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Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization

APPLIED INTELLIGENCE 2022年第7期52卷 7922-7964页

作者： Wang, Zongshan Ding, Hongwei Yang, Zhijun Li, Bo Guan, Zheng Bao, Liyong Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China Yunnan Educ Dept Kunming 650500 Yunnan Peoples R China

salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmeta-heuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous m

关键词： salp swarm algorithm Lens opposition-based learning Orthogonal experiment design Dynamic learning Global optimization Engineering design optimization Photovoltaic models Parameter extraction Robot path planning

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ESSAWOA: Enhanced Whale Optimization algorithm integrated with salp swarm algorithm for global optimization

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 1期38卷 797-814页

作者： Fan, Qian Chen, Zhenjian Zhang, Wei Fang, Xuhua Fuzhou Univ Coll Civil Engn Fuzhou 350116 Peoples R China Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China Fujian Acad Bldg Res Fuzhou 350025 Peoples R China

In this paper, a novel hybrid meta-heuristic algorithm called ESSAWOA is proposed for solving global optimization problems. The main idea of ESSAWOA is to enhance Whale Optimization algorithm (WOA) by combining the mechanism of salp swarm algorithm (SSA) and Lens Opposition-based Learning strategy (LOBL). The hybridization process includes three parts: First, the leader mechanism with strong exploitation of SSA is applied to update the population position before the basic WOA operation. Second, the nonlinear parameter related to the convergence property in SSA is introduced to the two phases of encircling prey and bubble-net attacking in WOA. Third, LOBL strategy is used to increase the population diversity of the proposed optimizer. The hybrid design is expected to significantly enhance the exploitation and exploration capacity of the proposed algorithm. To investigate the effectiveness of ESSAWOA, twenty-three benchmark functions of different dimensions and three classical engineering design problems are performed. Furthermore, SSA, WOA and seven other well-known meta-heuristic algorithms are employed to compare with the proposed optimizer. Our results reveal that ESSAWOA can effectively and quickly obtain the promising solution of these optimization problems in the search space. The performance of ESSAWOA is significantly superior to the basic WOA, SSA and other meta-heuristic algorithms.

关键词： salp swarm algorithm Whale Optimization algorithm Nonlinear parameter Lens Opposition-based Learning Hybridization

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A hybrid model using teaching-learning-based optimization and salp swarm algorithm for feature selection and classification in digital mammography

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第9期12卷 8793-8808页

作者： Thawkar, Shankar Hindustan Coll Sci & Technol Dept Informat Technol Mathura Uttar Pradesh India

Feature selection is the most important step in the design of a breast cancer diagnosis system. The basic objective of the proposed methodology is to reduce the size of the feature space to improve the performance of the classification system. In this article, a hybrid teaching-learning based optimization (TLBO) with a salp swarm algorithm (SSA) is presented to select the features with an artificial neural network as a fitness evaluator. The features selected by TLBO-SSA are evaluated using an adaptive neuro-fuzzy inference system. The performance of the proposed methodology is tested over 651 mammograms. The experimental results show that TLBO-SSA appears to be the best when compared with the basic TLBO algorithm. TLBO-SSA archived an accuracy of 98.46% with 98.81% sensitivity, 98.08% specificity, 0.9852 F-score, 0.9692 Cohen's kappa coefficient, and area under curve A(Z) = 0.997 +/- 0.001. Again the robustness of the proposed TLBO-SSA method is tested using a benchmark dataset obtained from the UCI repository. The result obtained by TLBO-SSA is compared with the Genetic algorithm. The results show that TLBO-SSA is better than the Genetic algorithm.

