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Modified salp swarm algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

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IEEE ACCESS 2023年 11卷 25959-25967页

作者： Obayya, Marwa Al-Wesabi, Fahd N. Maashi, Mashael Mohamed, Abdullah Hamza, Manar Ahmed Drar, Suhanda Yaseen, Ishfaq Alsaid, Mohamed Ibrahim Princess Nourah bint Abdulrahman Univ Coll Engn Dept Biomed Engn Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha 62217 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11845 Egypt Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16242 Saudi Arabia

Nowadays, the analysis of gastrointestinal (GI) tract disease utilzing endoscopic image classification becomes an active research activity from the biomedical sector. The latest technology in medical imaging is Wireless Capsule Endoscopy (WCE) for diagnosing gastrointestinal diseases namely bleeding, ulcer, polyp, and so on. Manual diagnoses will be time taking and tough for the medical practitioner;thus, the authors have designed computerized approaches for classifying and detecting such diseases. Many research groups presented various machine learning (ML) and image processing methods for classifying GI tract diseases in recent times. Conventional data augmentation and image processing methods are integrated with adjusted pre-trained deep convolutional neural networks (CNNs) for classifying diseases in the GI tract from WCI images. This study presents a Modified salp swarm algorithm with Deep Learning based Gastrointestinal Tract Disease Classification (MSSADL-GITDC) on Endoscopic Images. The presented MSSADL-GITDC technique mainly focuses on the examination of WCE images for GIT classification. To accomplish this, the presented MSSADL-GITDC technique applies median filtering (MF) technique for image smoothening. The presented MSSADL-GITDC technique designs improved capsule network (CapsNet) model for feature extraction where the CapsNet model is modified by the class attention layer (CAL). Moreover, MSSA based hyperparameter tuning process is performed to improve the efficiency of the improved CapsNet model. For GIT classification, deep belief network with extreme learning machine (DBN-ELM) was used. Finally, backpropagation is applied for supervised fine tuning of the DBN-ELM model. The experimental validation of the MSSADL-GITDC technique takes place on Kvasir-V2 database reported the betterment of the MSSADL-GITDC technique on GIT classification with maximum accuracy of 98.03%.

关键词： Feature extraction Diseases Convolutional neural networks Gastrointestinal tract Tuning Deep learning Classification algorithms Biomedical imaging Particle swarm optimization Medical imaging gastrointestinal tract diseases deep learning metaheursitics fine-tuning salp swarm algorithm

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Discrete salp swarm algorithm for Euclidean Travelling Salesman Problem

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APPLIED INTELLIGENCE 2023年第10期53卷 11420-11438页

作者： Panwar, Karuna Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee Uttar Pradesh India

The salp swarm algorithm (SSA) is one of the recently proposed swarm intelligence-based algorithms, which finds its motivation in the swarming behaviour of salps when navigating and foraging in the ocean. The SSA is essentially derived to solve optimization problems that have continuous search space. SSA is a simple to implement and competitive algorithm that has been proven helpful in a variety of real-world applications. It has been explored over several optimization problems so far. In this study, an enhanced discrete version of SSA is proposed to solve the Travelling Salesman Problem (TSP). As TSP is a combinatorial optimization problem, the classical SSA is modified using swap, shift and symmetry operators for global exploration and local exploitation. Also, the 2-opt method has been incorporated with the proposed algorithm to improve the local search ability of the algorithm while addressing discrete problems. The proposed Discrete salp swarm algorithm (DSSA) has evaluated over 45 TSP instances, and the computational results showed that it is a promising algorithm. To assess its performance, the proposed algorithm's results are compared with well-known algorithms such as Genetic algorithm, Artificial Bee Colony, Spider Monkey algorithm, Jaya algorithm, Black Hole, Symbiotic Organism Search etc. The proposed algorithm significantly outperformed these algorithms for a majority of TSP instances.

关键词： salp swarm algorithm Travelling salesman problem Combinatorial optimization problem Metaheuristics 2-opt algorithm

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Parameter Identification of a Multilayer Perceptron Neural Network using an Optimized salp swarm algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 1221-1232页

作者： Al-Laham, Mohamad Abdullah, Salwani Al-Ma'aitah, Mohammad Atwah Al-Betar, Mohammed Azmi Kassaymeh, Sofian Azzazi, Ahmad Al Balqa Appl Univ Amman Univ Coll MIS Dept Amman Jordan Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol DM & Optimizat Res Grp Bangi Selangor Malaysia Ajman Univ Coll Engn & IT Artificial Intelligence Res Ctr Ajman U Arab Emirates Aqaba Univ Technol Fac IT Software Engn Dept Aqaba Jordan Appl Sci Private Univ Fac IT Comp Sci Dept Amman Jordan

