help of SHO and the same is utilized for solving diversified practical problems. Luo et al integrated SHO with lateral inhibition concept to resolve complicated image matching problem.…”

Section: Background Researchmentioning

confidence: 99%

Improved spotted hyena optimizer with space transformational search for training pi‐sigma higher order neural network

Panda

Majhi

2020

Computational Intelligence

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Spotted hyena optimizer (SHO) is a recently developed swarm‐based algorithm in the field of metaheuristic research, for solving realistic engineering design constraint and unconstrained difficulties. To resolve complicated nonlinear physical world tasks, at times, SHO reveals deprived performance concerning to explorative strength. So, to enhance the explorative strength along with exploitation in the search region, an attempt has been made by proposing the enhanced version of classical SHO. The suggested method is designated as space transformation search (STS)‐SHO. In STS‐SHO, a new evolutionary technique named as STS technique has been incorporated with original SHO. The suggested method has been assessed by IEEE CEC 2017 benchmark problems. The efficacy of the said method has been proven by using standard measures such as given performance metrics in CEC 2017, complexity analysis, convergence analysis, and statistical implications. Further as real‐world application, the said algorithm has been applied to train pi‐sigma neural network by means of 13 benchmark datasets considered from UCI depository. From the article it can be concluded that the suggested method STS‐SHO is an effective and trustworthy algorithm, which has the ability to resolve real‐life optimization complications.

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“…This is a novel concept, which has not been extensively considered in the literature. For instance, we may cite the following works defining an autonomous strategy for guiding specific metaheuristics [51][52][53]. On this basis, in this work, the authors study how to provide autonomous adaptability to the water cycle algorithm, which is a metaheuristic that was successfully applied to solve several optimization problems [54][55][56][57].…”

Section: Related Workmentioning

confidence: 99%

Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm

et al. 2019

Self Cite

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Metaheuristics are multi-purpose problem solvers devoted to particularly tackle large instances of complex optimization problems. However, in spite of the relevance of metaheuristics in the optimization world, their proper design and implementation to reach optimal solutions is not a simple task. Metaheuristics require an initial parameter configuration, which is dramatically relevant for the efficient exploration and exploitation of the search space, and therefore to the effective finding of high-quality solutions. In this paper, the authors propose a variation of the water cycle inspired metaheuristic capable of automatically adjusting its parameter by using the autonomous search paradigm. The goal of our proposal is to explore and to exploit promising regions of the search space to rapidly converge to optimal solutions. To validate the proposal, we tested 160 instances of the manufacturing cell design problem, which is a relevant problem for the industry, whose objective is to minimize the number of movements and exchanges of parts between organizational elements called cells. As a result of the experimental analysis, the authors checked that the proposal performs similarly to the default approach, but without being specifically configured for solving the problem.

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Solving the Set Covering Problem Using Spotted Hyena Optimizer and Autonomous Search

Cited by 6 publications

References 15 publications

QSHO: Quantum Spotted Hyena Optimizer for Global Optimization

QSHO: Quantum Spotted Hyena Optimizer for Global Optimization

Improved spotted hyena optimizer with space transformational search for training pi‐sigma higher order neural network

Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm

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