关键词： Teaching learning based optimization salp swarm algorithm Adaptive neuro-fuzzy inference system Artificial neural network Feature selection Mammography Classification

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The Influence of salp swarm algorithm-Based Feature Selection on Network Anomaly Intrusion Detection

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IEEE ACCESS 2021年 9卷 112466-112477页

作者： Alsaleh, Alanoud Binsaeedan, Wojdan Imam Mohammad Ibn Saud Islam Univ IMSIU Coll Comp & Informat Sci Dept Comp Sci Riyadh 11432 Saudi Arabia

Network security plays a critical role in our lives because of the threats and attacks to which we are exposed, which are increasing daily;these attacks result in a need to develop various protection methods and techniques. Network intrusion detection systems (NIDSs) are a way to detect malicious network attacks. Many researchers have focused on developing NIDSs based on machine learning (ML) approaches to detect diverse attack variants. ML approaches can automatically discover the essential differences between normal and abnormal data by analysing the features of a large dataset. For this purpose, many features are typically extracted without discrimination, increasing the computational complexity. Then, by applying a feature selection method, a subset of features is selected from the whole feature set with the aim of improving the performance of ML-based detection methods. The salp swarm algorithm (SSA) is a nature-inspired optimization algorithm that has demonstrated efficiency in minimizing the processing challenges faced in performing optimization for feature selection problems. This research investigates the impact of the SSA on improving ML-based network anomaly detection using various ML classifiers, including the extreme gradient boosting (XGBoost) and Naive Bayes (NB) algorithms. Experiments were conducted on standard datasets for comparison. Specifically, two datasets explicitly focused on network intrusion attacks were used: UNSW-NB15 and NSL-KDD. The experimental results show that the proposed method is more effective in improving the performance of anomaly NIDSs in terms of the F-measure, recall, detection rate, and false alarm rate on both datasets, outperforming state-of-the-art techniques recently proposed in the literature.

关键词： Classification algorithms Anomaly detection Feature extraction Security Optimization Support vector machines Particle swarm optimization Network security anomaly detection swarm intelligence salp swarm algorithm feature selection machine learning XGBoost Naive Bayes

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Improved salp swarm algorithm for solving single-objective continuous optimization problems

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APPLIED INTELLIGENCE 2022年第15期52卷 17217-17236页

作者： Abed-Alguni, Bilal H. Paul, David Hammad, Rafat Yarmouk Univ Dept Comp Sci Irbid Jordan Univ New England Sch Sci & Technol Armidale NSW Australia Yarmouk Univ Dept Comp Informat Syst Irbid Jordan

The salp swarm algorithm (SSA) is an effective single-objective optimization algorithm that was inspired by the navigating and foraging behaviors of salps in their natural habitats. Although SSA was successfully tailored and applied to solve various types of optimization problems, it often suffers from premature convergence and typically does not perform well with high-dimensional optimization problems. This paper introduces an Improved SSA (ISSA) algorithm to enhance the performance of SSA in solving single-objective continuous optimization problems. ISSA has four characteristics. First, it employs Gaussian Perturbation to improve the diversity of initial population. Second, it uses highly disruptive polynomial mutation (HDPM) to update the leader salp in the salp chain. Third, it uses the Laplace crossover operator to improve its exploration ability. Fourth, it uses a new opposition learning method called Mixed Opposition-based Learning (MOBL) to improve its convergence rate and exploration ability. A set of 14 standard benchmark functions was used to evaluate the performance of ISSA and compare it to three variations of SSA (SSA, Hybrid SSA with Particle swarm Optimization HSSAPSO Singh et al. (2020) and Enhanced SSA (ESSA) Zhang et al. (2020)). The overall experimental and statistical results indicate that ISSA is a better optimization algorithm than the other SSA variations. Further, the single-objective IEEE CEC 2014 (IEEE Congress on Evolutionary Computation 2014) functions were used to evaluate and compare the performance of ISSA to 18 well-known and state-of-the-art optimization algorithms (Exploratory Cuckoo Search (ECS) Abed-alguni (2021)), Grey Wolf Optimizer (GWO) Mirjalili and Mirjalili (Advances in Engineering Software, 69, 46-61, 2014), Distributed Grey Wolf Optimize (DGWO) Abed-alguni and Barhoush (2018), Cuckoo Search (CS) Yang and Deb (2009), Distributed adaptive differential evolution with linear population size reduction evolution (L-SHADE) Tana

