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Bare-Bones Based salp swarm algorithm for Text Document Clustering

IEEE ACCESS 2023年 11卷 100010-100028页

作者： Al-Betar, Mohammed Azmi Abasi, Ammar Kamal Al-Naymat, Ghazi Arshad, Kamran Makhadmeh, Sharif Naser Ajman Univ Dept Informat Technol Coll Engn & Informat Technol Ajman U Arab Emirates Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid 19117 Jordan Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Dept Informat Technol Abu Dhabi U Arab Emirates Univ Petra Dept Data Sci & Artificial Intelligence Amman 11196 Jordan

Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The salp swarm algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA's exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones salp swarm algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from the text clustering domain and six scientific papers datasets extracted from the top eight UAE universities. The experimental results demonstrate that the BBSSA algorithm outperforms traditional SSA and nine other optimization algorithms. Furthermore, the BBSSA algorithm achieves better results than the five traditional clustering techniques.

关键词： Global optimization salp swarm algorithm bare bones greedy selection strategy text document clustering

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Breast Cancer Segmentation From Thermal Images Based on Chaotic salp swarm algorithm

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IEEE ACCESS 2020年 8卷 122121-122134页

作者： Ibrahim, Abdelhameed Mohammed, Shaimaa Ali, Hesham Arafat Hussein, Sherif E. Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura 35516 Egypt

Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this paper, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic salp swarm algorithm (CSSA). Although the salp swarm algorithm (SSA) shows superiority in single-objective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by CSSA. The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. CSSA algorithm is developed based on ten chaotic maps to enhance the original SSA convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to real-world thermal images for the breast area. The results demonstrate that the proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm. Moreover, a dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.

关键词： salp swarm algorithm optimization breast cancer chaotic maps image segmentation

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Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 145卷 113122-113122页

作者： Tubishat, Mohammad Idris, Norisma Shuib, Liyana Abushariah, Mohammad A. M. Mirjalili, Seyedali Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Dept Informat Syst Kuala Lumpur 50603 Malaysia Univ Jordan King Abdullah II Sch Informat Technol Comp Informat Syst Dept Amman Jordan Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia

Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of salp swarm algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic algorithm, Particle swarm Optimization, Grasshopper Optimization algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most o

关键词： salp swarm algorithm Classification Feature selection Optimization Machine Learning algorithm Opposition Based Learning

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Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill

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ENGINEERING WITH COMPUTERS 2021年第4期37卷 3519-3540页

作者： Li, Enming Zhou, Jian Shi, Xiuzhi Armaghani, Danial Jahed Yu, Zhi Chen, Xin Huang, Peisheng Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Fankou Lead Zinc Mine Shaoguan 512325 Guangdong Peoples R China

To test the impact of different mixture ratios on backfilling strength in Fankou lead-zinc mine, various mixture ratio designs have been conducted. Meanwhile, to improve the strength of ultra-fine tailings-based cement paste backfill (CPB), two kinds of fibers were utilized in this study, namely polypropylene (PP) fibers and straw fibers. To achieve these, a total of 144 CPB backfilling scenarios with different combinations of influenced factors were tested by uniaxial compressive tests. The test results indicated that polypropylene fibers improve the strength of CPB, while in some scenarios the addition of straw fibers decreases the strength of CPB. In this research, the support vector machine (SVM) technique coupled with three heuristic algorithms, namely genetic algorithms, particle swarm optimization and salp swarm algorithm (SSA), was developed to predict the strength of fiber-reinforced CPB. Also, the optimal performance of metaheuristic algorithms was compared with one fundamental search method, i.e., grid search (GS). The overall performance of four optimal algorithms was calculated by the ranking system. It can be found that these four approaches all presented satisfactory predictive capability. But the metaheuristic algorithms can capture better hyper-parameters for SVM prediction models compared with GS-SVM method. The robustness and generalization of SSA-SVM methods were the most prominent with the R-2 values of 0.9245 and 0.9475 for training sets and testing sets. Therefore, SSA-SVM will be recommended to model the complexity of interactions for fiber-reinforced CPB and predict fiber-reinforced CPB strength.

