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Modified salp swarm algorithm for global optimisation

NEURAL COMPUTING & APPLICATIONS 2021年第14期33卷 8709-8734页

作者： Ouaar, Fatima Boudjemaa, Redouane Univ Mohamed Kheider Biskra Fac Sci Dept Math Biskra 07000 Algeria Univ Saad Dahlab Blida Fac Sci Dept Math POB 270 Blida 09000 Algeria

salp swarm algorithm (SSA) is a newly swarm-based metaheuristic algorithm that simulate the swimming and foraging behaviour of salps in oceans so to search for global optimum solution. Similarly to other metaheuristic algorithms, SSA suffers from poor convergence rate and stagnation in local optima. In this paper, three different improvements to the original population update process are proposed in order to enhance its exploitation and exploration capabilities. The first modification (MSSA1) introduces the concept of local best information to the followers salps update process allowing a better exploration of local search neighbourhood. The second improvement (MSSA2) provide two followers update process. The first is based on a differential evolution combined with a randomly selected local best position, and the second uses a local search in the global best neighbourhood which is triggered by a non-improvement in the corresponding local best. A third modification to the SSA algorithm (MSSA3) penalises a non-improvement of the local best solution by computing a new corresponding follower's position based on a local jump in the local best neighbourhood for better exploitation. The performances of the proposed algorithms are tested on 27 CEC'15 test suite, and two real-world optimisation problems. A comparative study using nonparametric statistical tests of the obtained results is conducted against those of eight well-known metaheuristics, including the original SSA. The results indicate an overall distinctive performance of all three modification compared to the remaining algorithms, while MSSA1 scored generally better than MSSA2 and MSSA3.

关键词： Global optimisation salp swarm algorithm Nonparametric statistical tests

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Multilevel Color Image Segmentation Based on GLCM and Improved salp swarm algorithm

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IEEE ACCESS 2019年 7卷 37672-37690页

作者： Xing, Zhikai Jia, Heming Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

The grayscale co-occurrence matrix (GLCM) can be adapted to segment the image according to the pixels, but the segmentation effect becomes worse as the number of threshold increases. To solve this problem, we propose an improved salp swarm algorithm (LSSA) to optimize GLCM, with the novel diagonal class entropy (DCE) as the fitness function of the GLCM algorithm. At the same time, in order to increase the optimization ability of traditional SSA algorithm, Levy flight (LF) strategy should be improved. Through experiments on the LSSA algorithm of the color natural images, the satellite images, and the Berkeley images, the segmentation quality of the segmented images is evaluated by peak signal-to-noise ratio, feature similarity, probability rand index, variation of information, global consistency error, and boundary displacement error. The experimental results show that the segmentation ability of the GLCM-LSSA algorithm is superior to other comparison algorithms and has a good segmentation ability.

关键词： Color image segmentation GLCM salp swarm algorithm Levy flight

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Enhanced salp swarm algorithm based on random walk and its application to training feedforward neural networks

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SOFT COMPUTING 2020年第19期24卷 14791-14807页

作者： Yin, Yongqiang Tu, Qiang Chen, Xuechen Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China

salp swarm algorithm (SSA) is a new type of metaheuristic and has shown superiority over other well-known algorithms such as Particle swarm Optimization and Grey Wolf Optimizer in solving challenging optimization problems. Despite its superior performance, SSA still has problems such as insufficient convergence speed. Moreover, its local optima avoidance ability is not as good as those evolutionary algorithms using crossover operators. In this paper, we propose a modified salp swarm algorithm (m-SSA) which improves the exploitation and exploration of SSA by integrating random walk strategy and especially enhances exploration by adding a new controlling parameter. In addition, a simulated annealing-type acceptance criterion is adopted to accept the fittest follower position as the new best leader position. The performance of the proposed algorithm is benchmarked on a set of classical functions and CEC2014 test suite. The proposed algorithm (m-SSA) outperforms SSA significantly on most test functions. When compared with other state-of-the-art metaheuristics, it also presents very competitive results. Besides, we apply the proposed algorithm on training feedforward neural networks (FNNs) and the results prove the effectiveness and efficiency of m-SSA.

