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Stability of salp swarm algorithm with random replacement and double adaptive weighting

APPLIED MATHEMATICAL MODELLING 2021年 95卷 503-523页

作者： Ren, Hao Li, Jun Chen, Huiling Li, ChenYang Wenzhou Univ Dept Comp Sci Wenzhou 325035 Peoples R China

The salp swarm algorithm is a newly population-based search algorithm. Because the orig-inal salp swarm algorithm has low search efficiency and is easy to fall into local opti-mum, in this paper, we propose an enhanced salp swarm algorithm, which combines two strategies with the original salp swarm algorithm. One is the random replacement strategy, which can replace the current position with the optimal solution position with a certain probability of speeding up the convergence rate. The other tactic is double adaptive weight, which can expand the search scope throughout the early stages and enhance exploitation capability in the later stages. With the cooperation and guidance of the two mechanisms, the algorithm's convergence speed is accelerated, and the exploitation capacity is meritori-ously increased. The proposed method's performance is compared with three mainstream meta-heuristics and four advanced algorithms on four necessary test cases. The extensive analysis and recorded results indicate that the proposed method outperforms these algo-rithms in terms of the accuracy of the solution and convergence speed. Finally, we apply the developed method to four well-known engineering design problems (welded beam de-sign problem;cantilever beam design;I-beam design;and multiple disk clutch brake) to validate the algorithm's effectiveness for some constrained challenge. The results show that our algorithm has significant advantages in solving practical problems with constraints and unknown search spaces. (c) 2021 Elsevier Inc. All rights reserved.

关键词： salp swarm algorithm swarm intelligence Global optimization Engineering design

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A dynamic locality multi-objective salp swarm algorithm for feature selection

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 147卷 106628-106628页

作者： Aljarah, Ibrahim Habib, Maria Faris, Hossam Al-Madi, Nailah Heidari, Ali Asghar Mafarja, Majdi Abd Elaziz, Mohamed Mirjalili, Seyedali Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Birzeit Univ Dept Comp Sci Birzeit Palestine Zagazig Univ Fac Sci Dept Math Zagazig Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Princess Sumaya Univ Technol Dept Comp Sci Amman Jordan Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore

Developing intelligent analytical tools requires pre-processing data and finding relevant features that best reinforce the performance of the predictive algorithms. Feature selection plays a significant role in maximizing the accuracy of machine learning algorithms since the presence of redundant and irrelevant attributes deteriorates the performance of the learning process and increases its complexity. Feature selection is a combinatorial optimization problem that can be formulated as a multi-objective optimization problem with the purpose of maximizing the classification performance and minimizing the number of irrelevant features. It is considered an NP hard optimization problem since having a number of (n) features produces a large search space of size (2(n)) of different permutations of features. An eminent type of optimizer for tackling such an exhausting search process is evolutionary, which mimic evolutionary processes in nature to solve problems in computers. salp swarm algorithm (SSA) is a well-established metaheuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating global optima for optimization problems. The objective of this article is to promote and boost the performance of the multi-objective SSA for feature selection. Therefore, it proposes an enhanced multi-objective SSA algorithm (MODSSA-lbest) that adopts two essential components: the dynamic time-varying strategy and local fittest solutions. These components assist the SSA algorithm in balancing exploration and exploitation. Thus, it converges faster while avoiding locally optimal solutions. The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets and compared with the well-regarded Multi-Objective Evolutionary algorithms (MOEAs). The results show that the MODSSA-lbest achieves significantly promising results versus its counterpart algorithms.

关键词： Wrapper feature selection salp swarm algorithm Optimization Multi-objective Classification

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Teaching-learning guided salp swarm algorithm for global optimization tasks and feature selection

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SOFT COMPUTING 2023年第23期27卷 17887-17908页

作者： Li, Jun Ren, Hao Chen, Huiling Li, ChenYang Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China

The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching-learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in

关键词： salp swarm algorithm Teaching-learning-based optimization Engineering design Feature selection

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An improved image denoising technique using differential evolution-based salp swarm algorithm

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SOFT COMPUTING 2021年第3期25卷 1941-1961页

作者： Dhabal, Supriya Chakrabarti, Roshni Mishra, Niladri Shekhar Venkateswaran, Palaniandavar Netaji Subhash Engn Coll Dept Elect & Commun Engn Kolkata 700152 W Bengal India Techno Engn Coll Dept Elect & Commun Engn Banipur 743233 W Bengal India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 W Bengal India