Effort estimation in software development (SEE) is a crucial concern within the software engineering domain, as it directly impacts cost estimation, scheduling, staffing, planning, and resource allocation accuracy. In this scientific article, the authors aim to tackle this issue by integrating machine learning (ML) techniques with metaheuristic algorithms in order to raise prediction accuracy. For this purpose, they employ a multilayer perceptron neural network (MLP) to perform the estimation for SEE. Unfortunately, the MLP network has numerous drawbacks as well, including weight dependency, rapid convergence, and accuracy limits. To address these issues, the SSA algorithm is employed to optimize the MLP weights and biases. Simulta-neously, the SSA algorithm has shortcomings in some aspects of the search mechanisms as well, such as rapid convergence and being susceptible to the local optimal trap. As a result, the genetic algorithm (GA) is utilized to address these shortcomings through fine-tuning its parameters. The main objective is to develop a robust and reliable prediction model that can handle a wide range of SEE problems. The developed techniques are tested on twelve benchmark SEE datasets to evaluate their performance. Furthermore, a comparative analysis with state-of-the-art methods is conducted to further validate the effectiveness of the developed techniques. The findings demonstrate that the developed techniques surpass all other methods in all benchmark problems, affirming their superiority.

关键词： Software development effort estimation machine learning multilayer perceptron neural network salp swarm algorithm genetic algorithm

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Synergistic Integration of Renewable Energy and HVDC Technology for Enhanced Multi-objective Economic Emission Dispatch Using the salp swarm algorithm 5th

Synergistic Integration of Renewable Energy and HVDC Technol...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Gbadega, Peter Anuoluwapo Sun, Yanxia Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

ISBN: (纸本)9789819770038;9789819770045

This paper proposes a salp swarm algorithm (SSA), a unique optimization technique for the synergistic integration of renewable energy and High Voltage Direct Current (HVDC) technology to enhance the performance of multi-objective economic emission dispatch (MODED). The primary aim is to optimize both the economic and environmental aspects of power systems. A mathematical model for MODED based on Wind-Solar-Thermal integrated energy has been carefully constructed, considering variables like the valve point effect, equality constraints, and inequality constraints. The study determines optimal generation levels and associated costs for six thermal generating units under various power demands, exploring diverse scenarios such as Economic Dispatch for High Voltage-Alternating Current (HVAC) with Losses, Economic Dispatch for HVDC with Losses, Economic Dispatch for HVDC addressing challenges related to voltage instability, protection difficulties and losses in DC systems, Economic Dispatch HVAC & HVDC with Losses and Economic Dispatch for HVAC & HVDC with Renewable Energy (RE). To validate the model, tests have been conducted on the IEEE 30 Bus System with a substantial presence of renewable energy.

关键词： HVDC technology HVAC Renewable energy salp swarm algorithm Multi-objective dynamic economic dispatch

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Improved discrete salp swarm algorithm using exploration and exploitation techniques for feature selection in intrusion detection systems

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JOURNAL OF SUPERCOMPUTING 2023年第18期79卷 21265-21309页

作者： Barhoush, Malek Abed-alguni, Bilal H. Al-qudah, Nour Elhuda A. Yarmouk Univ Dept Comp Sci Irbid Jordan

The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization problems. However, SSA may converge to sub-optimal solutions when it is applied to discrete problems such as the feature selection (FS) problem. This paper presents the enhanced opposition-based learning salp swarm algorithm (EOSSA), which is an improved SSA algorithm for solving the FS problem in intrusion detection systems (IDS). EOSSA incorporates four improvements into the original SSA algorithm. Firstly, the opposition-based learning (OBL) method is used in the initialization step of SSA to boost its population diversity. Secondly, the Elite opposition-based learning (EOBL) is used in the improvement loop of SSA to improve its exploration ability. Third, a variable neighborhood search (VNS) method is used in the improvement loop of SSA to improve its exploration mechanism to improve the local search space. Lastly, the Sigmoid binary transform function is used to convert the continuous candidate solutions produced by SSA into discrete binary solutions. EOSSA was evaluated against eighteen popular optimization algorithms (e.g., improved salp swarm algorithm based on opposition-based learning (ISSA), SSA, particle swarm algorithm (PSO), cuckoo search (CS), bat algorithm (BA), and Harris Hawk Optimization (HHO)) using eleven popular intrusion detection datasets (CICIDS2017, CSE-CIC-IDS2018, CICDDOS2019, CIRA-CIC-DoH, Intrusion detection 2018, UNSW-NB15, NSL-KDD, Phishing Legitimate, Malmem2022, IoT, and LUFlow Network) to Detect IoT Botnet Attacks. Moreover, EOSSA was compared with four machine learning algorithms (Decision Tree (DT), logistic regression (LR), Naive Bayes (NB), and K-Nearest Neighborhood (KNN)). The overall simulation results suggested that the proposed method is superior to the other algorithms in terms of the accuracy and number of selected features. The statistical analysis of the simulation results using

关键词： Feature selection problem Variable neighborhood search salp swarm algorithm Opposition-based learning Elite opposition-based learning Intrusion detection datasets Friedman and Wilcoxon signed-rank test Sigmoid function

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An efficient image segmentation method based on expectation maximization and salp swarm algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第26期82卷 40625-40655页

作者： Ehsaeyan, Ehsan Sirjan Univ Technol Elect Engn Dept Sirjan Iran

Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and salp swarm algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.