关键词： Metaheuristic search salp swarm algorithm Random walk Optimization techniques Simulated annealing

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On the Best Fitness Function for the WSN Lifetime Maximization: A Solution Based on a Modified salp swarm algorithm for Centralized Clustering and Routing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第4期20卷 4244-4254页

作者： Daneshvar, Seyyed Mohammad Mahdi Hosseini Mazinani, Sayyed Majid Imam Reza Int Univ Dept Elect & Comp Engn Mashhad 91735553 Iran

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs are often limited by their energy resources, making energy efficiency a critical concern in designing WSNs. Clustering and multi-hop routing are effective methods for maximizing the lifetime of WSNs. The energy efficient clustering and routing problem in WSNs can be formulated as a multi-dimensional knapsack problem (d-kp). This formulation can generalize all clustering and routing protocols in regard to energy efficiency. In this paper, we investigate the hardness of the energy-efficient clustering and routing problem in WSNs based on the d-kp formulation and introduce a modified salp swarm algorithm (SSA) as a method to solve this problem. SSA is a metaheuristic algorithm inspired by the behavior of salps in the ocean. Moreover, we prove that it is impossible to design a polynomial time fitness function to maximize a long-term metric such as FND (time of first node death), which is a commonly used metric for network lifetime. Our results have implications for the design of energy-efficient WSNs and call for the development of more efficient algorithms that can handle the complexity of this problem. furthermore, we evaluate our proposed algorithm using simulation and compare it to existing algorithms. Our results show that using SSA with a carefully designed fitness function based on the theoretical findings and a unique one-to-one routing protocol to mitigate the energy hole problem, outperforms many of the existing algorithms in terms of energy efficiency and network lifetime.

关键词： WSN clustering routing salp swarm algorithm

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Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution

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APPLIED MATHEMATICAL MODELLING 2020年 80卷 929-943页

作者： Abd Elaziz, Mohamed Li, Lin Jayasena, K. P. N. Xiong, Shengwu Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka Zagazig Univ Fac Sci Dept Math Zagazig Egypt

This paper developed a multiobjective Big Data optimization approach based on a hybrid salp swarm algorithm and the differential evolution algorithm. The role of the differential evolution algorithm is to enhance the capability of the feature exploitation of the salp swarm algorithm because the operators of the differential evolution algorithm are used as local search operators. In general, the proposed method contains three stages. In the first stage, the population is generated, and the archive is initialized. The second stage updates the solutions using the hybrid salp swarm algorithm and the differential evolution algorithm, and the final stage determines the nondominated solutions and updates the archive. To assess the performance of the proposed approach, a series of experiments were performed. A set of single-objective and multiobjective problems from the 2015 Big Data optimization competition were tested;the dataset contained data with and without noise. The results of our experiments illustrated that the proposed approach outperformed other approaches, including the baseline nondominated sorting genetic algorithm, on all test problems. Moreover, for single-objective problems, the score value of the proposed method was better than that of the traditional multiobjective salp swarm algorithm. When compared with both algorithms, that is, the adaptive DE algorithm with external archive and the hybrid multiobjective firefly algorithm, its score was the largest. In contrast, for the multiobjective functions, the scores of the proposed algorithm were higher than that of the fireworks algorithm framework. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Big Data optimization problem Multiobjective optimization problem Metaheuristic techniques salp swarm algorithm Differential evolution

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An Opposition-Based Chaotic salp swarm algorithm for Global Optimization

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IEEE ACCESS 2020年 8卷 36485-36501页

作者： Zhao, Xiaoqiang Yang, Fan Han, Yazhou Cui, Yanpeng Xian Univ Posts & Telecommun Sch Commun & Informat Xian 710121 Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Peoples R China

The salp swarm algorithm (SSA) is a bio-heuristic optimization algorithm proposed in 2017. It has been proved that SSA has competitive results compared to several other well-known meta-heuristic algorithms on various optimization problem. However, like most meta-heuristic algorithms, SSA is prone to problems such as local optimal solution and a slow convergence rate. To solve these problems, a chaotic salp swarm algorithm based on opposition-based learning (OCSSA) is proposed. The application of opposition-based learning (OBL) guarantees a better convergence speed and better develops the search space. The chaotic local search (CLS) method is also introduced, which can improve the performance of the algorithm to obtain the global optimal solution. The performance of OCSSA is compared with that of the original SSA and some other meta-heuristic algorithms on 28 benchmark functions with unimodal or multimodal characteristics. The experimental results show that the performance of OCSSA, with an appropriate chaotic map, is better than or comparable with the SSA and other meta-heuristic algorithms.

