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

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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Improved salp swarm algorithm based on Newton interpolation and cosine opposition-based learning for feature selection

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 219卷 544-558页

作者： Zhang, Hongbo Qin, Xiwen Gao, Xueliang Zhang, Siqi Tian, Yunsheng Zhang, Wei Changchun Univ Technol Sch Math & Stat Changchun 130012 Peoples R China Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

Feature selection (FS) is one of the most critical tasks in data mining, which aims to reduce the dimensionality of the data and maximize classification accuracy. The FS problem can be treated as an NP-hard problem. Recently, various swarm intelligent (SI) algorithms have been employed to deal with the FS problem to solve the expensive computation of the exact method. However, the performance of the SI algorithms is limited because these algorithms do not comprehensively take the characteristics of the FS problem into consideration. Therefore, a promising salp swarm algorithm called NCSSA is presented to solve this problem. In NCSSA, multi-perspective initialization strategy, Newton interpolation inertia weight, improved followers' update model and cosine opposition-based learning (COBL) are proposed. In the majority of the SI algorithm-based FS method, the initial search agents are randomly generated or using a single filter method. However, a single filter method has different performance on various datasets. Therefore, a multi-perspective initialization strategy based on minimal redundancy maximal relevance (MRMR) and ReliefF is proposed, which can select the optimal subsets from different perspectives. Furthermore, Newton interpolation inertia weight is presented to balance the algorithm's exploration and exploitation. Compare with the existing inertia weights, the adjustment flexibility of the proposed inertia weight is enhanced. Additionally, the followers update their positions according to the values of ReliefF and MRMR, which can make full use of the relationship between data and labels. Finally, the COBL is introduced to accelerate the convergence rate and helps the algorithm jump out of the local best solutions. The COBL is better than opposition-based learning (OBL) in terms of randomness, and considers the characteristics of the FS problem. The proposed NCSSA is compared to a series of non-SI-based methods and SI-based methods employing the standard dat

关键词： salp swarm algorithm MRMR ReliefF Newton interpolation Cosine opposition-based learning

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Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2020年第1期83卷 38-53页

作者： Yildiz, Betul Sultan Bursa Uludag Univ Dept Elect & Energy TR-16059 Bursa Turkey

Considering the light-weight design expectations and government requirements in the automotive industry, both structural optimisation approaches and swarm intelligence methods have been receiving gigantic attention for their high accuracy and robustness. In this research, a new hybrid salp swarm-Nelder-Mead (HSSA-NM) algorithm is developed to optimise electric vehicle components. Both Latin hypercube sampling methodology and radial basis function surrogate modelling approach are used for obtaining equations of constraints and objectives used in the shape optimisation. Initially, the performance of the HSSA-NM is tested using a coil spring problem. Finally, the HSSA-NM is used for the optimum design of a vehicle control arm. As a result, a design problem is solved using the HSSA-NM. The optimal design meets all of the problem constraints and reduces the weight by about 2056 grams compared with that of the initial model. Thus, the proposed design method is an efficient method for shape optimisation design.

关键词： electric vehicles hybrid algorithm salp swarm algorithm radial basis function vehicle design control arm shape optimisation topology optimisation Nelder-Mead optimum design

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Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第8期19卷 7756-7804页

作者： Ding, Hongwei Cao, Xingguo Wang, Zongshan Dhiman, Gaurav Hou, Peng Wang, Jie Li, Aishan Hu, Xiang Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China Univ Key Lab Internet Things Technol & Applicat Kunming 650500 Yunnan Peoples R China Govt Bikram Coll Commerce Dept Comp Sci Patiala Punjab India Chandigarh Univ Univ Ctr Res & Dev Dept Comp Sci & Engn Mohali India Graph Era Deemed Be Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Zhengzhou Univ Sch Mech & Power Engn Zhengzhou 450000 Peoples R China Univ Michigan Rackham Grad Sch Ann Arbor MI USA Shanghai Ocean Univ Coll Informat Shanghai Peoples R China

salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.

关键词： salp swarm algorithm swarm intelligence numerical optimization engineering design optimization robot path planning

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

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APPLIED INTELLIGENCE 2022年第11期52卷 12630-12667页

作者： Kundu, Tanmay Deepmala Jain, Pramod K. PDPM IIITDM PDPM Indian Inst Informat Technol Des Jabalpur MP India Indian Inst Technol Roorkee Dept Mech & Ind Engn Roorkee 247667 Uttarakhand India

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.

关键词： salp swarm algorithm TLBO Reliability redundancy allocation problem Constrained optimization

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Improved salp swarm algorithm with mutation schemes for solving global optimization and engineering problems

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 5期38卷 3927-3949页

作者： Nautiyal, Bhaskar Prakash, Rishi Vimal, Vrince Liang, Guoxi Chen, Huiling Graph Era Univ Elect & Commun Engn Dehra Dun 248002 Uttarakhand India Graph Era Hill Univ Comp Sci & Engn Dehra Dun 248002 Uttarakhand India Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Wenzhou Univ Dept Comp Sci Wenzhou 325035 Peoples R China

salp swarm algorithm (SSA) is a recent metaheuristic algorithm developed from the inspiration of salps' swarming behavior and characterized by a simple search mechanism with few handling parameters. However, in solving complex optimization problems, the SSA may suffer from the slow convergence rate and a trend of falling into sub-optimal solutions. To overcome these shortcomings, in this study, versions of the SSA by employing Gaussian, Cauchy, and levy-flight mutation schemes are proposed. The Gaussian mutation is used to enhance neighborhood-informed ability. The Cauchy mutation is used to generate large steps of mutation to increase the global search ability. The levy-flight mutation is used to increase the randomness of salps during the search. These versions are tested on 23 standard benchmark problems using statistical and convergence curves investigations, and the best-performed optimizer is compared with some other state-of-the-art algorithms. The experiments demonstrate the impact of mutation schemes, especially Gaussian mutation, in boosting the exploitation and exploration abilities.