关键词： salp swarm algorithm Gaussian perturbation Mixed opposition-based learning Laplace crossover operator

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An efficient refracted salp swarm algorithm and its application in structural parameter identification

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ENGINEERING WITH COMPUTERS 2022年第1期38卷 175-189页

作者： Fan, Qian Chen, Zhenjian Li, Zhao Xia, Zhanghua Lin, Youqin Fuzhou Univ Coll Civil Engn Fuzhou 350116 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Kowloon Hong Kong 999077 Peoples R China

Efficient and accurate structural parameter identification is critical for the practical application of structural health monitoring. In this paper, a novel algorithm named refracted salp swarm algorithm (RSSA) is proposed and applied to identify structural parameters. Firstly, the basic salp swarm algorithm is improved by refracted opposition-based learning strategy, multi-leader mechanism and adaptive conversion parameter strategy. The superiority of the proposed algorithm is verified by experiments of eight benchmark functions of various types and dimensions. Secondly, a new type of structural parameter identification (SPI) model is established by combining RSSA and the Newmark integration method, which is mainly used to solve the optimization problem based on structural acceleration, thereby identifying structural parameters such as stiffness, mass and damping ratio. Numerical simulation test of seven-floor frame proves that the new proposed RSSA could be successfully applied in the SPI model. Compared with other heuristic algorithms, RSSA can obtain more accurate recognition results under the circumstances incomplete measurement data and low signal-to-noise ratio.

关键词： Structural parameter identification salp swarm algorithm Refracted opposition-based learning Newmark integration method

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An efficient salp swarm algorithm based on scale-free informed followers with self-adaption weight

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APPLIED INTELLIGENCE 2023年第2期53卷 1759-1791页

作者： Wang, Chao Xu, Ren-qian Ma, Lei Zhao, Jie Wang, Lu Xie, Neng-gang Cheong, Kang Hao Anhui Polytech Univ Key Lab Mech Wuhu 241000 Anhui Peoples R China Anhui Prov Key Lab Multidisciplinary Management & Maanshan 243002 Anhui Peoples R China Anhui Univ Technol Dept Management Sci & Engn Maanshan 243002 Anhui Peoples R China Singapore Univ Technol & Design SUTD Sci Math & Technol Cluster 8 Somapah Rd Singapore 487372 Singapore

Meta-heuristic algorithms are often leveraged to solve complicated engineering optimization and scientific problems. salp swarm algorithm is one of the most useful meta-heuristic algorithms in recent years. To alleviate the slow convergence speed of the salp swarm algorithm, as well as the tendency to fall into local minima, we have proposed an efficient salp swarm algorithm called E-SSA, which combines the effective evolutionary strategies of basic salp swarm algorithm and two efficient mechanisms named self-adaption weight and scale-free network. These two mechanisms have been integrated into the follower evolution process of the algorithm to achieve the balance of exploration and exploitation. The performance of the E-SSA is benchmarked against a suit of CEC'2019 series functions and 23 commonly used international benchmarks. The algorithm is further validated via three engineering application problems. The experimental results indicate that the improved algorithm has clear advantages in optimization performance compared with other existing heuristic algorithms.

关键词： salp swarm algorithm Scale-free topology Self-adaption weight Exploration Exploitation Meta-heuristic algorithms

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Boosting Marine Predators algorithm by salp swarm algorithm for Multilevel Thresholding Image Segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第12期81卷 16707-16742页

作者： Abualigah, Laith Al-Okbi, Nada Khalil Abd Elaziz, Mohamed Houssein, Essam H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Univ Baghdad Coll Sci Women Dept Comp Sci Baghdad Iraq Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk 634050 Russia Minia Univ Fac Comp & Informat Al Minya 61519 Egypt

Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators algorithm (MPA) with salp swarm algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc. horizontal ellipsis ) are employed to evaluate the proposed segmentation method's effectiveness. Several benchmark images are used to validate the proposed algorithm's performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature.