关键词： salp swarm algorithm global optimization meta-heuristic algorithms opposition-based learning chaotic local search

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Enhanced salp swarm algorithm based on opposition learning and merit function methods for optimum design of MTMD

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EARTHQUAKES AND STRUCTURES 2020年第6期18卷 719-730页

作者： Raeesi, Farzad Shirgir, Sina Azar, Bahman F. Veladi, Hedayat Ghaffarzadeh, Hosein Univ Tabriz Fac Civil Engn Tabriz Iran

Recently, population based optimization algorithms are developed to deal with a variety of optimization problems. In this paper, the salp swarm algorithm (SSA) is dramatically enhanced and a new algorithm is named Enhanced salp swarm algorithm (ESSA) which is effectively utilized in optimization problems. To generate the ESSA, an opposition-based learning and merit function methods are added to standard SSA to enhance both exploration and exploitation abilities. To have a clear judgment about the performance of the ESSA, firstly, it is employed to solve some mathematical benchmark test functions. Next, it is exploited to deal with engineering problems such as optimally designing the benchmark buildings equipped with multiple tuned mass damper (MTMD) under earthquake excitation. By comparing the obtained results with those obtained from other algorithms, it can be concluded that the proposed new ESSA algorithm not only provides very competitive results, but also it can be successfully applied to the optimal design of the MTMD.

关键词： salp swarm algorithm enhanced SSA opposition based learning merit function optimization multiple tuned mass damper

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Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 139卷 105015-105015页

作者： Zhao, Songwei Wang, Pengjun Heidari, Ali Asghar Chen, Huiling He, Wenming Xu, Suling Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Ningbo Univ Affiliated Hosp Med Sch Ningbo 315020 Peoples R China

Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the salp swarm algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://***.

关键词： Meta-heuristic algorithms salp swarm algorithm Multi-threshold image segmentation Kapur 's entropy Breast cancer Performance optimization

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Selection scheme sensitivity for a hybrid salp swarm algorithm: analysis and applications

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ENGINEERING WITH COMPUTERS 2022年第2期38卷 1149-1175页

作者： Abualigah, Laith Shehab, Mohammad Diabat, Ali Abraham, Ajith Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Aqaba Univ Technol Comp Sci Dept Aqaba 77110 Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa Machine Intelligence Res Labs MIR Labs 3rd St NWPOB 2259 Auburn WA 98071 USA

This paper proposes a hybrid version of the salp swarm algorithm (SSA) and the hill climbing (HC) technique using various selection schemes to solve engineering design problems. The proposed algorithm consists of two stages. In the first stage, the basic SSA is hybridized with HC local search to improve its exploitation capabilities;we refer to the hybridized algorithm as HSSA. In the second stage, a selection scheme is applied to enhance the exploration capabilities of the hybrid SSA. Six popular selection schemes were investigated, and the proportional selection scheme was selected as it yielded the best performance. We refer to the hybridized SSA along with the proportional selection scheme as PHSSA. To validate the performance of the proposed algorithms, a series of experiments were conducted using thirty benchmark functions and four engineering design problems. The investigations using benchmark functions revealed that HSSA overcame the weaknesses of the local search in the basic SSA. Moreover, PHSSA enhanced performance by providing an appropriate balance between exploration and exploitation as well as maintaining the diversity of the solutions and avoiding premature convergence. Finally, PHSSA produced results on engineering design problems that were at least comparable and in many cases superior to SSA and similar algorithms in the literature.

关键词： salp swarm algorithm Hill climbing Selection schemes Hybridization Meta-heuristic algorithms Optimization problems

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Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems

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ENGINEERING WITH COMPUTERS 2023年第3期39卷 1735-1769页

作者： Zhang, Hongliang Liu, Tong Ye, Xiaojia Heidari, Ali Asghar Liang, Guoxi Chen, Huiling Pan, Zhifang Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201209 Peoples R China Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.

关键词： salp swarm algorithm Chaotic initialization Global optimization Feature selection Engineering optimization problems

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