关键词： Fiber-reinforced strength Compressive strength Support vector machine salp swarm algorithm Hybrid SVM-based technique

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A novel chaotic salp swarm algorithm for global optimization and feature selection

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APPLIED INTELLIGENCE 2018年第10期48卷 3462-3481页

作者： Sayed, Gehad Ismail Khoriba, Ghada Haggag, Mohamed H. Cairo Univ Fac Comp & Informat Giza Egypt Sci Res Grp Egypt Giza Egypt Helwan Univ Fac Comp & Informat Helwan Egypt

salp swarm algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. Recently, chaos theory has been successfully applied to solve these problems. In this paper, a novel hybrid solution based on SSA and chaos theory is proposed. The proposed Chaotic salp swarm algorithm (CSSA) is applied on 14 unimodal and multimodal benchmark optimization problems and 20 benchmark datasets. Ten different chaotic maps are employed to enhance the convergence rate and resulting precision. Simulation results showed that the proposed CSSA is a promising algorithm. Also, the results reveal the capability of CSSA in finding an optimal feature subset, which maximizes the classification accuracy, while minimizing the number of selected features. Moreover, the results showed that logistic chaotic map is the optimal map of the used ten, which can significantly boost the performance of original SSA.

关键词： salp swarm algorithm Global optimization Feature selection Chaos theory

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Improved salp swarm algorithm Based on Levy Flight and Sine Cosine Operator

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IEEE ACCESS 2020年 8卷 99740-99771页

作者： Zhang, J. Wang, J. S. Univ Sci & Technol Liaoning Sch Comp & Software Engn Anshan 114000 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114000 Peoples R China

The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea. Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm. Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation. At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation. In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems. Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated. Finally, LSC-SSA was applied to train muti-layer perceptron neural network. Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms. In addition, the improved algorithm can effectively solve high-dimensional large-scale optimization problems. In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.

关键词： Optimization Sociology Statistics Mathematical model Convergence Classification algorithms Particle swarm optimization salp swarm algorithm levy flight mechanism sine cosine algorithm function optimization muti-layer perceptron

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Robust Load Frequency Control Using Fractional-order TID-PD Approach Via salp swarm algorithm

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IETE JOURNAL OF RESEARCH 2023年第5期69卷 2710-2726页

作者： Sharma, Mandeep Prakash, Surya Saxena, Sahaj BHSB Inst Engn & Technol Dept Elect Engn Lehragaga 148031 India Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Patiala 147004 Punjab India

To preserve frequency and tie-line power flow within a tolerable range in modern intricate and renewable energy sources (RES) integrated power system, an intelligent, expert, and robust load frequency control (LFC) scheme is requisite. This paper proposes a new load frequency control (LFC) approach using a dual-stage controller composed of fractional-order tilted-integral-derivative (TID) and integer-order proportional-derivative (PD) controllers. The recently developed salp swarm algorithm has tuned the parameters of the controller. The proposed scheme exhibits robustness and fast disturbance rejection performance because the tilted-integral-derivative controller is structured in a fractional-order framework, and transient response is improved due to the proportional-derivative controller. The overall activity of the LFC has been further upgraded by incorporating the flexible AC transmission system (FACTS) device namely thyristor-controlled phase shifter (TCPS) unit and energy storage system called redox flow battery (RFB) units. The proposed control approach is applied to diverse types of multi-area multi-source power systems with thermal, hydro, and wind turbine units along with nonlinear functions namely generation rate constraint (GRC), governor dead band (GDB), and communication delay (CD). The simulation results yield improved frequency regulation activity with and without TCPS and RFB using the proposed control approach when compared with the recently developed LFC approaches. Sensitivity analysis also established the robustness of the proposed control approach.

关键词： FACTS fractional-order controller load frequency control redox flow batteries salp swarm algorithm wind

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A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm

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SOFT COMPUTING 2021年第1期25卷 181-206页

作者： Braik, Malik Sheta, Alaa Turabieh, Hamza Alhiary, Heba Al Balqa Appl Univ Dept Comp Sci Salt Jordan Southern Connecticut State Univ Comp Sci Dept 501 Crescent St New Haven CT 06515 USA Taif Univ Dept Informat Technol At Taif Saudi Arabia Al Balqa Appl Univ Dept Comp Informat Syst Salt Jordan