This paper proposes an improved denoising method based on the cascaded arrangement of filters. The different combinations of filters are obtained optimally through the improved performance of the salp swarm algorithm and cascading four filters out of twelve different types of filters. The searching ability of standard salp swarm algorithm is enhanced following the strategies in differential evolution, and hence the algorithm is named as differential evolution-based salp swarm algorithm (DESSA). Most of the existing image denoising algorithms are suitable to remove either Gaussian, Salt & Pepper, or Speckle noise. Alternatively, due to the optimal combination of filters in the cascaded arrangement, the proposed denoising method exhibits its effectiveness in the removal of all three noises and the denoised images are better in terms of both quantitative analysis and visual quality. The denoising performance of the proposed method is also tested on the mixed noise which demonstrates the significant improvements compared to state-of-the-art algorithms. Further, the experiment on CEC 2014 benchmark functions indicates that the proposed DESSA achieves better optimal solutions than existing algorithms.

关键词： Denoising Optimization salp swarm algorithm Differential evolution Mutation Crossover Visual quality analysis

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A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

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COMPUTING 2022年第9期104卷 2031-2051页

作者： Li, Jun Ren, Hao Li, ChenYang Chen, Huiling Wenzhou Univ Dept Comp Sci Wenzhou Peoples R China Wenzhou Univ Technol Wenzhou Peoples R China

The rapid growing number of web services has posed new challenges for service composition computing. How to combine services that meet the needs of users in the least amount of time from a huge number of candidate services is a hot topic of research today. As a meta-heuristic algorithm for solving optimization problems, salp swarm algorithm (SSA) has been widely applied to case scenarios in different fields due to its simple structure and high performance. However, QoS-aware service composition is a discrete problem and existing methods are not suitable for it. Therefore, in this paper, we propose an improved SSA integrating chaotic mapping method for QoS service composition selection, named CSSA. Through the randomness and ergodicity of chaos, reducing the possibility of falling into local optimum and strengthening the exploitation capability of the algorithm. In addition, a fuzzy continuous neighborhood search method is used to enhance the local search capability of the algorithm which makes the discrete space of service composition in a way similar to continuous space. Finally, two well-known datasets are used to verify the effectiveness of CSSA compared to three advanced algorithms and original SSA. The test results demonstrate that CSSA has significant advantages and it also has satisfactory performance in large scale scenarios.

关键词： Web services composition salp swarm algorithm Quality of services (QoS) Chaotic mapping Fuzzy continuous neighborhood search

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Coke price prediction approach based on dense GRU and opposition-based learning salp swarm algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2023年第2期21卷 106-121页

作者： Zhu, Xuhui Xia, Pingfan He, Qizhi Ni, Zhiwei Ni, Liping Hefei Univ Technol Sch Management Hefei Anhui Peoples R China Zhejiang Gongshang Univ Sch Stat & Math Hangzhou Zhejiang Peoples R China

Coke price prediction is critical for smart coking plants to make sensible production plan. The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit (GRU) performs well on dealing with it. Meanwhile, densely connected GRU can improve the information flow of time-series data, but its key parameters are sensitive. Therefore, a novel coke price prediction method, named DGOLSCPP, is proposed using dense GRU (DGRU) and opposition-based learning salp swarm algorithm (OLSSA). Firstly, a model with two layers stacked DGRU is constructed for capturing deeper features. Secondly, OLSSA is proposed by introducing opposition-based learning, following and stochastic walk operation for enhancing searching ability. Finally, OLSSA is employed to adjust the key parameters of DGRU for winning the accurate predictive results. Experimental results on two real-world coke price datasets from a certain smart coking plant suggest DGOLSCPP outperforms other competitive methods.

关键词： salp swarm algorithm gated recurrent unit GRU dense network coke price prediction opposition-based learning salp swarm algorithm OLSSA

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Optimal operation of multiple integrated energy systems based on a hybrid Taguchi-compact salp swarm algorithm

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IET RENEWABLE POWER GENERATION 2023年第13期17卷 3355-3372页

作者： Fu, Yang Shan, Jie Li, Zhenkun Xie, Bolin Shanghai Univ Elect Power Shanghai Peoples R China South China Univ Technol Guangzhou Peoples R China