关键词： Image segmentation Multilevel thresholding Meta-heuristic algorithms Marine Predator algorithm salp swarm algorithm

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Self-adaptive salp swarm algorithm for optimization problems

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SOFT COMPUTING 2022年第18期26卷 9349-9368页

作者： Kassaymeh, Sofian Abdullah, Salwani Al-Betar, Mohammed Azmi Alweshah, Mohammed Al-Laham, Mohamad Othman, Zalinda Aqaba Univ Technol Fac Informat Technol Software Engn Dept Aqaba Jordan Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimizat Res Grp Bangi Selangor Malaysia Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Al Balqa Appl Univ Prince Abdullah bin Ghazi Fac Informat & Commun T Dept Comp Sci Al Salt Jordan Aqaba Univ Technol Fac Informat Technol Artificial Intelligence Dept Aqaba Jordan Al Balqa Appl Univ Amman Univ Coll MIS Dept Amman Jordan

In this paper, an enhanced version of the salp swarm algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) Diversification of the SSA population referred as SSA(std), (ii) SSA parameters are tuned using a self-adaptive technique-based genetic algorithm (GA) referred as SSA(GA-tuner). The novelty of developing a self-adaptive SSA is to enhance its performance through balancing search exploration and exploitation. The enhanced SSA versions are evaluated using twelve benchmark functions. The diversified population of SSA(std) enhances convergence behavior, and self-adaptive parameter tuning of SSA(GA-tuner) improves the convergence behavior as well, thus improving performance. The comparative evaluation against nine well-established methods shows the superiority of the proposed SSA versions. The enhancement amount in accuracy was between 2.97 and 99% among all versions of algorithm. In a nutshell, the proposed SSA version shows a powerful enhancement that can be applied to a wide range of optimization problems.

关键词： salp swarm algorithm Initial population diversity Self-adaptive parameters tuning Swann algorithms Optimization Metaheuristic

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Improved salp swarm algorithm based on the levy flight for feature selection

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JOURNAL OF SUPERCOMPUTING 2021年第11期77卷 12399-12419页

作者： Balakrishnan, K. Dhanalakshmi, R. Khaire, Utkarsh Mahadeo Indian Inst Informat Technol Tiruchirappalli Dept Comp Sci & Engn Tiruchirappalli 620009 Tamil Nadu India Indian Inst Informat Technol Dharwad Dept Data Sci & Intelligent Syst Sattur 580009 Karnataka India

The fields of data science and data mining are enduring high-dimensionality issues because of a high volume of data. Conventional machine learning techniques give disgruntled responses to high-dimensional datasets. Feature selection is used to get the appropriate information from the dataset to reduce the dimensionality of the data. The recently proposed salp swarm algorithm (SSA) is a population-based meta-heuristic optimization technique inspired by the Sea salps swarming technique. SSA failed to converge initial random solutions to the global optimum owing to its complete dependency on the number of iterations for the process of exploration and exploitation. The proposed improved SSA (iSSA) aims to enhance the ability of salps to explore divergent areas by randomly updating its location. Randomizing the salps location via Levy flight enriches the exploitation potential of SSA resulting in it converging the model toward the global optima. The performance of the proposed iSSA is investigated using six different high-dimensional microarray datasets. While comparing the ability to converge, it is understood that the proposed model outperforms SSA providing 0.1033% more confidence in the selected features. The results of the simulation revealed that the iSSA can provide better competitive and significant results compared to SSA.

关键词： Feature selection High-dimensional dataset Levy flight Meta-Heuristic Optimization salp swarm algorithm

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