The performance of any meta-heuristic algorithm depends highly on the setting of dependent parameters of the algorithm. Different parameter settings for an algorithm may lead to different outcomes. An optimal parameter setting should support the algorithm to achieve a convincing level of performance or optimality in solving a range of optimization problems. This paper presents a novel enhancement method for the salp swarm algorithm (SSA), referred to as enhanced SSA (ESSA). In this ESSA, the following enhancements are proposed: First, a new position updating process was proposed. Second, a new dominant parameter different from that used in SSA was presented in ESSA. Third, a novel lifetime convergence method for tuning the dominant parameter of ESSA using ESSA itself was presented to enhance the convergence performance of ESSA. These enhancements to SSA were proposed in ESSA to augment its exploration and exploitation capabilities to achieve optimal global solutions, in which the dominant parameter of ESSA is updated iteratively through the evolutionary process of ESSA so that the positions of the search agents of ESSA are updated accordingly. These improvements on SSA through ESSA support it to avoid premature convergence and efficiently find the global optimum solution for many real-world optimization problems. The efficiency of ESSA was verified by testing it on several basic benchmark test functions. A comparative performance analysis between ESSA and other meta-heuristic algorithms was performed. Statistical test methods have evidenced the significance of the results obtained by ESSA. The efficacy of ESSA in solving real-world problems and applications is also demonstrated with five well-known engineering design problems and two real industrial problems. The comparative results show that ESSA imparts better performance and convergence than SSA and other meta-heuristic algorithms.

关键词： salp swarm algorithm Lifetime convergence scheme Meta-heuristic Benchmark test functions Engineering optimization problems

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An efficient binary salp swarm algorithm with crossover scheme for feature selection problems

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KNOWLEDGE-BASED SYSTEMS 2018年第Aug.15期154卷 43-67页

作者： Faris, Hossam Mafarja, Majdi M. Heidari, Ali Asghar Aljarah, Ibrahim Al-Zoubi, Ala' M. Mirjalili, Seyedali Fujita, Hamido Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Birzeit Univ Dept Comp Sci Birzeit Palestine Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran Griffith Univ Inst Integrated & Intelligent Syst Brisbane Qld 4111 Australia Iwate Prefectural Univ IPU Fac Software & Informat Sci Takizawa Iwate Japan

Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed salp swarm algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search algorithms (BGSA), Binary Bat algorithm (BBA), Binary Particle swarm Optimization (BPSO), and Genetic algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.

关键词： Wrapper feature selection salp swarm algorithm Optimization Classification Machine Learning Data Mining Evolutionary Computation swarm Intelligence

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Hybrid Bat and salp swarm algorithm for Feature Selection and Classification of Crisis-Related Tweets in Social Networks

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IEEE ACCESS 2024年 12卷 103908-103920页

作者： Farooqui, Nafees Akhter Hasan, Mohammad Kamrul Noori, Mohammed Ahsan Raza Abd Rahman, Abdul Hadi Islam, Shayla Haleem, Mohammad Ahmad, Sheikh Fahad Khan, Asif Ahmed, Fatima Rayan Awad Babiker, Nissrein Babiker Mohammed Ahmed, Thowiba E. Khan, Atta Ur Rehman Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Malaysia DIT Univ Sch Comp Dehra Dun India UCSI Univ Inst Comp Sci & Digital Innovat Kuala Lumpur Malaysia Era Univ Dept Comp Sci Lucknow India GITAM Deemed be Univ Dept Comp Sci & Engn Hyderabad India Integral Univ Dept Comp Sci Lucknow India Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Comp Sci Dept Al Kharj 16273 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Sci & Humanities Jubail Comp Sci Dept Dammam 35811 Saudi Arabia Univ Bisha Coll Comp & Informat Technol Bisha Informat Syst Dept Bisha 61922 Saudi Arabia Ajman Univ Coll Engn & IT Ajman U Arab Emirates

Twitter is a useful tool for effectively tracking and managing crisis-related incidents. However, due to many irrelevant features in textual data, the problem of high dimensionality arises, which eventually increases the computational cost and decreases classification performance. Thus, to handle such a problem, this work presents a Spark-based hybrid binary Bat (BBA) and binary salp swarm algorithm (BSSA) named SBBASSA for feature selection and classification of crisis-related tweets. In the proposed technique, the hybridization of standard BBA and BSSA algorithms is performed to enhance their exploration capabilities, then the combined algorithm is implemented in parallel using Apache Spark framework to reduce the overall execution time during the feature selection process. A support vector machine (SVM) classifier is applied during the wrapper-based feature subset selection and classification. The performance of the proposed SBBASSA was analyzed on six benchmark crisis tweet datasets, namely Hurricane Sandy, Boston Bombings, Oklahoma Tornado, West Texas Explosion, Alberta Floods, and Queensland Floods, and then compared with standard BSSA, BBA, and binary particle swarm optimization (BPSO). Results showed that SBBASSA performed competently in the feature selection and classification, outperformed other algorithms in crisis tweet classification, and achieved the highest accuracy with the lowest feature set in a reduced execution time.

关键词： Feature extraction Disasters Classification algorithms Social networking (online) Support vector machines Vectors Deep learning Apache spark bat algorithm crisis tweet classification feature selection salp swarm algorithm

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