A Hybrid Salp Swarm Algorithm with $$\beta $$-Hill Climbing Algorithm for Text Documents Clustering

Abasi, Ammar Kamal; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Alyasseri, Zaid Abdi Alkareem; Makhadmeh, Sharif Naser; Al-Laham, Mohamad; Naim, Syibrah

doi:10.1007/978-981-33-4191-3_6

Cited by 13 publications

(6 citation statements)

References 59 publications

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“…The first salp of the chain is known as the leader, and rest of them are called the followers. The position of all salps (N p ) is stored in a two-dimensional matrix X given in equation (1). These salps looking for a food source that implies the target of the swarm.…”

Section: Salp Swarm Algorithm (Ssa)mentioning

confidence: 99%

“…The important characteristics like, simple structure, robustness, and scalability, makes SSA an efficient method for solving various kinds of real world problems (e.g., engineering design and optimization [80], feature selection [38], job shop scheduling [75], optimal power flow problem [22], parameter optimization of power system stabilizer [21], power generation [68], image segmentation [39,84], parameter estimation for soil water retention curve [95], PID controller for AVR system [20], target localization [54]). Also, SSA shows the following outstanding features like: (1) It can be easily applied to different optimization problems without adjusting other parameters except population size and stopping criterion and it is worth mentioning that these parameters are essential for all MHAs; (2) It has a powerful neighbourhood search ability and it can easily fitted for wide search space [8]. Therefore, these advantages make SSA an efficient technique and a rapid growth of the SSA studies has also been noticed recently.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, two different search strategies levyfight search and opposition-based learning are utilized to increase the convergence speed and also, to establish an appropriate balance between exploration and exploitation. Abasi et al, [1] proposed a new hybrid algorithm named H-SSA combining the SSA and the β−hill climbing algorithm (βHC) to enhance the convergence speed as well as the local searching ability of the conventional SSA. A new type improved SSA based on inertia weight search mechanism is introduced by Hegazy et al [33] to maximize reliability, optimizing accuracy, and convergence speed.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

2022

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A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO) is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints. Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour of salps. It is an efficient swarm optimization technique that has been used to solve various kinds of complex optimization problems. However, SSA suffers a slow convergence rate due to its poor exploitation ability. In view of this inadequacy and resulting in a better balance between exploration and exploitation, the proposed hybrid method HSSATLBO has been developed where the searching procedures of SSA are renovated based on the TLBO algorithm. The good global search ability of SSA and fast convergence of TLBO help to maximize the system reliability through the choices of redundancy and component reliability. The performance of the proposed HSSATLBO algorithm has been demonstrated by seven well-known benchmark problems related to reliability optimization that includes series system, complex (bridge) system, series-parallel system, overspeed protection system, convex system, mixed series-parallel system, and large-scale system with dimensions 36, 38, 40, 42 and 50. After illustration, the outcomes of the proposed HSSATLBO are compared with several recently developed competitive meta-heuristic algorithms and also with three improved variants of SSA. Additionally, the HSSATLBO results are statistically investigated with the wilcoxon sign-rank test and multiple comparison test to show the significance of the results. The experimental results suggest that HSSATLBO significantly outperforms other algorithms and has become a remarkable and promising tool for solving RRAP.

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Section: Salp Swarm Algorithm (Ssa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

2022

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“…Abasi et al presented a new hybrid version of SSA and β-hill climbing and named it H-SSA to increase the worth of initial candidate solutions and boost the local search ability of SSA with an enhanced convergence rate. They evaluated the performance of H-SSA in data clustering using five standard datasets and observed its superior convergence rate and accuracy in text document classification problems [17]. Salgotra et al suggested an improvised version of SSA to make the SSA self-adaptive and named it Adaptive SSA (ASSA).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dimensional Search Based Orthogonal Experimentation SSA (ADOX-SSA) for training RBF Neural Network and optimal Feature Selection

Mahapatra

Panda

Pattanayak

2022

Preprint

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Salp Swarm Algorithm (SSA) is a new stochastic approach for solving optimization issues based on the ideas of swarm intelligence. The ease of implementation and lower number of parameters to fine-tune are responsible for SSA's success and universal acceptance among researchers. The typical SSA method suffers from local optima entrapment and a poor convergence rate while dealing with more complex situations because of a lack of population density and inadequate local exploitation. To overcome such challenges, this study suggests a hybrid kind of SSA called Adaptive-Dimensional-Search based Orthogonal Experimentation SSA (ADOX-SSA). Furthermore, the inclusion of an ADOX operator increases population diversity, intensifies local exploitation, and strengthens the SSA standards. Consequently, the balance between the processes of exploration and exploitation is attuned, resulting in a faster rate of convergence than the normal SSA. To validate the ADOX-SSA technique's competency, 14 basic functions and 30 advanced standard functions were chosen following IEEE-CEC-2014. The findings of the proposed strategy have also been compared to those of recent metaheuristic approaches. Two nonparametric tests were employed to demonstrate statistical significance as Friedman and Holms approach. Additionally, the suggested ADOX-SSA approach is castoff for training Radial Basis Function Neural Network (RBFNN) by selecting datasets from the UCI depository. Finally, the same suggested approach is utilized to select the best features from benchmark datasets while maintaining accuracy and minimizing neural network complexity.

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“…This model has been used to solve and optimize three different photovoltaic models, which are SM55, ST40, and KC200GT. In [24], the SSO algorithm is hybridized with the BHC technique and has been used to solve the text clustering problem. The BHC technique has been used in this model to increase local search capabilities and speed up convergence.…”

mentioning

confidence: 99%

An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

Alizadeh

Gharehchopogh

Màsdàrí

et al. 2023

Preprint

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Optimization is necessary for solving and improving the solution of various complex problems. Every meta-heuristic algorithm can have a weak point, and multiple mechanisms and methods can be used to overcome these weaknesses. We can use hybrid meta-heuristic algorithms to arrive at an efficient algorithm. This paper presents a new and intelligent approach by hybridizing meta-heuristic algorithms and using different mechanisms simultaneously without significantly increasing the time complexity. For this purpose, two algorithms, Salp Swarm Optimization(SSO) and the African Vulture Optimization Algorithm (AVOA) have been hybridized. And to improve the optimization process of the Modified Choice Function and Learning Automata mechanisms. In addition, two other improving mechanisms, named Opposition-Based Learning (OBL) and β-hill climbing (BHC) technique, have been presented and integrated with the AVOA-SSA algorithm. Fifty-two standard benchmarks were used to test and evaluate the AVOA-SSA algorithm. Finally, an improved version of the Extreme Learning Machine(ELM) classifier has been used with real stock market data for stock market prediction. The obtained results indicate the excellent and acceptable performance of the AVOA-SSA algorithm in `solving optimization problems and has been able to achieve high-quality solutions.

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A Hybrid Salp Swarm Algorithm with $$\beta $$-Hill Climbing Algorithm for Text Documents Clustering

Cited by 13 publications

References 59 publications

A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

Adaptive Dimensional Search Based Orthogonal Experimentation SSA (ADOX-SSA) for training RBF Neural Network and optimal Feature Selection

An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

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