关键词： salp swarm algorithm Gaussian mutation Levy-flight mutation Cauchy mutation

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Class imbalanced data handling with cyberattack classification using Hybrid salp swarm algorithm with deep learning approach

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ALEXANDRIA ENGINEERING JOURNAL 2024年 106卷 654-663页

作者： Alabduallah, Bayan Maray, Mohammed Alruwais, Nuha Alabdan, Rana Darem, Abdulbasit A. Alallah, Fouad Shoie Alsini, Raed Yafoz, Ayman Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Abha Saudi Arabia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn POB 22459 Riyadh 11495 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Al Majmaah 11952 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia

Cyberattack classification involves applying deep learning (DL) and machine learning (ML) models to categorize digital threats based on their features and behaviors. These models examine system logs, network traffic, or other associated data patterns to discriminate between standard activities and malicious actions. Efficient cyberattack classification is vital for on-time threat detection and response, permitting cybersecurity specialists to categorize and reduce potential risks to a system. Handling class-imbalanced data in cyberattack classification using DL is critical for achieving exact and robust models. In cybersecurity databases, instances of normal behavior frequently significantly outnumber instances of cyberattacks, foremost due to biased methods that may complete poorly on minority classes. To address this issue approaches such as oversampling the lesser class, undersampling the popular class, or using more advanced systems can be used. These plans defend that the DL technique is more complex when determining cyberattacks, so it increases complete performance and adapts the effect of the imbalance class on the classification results. This study presents a novel Hybrid salp swarm algorithm with a DL Approach for Cyberattack Classification (HSSADL-CAC) technique. The HSSADL-CAC method intends to resolve class imbalance data handling with an optimum DL model for the recognition of cyberattacks. At first, the HSSADL-CAC method experiences data normalization as a pre-processing stage. The HSSADL-CAC technique uses the ADASYN approach to handle class imbalance problems. In addition, the HSSADL-CAC technique applies an HSSA-based feature selection approach. The HSSADL-CAC technique detects cyberattacks using a deep extreme learning machine (DELM) model. Finally, the hyperparameter tuning of the ELM model takes place by utilizing the beluga whale optimization (BWO) model. The performance analysis of the HSSADL-CAC technique employs a benchmark database. The com

关键词： Cyberattack IoT Deep learning salp swarm algorithm Class imbalance Data normalization

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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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A salp swarm algorithm based on Harris Eagle foraging strategy

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 203卷 858-877页

作者： Zhang, Xuncai Wang, Shida Zhao, Kai Wang, Yanfeng Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China

For the operation of the salp swarm algorithm(SSA), the leaders fall into local optimum, which leads to the population falling into local optimum problem. In this paper, a salp swarm algorithm based on Harris Eagle foraging strategy is proposed(ISSAHF). Harris hawk optimization algorithms of four types of feeding mechanism are integrated into salp location update process optimization algorithm follower. To strengthen the exploitation of potential areas, complemented by a multi-point leadership crossover strategy to maintain a balance between exploitation and exploration. The performance of the proposed ISSAHF was compared, and Wilcoxon's statistical analysis was performed on 20 benchmark functions including unimodal, multimodal, and partial CEC2014. Finally, ISSAHF is applied to three practical engineering optimization problems to evaluate its optimization performance further.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： salp swarm algorithm Harris Eagle optimization algorithm Multi -leader strategy swarm intelligence algorithm

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Improved salp swarm algorithm combined with chaos

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年 202卷 113-148页

作者： Tawhid, Mohamed A. Ibrahim, Abdelmonem M. Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut Egypt

A recently developed metaheuristic optimization algorithm, salp swarm algorithm (SSA), has manifested its capability in solving various optimization problems and many real-life applications. SSA is based on salps' swarming behaviour when finding their way and searching for food in the oceans. Nonetheless, like most metaheuristic algorithms, SSA experiences low convergence and stagnation in local optima and rate. There is a need to enhance SSA to speed its convergence and effectiveness to solve complex problems. In the present study, we will introduce chaos into SSA (CSSA) to increase its global search mobility for robust global optimization. Detailed studies are carried out on real-world nonlinear benchmark systems and CEC 2013 benchmark functions with chaotic map (Tent). Here, the algorithm utilizes a Tent map to tune the salp leaders' attractive movement around food sources. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of CSSA for 12 nonlinear systems and 28 unconstrained optimization problems CEC 2013. Two nonparametric statistical tests, the Friedman test and Wilcoxon Signed-Rank Test, are conducted to show the superiority of CSCA over other states of the art algorithms and our results' significance. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Bio-inspired algorithm salp swarm algorithm Metaheuristics Chaos theory Nonlinear system of equations

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