关键词： Image segmentation Multilevel thresholding Expectation maximization salp swarm algorithm Artificial intelligence

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Three-dimensional route planning for multiple unmanned aerial vehicles using salp swarm algorithm

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2023年第7期35卷 1059-1078页

作者： Dewangan, Ram Kishan Saxena, Priyansh Thapar Inst Engn & Technol ECED Patiala Punjab India ABV Indian Inst Informat Technol & Management ABV IIITM IT Gwalior India

Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NP-hard optimisation problem. In this paper, a newly proposed salp swarm algorithm (SSA) is used, and its performance is compared with deterministic and other Nature-Inspired algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1.25% and 6.035%, respectively, when compared to recently reported data. Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even in commercial services like package delivery by drones.

关键词： Meta-heuristic Nature-Inspired algorithm Route planning salp swarm algorithm UAV Path planning

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Upgraded salp swarm algorithm for optimal design of semi-active MR dampers in buildings

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STRUCTURAL ENGINEERING AND MECHANICS 2023年第2期86卷 197-209页

作者： Raeesi, Farzad Veladi, Hedayat Azar, Bahman Farahmand Shirgir, Sina Jafarpurian, Baharak Tabriz Univ Tabriz Fac Civil Engn Tabriz Iran

In the case of designing control devices in a building, reliance on experimental formulation or engineering concepts without using optimization algorithms leads to non-optimal solutions or design parameters, which makes the use of control devices costly and unreasonable. The optimization algorithms are capable of identifying the required number of parameters for a specific design problem, however, this process is difficult and inefficient in dealing with some specific optimal design processes. This paper aims to introduce an upgraded version of the salp swarm algorithm to handle some engineering design. The performance of the new upgraded algorithm is tested using some benchmark test functions as well as a six-story benchmark building equipped with semi-active MR dampers. The simulation results show that the proposed algorithm can be successfully applied to get an optimal design of the MR dampers in the building.

关键词： MR damper optimum design salp swarm algorithm structural control upgraded salp swarm algorithm

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Sliding Wear Resistance Behaviour of Al-Zn-Mg-Cu/SiC/B4C/Porcelain Composites Using Fuzzy Model and salp swarm algorithm

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TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS 2023年第9期76卷 2493-2502页

作者： Rajak, Santosh Kumar Aherwar, Amit Choudhary, Roshani Pathak, Vimal Kumar Goyal, Ashish Saxena, Kuldeep Kr. Kuriachen, Basil Indian Inst Informat Technol Design & Mfg Dept Mech Engn Jabalpur India Madhav Inst Sci & Technol Dept Mech Engn Gwalior 474005 India Maulana Azad Natl Inst Technol Dept Comp Sci Engn Bhopal 462003 India Manipal Univ Jaipur Dept Mech Engn Jaipur 303007 Rajasthan India Lovely Profess Univ Div Res & Dev Phagwada India NIT Calicut Adv Mfg Ctr Kozhikode India

The prime motive of this experimental research work is to investigate and compare the wear behaviour of aluminium (AA7075/Al-Zn-Mg-Cu) alloy metal matrix composites enriched with three different ceramics reinforcements (SiC/7wt%, B4C/7wt%, and porcelain/7wt%) prepared via liquid metallurgy stir casting route. Sliding wear performance for all three aluminium-ceramics matrix composites was evaluated on a pin-on-disc tribometer as per Taguchi's L25 orthogonal array by varying normal loads, sliding velocities and sliding distances. The experimental results revealed that porcelain-reinforced composite (AA7075/7wt%porcelain) achieved better resistance property against sliding wear compared to AA7075/7wt%SiC and AA7075/7wt%B4C composites. Further, the fuzzy logic model was developed to evaluate wear loss for three composites with absolute accuracy. Finally, a metaheuristic salp swarm algorithm (SSA) was utilized to minimize wear loss for three composites. The wear loss value attained through SSA exhibits the lowest one in comparison with conventional Taguchi and fuzzy logic results.

关键词： AA7075 alloy SiC B4C Porcelain Stir casting Fuzzy logic salp swarm algorithm

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salp swarm algorithm with crossover scheme and Levy flight for global optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9277-9288页

作者： Jia, Heming Lang, Chunbo Sanming Univ Coll Informat Engn Sanming Fujian Peoples R China Northwestern Polytech Univ Coll Automat Xian Shaanxi Peoples R China

salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Levy flight (SSACL) is proposed. The crossover scheme and Levy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon's rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

关键词： salp swarm algorithm crossover scheme Levy flight functions optimization

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