With the rapid development of integrated energy system (IES), it has become a trend to form multiple integrated energy systems (MIESs) in a certain region. However, there is a lack of coordination and cooperation among MIESs. This paper proposes an optimal operation model of MIESs with four cost objectives, which focuses on energy interaction among MIESs. As a non-linear and large optimization problem, it is difficult for conventional mathematical methods to solve. Hence, this paper proposes a hybrid Taguchi-compact salp swarm algorithm (TCSSA). The compact technique can save the operation memory of model. Taguchi method can improve the convergence speed and solution accuracy of model. The proposed algorithm is tested on 28 benchmark functions and applied to optimal operation of MIESs. Results demonstrate that: (1) compared with other famous algorithms, TCSSA can provide more efficient execution and better solutions. (2) The optimal operation of MIESs based on TCSSA can achieve the complementary of energy advantages, which effectively reduces the total cost of MIESs (up to 9.67%). An optimal operation model of multiple energy integrated systems with four cost objectives are proposed, which considers energy interaction. Improved and implemented two different compact salp swarm algorithm, and then propose the hybrid Taguchi-compact salp swarm ***

关键词： compact technique integrated energy system multiple systems salp swarm algorithm Taguchi method

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Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第7期46卷 8742-8759页

作者： Dagal, Idriss Akin, Burak Akboy, Erdem Yildiz Tech Univ Fac Elect Engn TR-34220 Istanbul Esenler Turkey

In this study, an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems is investigated. The effect of PV partial shading conditions, uniform and fast-tracking irradiance, duty cycle, frequency, temperature changes, and load types, and besides some comparative studies of different algorithms are adequately examined for better performance study of the proposed technique. The proposed improved salp swarm algorithm based particle swarm optimization utilizes the PV Solarex-MSX-60 photovoltaic solar panel, which considers voltage and current as inputs based on the proposed algorithm parameters selection. Besides, it uses a buck-boost converter as an interface between input and output. The particle swarm optimization monitors the PV voltage and current, and the salp swarm algorithm does for the duty cycle (particles) in various environmental conditions. The proposed algorithm performs efficiencies 99.99%, 99.63%, and 99.24% comparison with other methods, under uniform irradiance and fast-tracking irradiance respectively. Moreover, the highest power of 316.32 W reached at the duty cycle of 0.6 and 428.6 W at the frequency of 30 kHz under the same partial shading condition with optimal operating temperature values 10 degrees C,15 degrees C,20 degrees C,25 degrees C,30 degrees C,35 degrees C.

关键词： buck-boost converter particle swarm optimization photovoltaics salp swarm algorithm

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A Boosted Communicational salp swarm algorithm: Performance Optimization and Comprehensive Analysis

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Journal of Bionic Engineering 2023年第3期20卷 1296-1332页

作者： Chao Lin Pengjun Wang Ali Asghar Heidari Xuehua Zhao Huiling Chen College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035ZhejiangChina College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou325035China School of Digital Media Shenzhen Institute of Information TechnologyShenzhen518172China

The salp swarm algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

关键词： salp swarm algorithm swarm intelligence Global optimization Exploration Exploitation

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An improved variational mode decomposition method based on the optimization of salp swarm algorithm used for denoising of natural gas pipeline leakage signal

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MEASUREMENT 2021年 185卷 110107-110107页

作者： Lu, Jingyi Yue, Jikang Zhu, Lijuan Wang, Dongmei Li, Gongfa Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Heilongjiang Peoples R China Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ Coll Elect & Informat Engn Daqing Heilongjiang Peoples R China Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Wuhan Peoples R China

Natural gas pipeline leak detection is susceptible to environmental noise, which leads to low detection accuracy. Denoising of natural gas pipeline leak signals is of great significance for improving the accuracy of pipeline leak detection. Variational mode decomposition (VMD) has the function of signal denoising. However, the inaccurate setting of VMD parameters will affect the result of signal decomposition. Therefore, an improved VMD adaptive signal denoising method is proposed in this paper. First, the control parameters of VMD, i.e. decomposition layer number K and penalty factor alpha, are optimized by using the salp swarm algorithm (SSA). The ratio of the mean and variance of the permutation entropy (PE) is used as the fitness function of SSA. Then, the optimized VMD is used to decompose the pipeline leakage signal to obtain several intrinsic mode functions (IMFs). Finally, the PE is used to select IMFs containing leakage features for reconstruction so as to obtain the denoised signal. The comparative experimental analysis shows that the improved VMD method after SSA optimization has stronger denoising robustness than other denoising methods.

关键词： Variational mode decomposition salp swarm algorithm Parameter optimization Signal denoising Pipeline leak detection

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