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An accelerated PSO for query expansion in web information retrieval: application to medical dataset

APPLIED intelligence 2017年第3期47卷 793-808页

作者： Khennak, Ilyes Drias, Habiba USTHB Lab Res Artificial Intelligence Comp Sci Dept BP 32 El Alia 16111 Algiers Algeria

swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

关键词： Information retrieval Web intelligence Query expansion swarm intelligence algorithms Accelerated particle swarm optimization MEDLINE

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An Improved Multi-Objective Particle swarm Optimization With TOPSIS and Fuzzy Logic for Optimizing Trapezoidal Labyrinth Weir

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IEEE ACCESS 2021年 9卷 25458-25472页

作者： Mahmoud, Ali Yuan, Xiaohui Kheimi, Marwan Almadani, Mohammad A. Hajilounezhad, Taher Yuan, Yanbin Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China China Three Gorges Univ Hubei Prov Key Lab Operat & Control Cascaded Hydr Yichang 443002 Peoples R China King Abdulaziz Univ Fac Engn Dept Civil & Environm Engn Rabigh Branch Jeddah 21589 Saudi Arabia Univ Missouri Dept Mech & Aerosp Engn Columbia MO 65211 USA Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China

Labyrinth Weir (LW) is a popular control structure that passes a significantly higher flow rate compared to the linear weirs. In order to approach the optimal design of a trapezoidal LW, a multi-objective problem is defined to concurrently minimize the LW consumed concrete volume and maximize its discharge capacity. Simultaneously, a Radial Basis function Neural Networks (RBFNN) is designed and used for estimating LW discharge coefficient (C-d) according to the existing experimental results. An improved multi-objective particle swarm optimization (MOPSO) algorithm named TOPSIS Fuzzy MOPSO (TFMOPSO) is proposed to solve the LW optimization problem. This algorithm utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the solutions, while a fuzzy inference system is developed to select the algorithm strategy for finding two leaders among the non-dominated solutions. The performance of the proposed TFMOPSO has been tested on the optimization problem of the LW of the Ute dam. The results of TFMOPSO, along with three other state-of-the-art multi-objective algorithms, are explored in terms of hypervolume, coverage, and spacing metrics. It is demonstrated that the TFMOPSO outperforms other algorithms and studies for solving the LW multi-objective optimization problem for the case of Ute dam. Also, RBFNN is found to be one of the most appropriate approaches among studied algorithms in estimating the discharge coefficient of LW, while Pareto optimal solutions from TFMOPSO exhibit a significant improvement compared to the original design of Ute dam LW.

关键词： Optimization Discharges (electric) Dams Shape Particle swarm optimization Mathematical model Genetic algorithms Labyrinth Weir swarm intelligence algorithms cost reduction shape optimization soft computing techniques

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A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets

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APPLIED SOFT COMPUTING 2018年 69卷 784-805页

作者： Li, Jinyan Fong, Simon Wong, Raymond K. Mohammed, Sabah Fiaidhi, Jinan Sung, Yunsick Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China Huawei Software Theol CO LTD Big Data PDU Nanjing Jiangsu Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2000 Australia Lakehead Univ Dept Comp Sci Thunder Bay ON Canada Dongguk Seoul Dept Multimedia Engn 30 Pildong Ro 1gil Seoul 04602 South Korea

Imbalanced datasets can be found in a number of fields;they are commonly regarded as big data because of their sheer volume and high attribute dimensions. As the name suggests, imbalanced big datasets come with an extremely imbalanced ratio between the amount of major class and minority class samples. Traditional methods: have been attempted but still cannot fully, effectively, and reliably solve the imbalanced class classification problem, especially when the distribution of the classes is exceedingly imbalanced. In this paper, we propose a collection of algorithms to solve the problem of imbalanced datasets in binary data classification. Most traditional methods: rebalance the imbalanced dataset merely by matching the data quantities of the two classes. Our proposed algorithms, which take the form of a suite of variants, focus on guaranteeing the credibility of the classification model and reaching the greatest possible accuracy by dynamically rebalancing the training dataset with multi-objective swarm intelligence optimisation. The new algorithms are extended from those we proposed earlier, which had a single objective - first find a set of solutions that satisfy the Kappa criterion, then search for the solution in the set that offers the highest accuracy. Two mam modifications are made in the new algorithms. Multi-objective optimisation is aimed at finding a solution that satisfies several criteria at the same time, such as accuracy and identifying a list of credibility indicators. The other enhancement is the incremental operation of the multi-objective optimisation. Incremental optimisation is imperative for processing data feeds that may arrive in a streaming manner. Instead of waiting for the full data archive to be available before optimisation, incremental optimisation rebalances the data feed segment by segment on the fly. The experimental results from the suite of proposed algorithms show that they can effectively attain better and more stable performanc

关键词： swarm intelligence algorithms Dynamic multi-objective Big highly imbalanced dataset Binary classification Rebalancing algorithm

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A comprehensive review: Krill Herd algorithm (KH) and its applications

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APPLIED SOFT COMPUTING 2016年 49卷 437-446页

作者： Bolaji, Asaju La'aro Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Khader, Ahamad Tajudin Abualigah, Laith Mohammad Fed Univ Wukari Dept Comp Sci Fac Pure & Appl Sci PMB 1020 Wukari Taraba State Nigeria Al Balqa Appl Univ Dept Informat Technol Al Huson Univ Coll POB 50 Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Israel Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Krill Herd algorithm swarm intelligence algorithms Nature-inspired algorithms Metaheuristics

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A Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm for Solving Complex Optimization Problems

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IEEE ACCESS 2023年 11卷 100919-100947页

作者： Zhan, Bihao Gu, Wei Hubei Univ Technol Sch Comp Sci Wuhan 430074 Peoples R China

Hunger Games Search (HGS) is a newly developed metaheuristic algorithm that models the hunger-driven activities and behaviors of animals. It incorporates the concept of hunger to devise an adaptive weight that mimics the impact of hunger on each search step. In this paper, a Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm (MASPE-HGSA) is proposed to alleviate the shortcomings of the original HGS in terms of insufficient optimization and convergence accuracy. In MASPE-HGSA, a Multi-Stage adaptive sequential parameter exploration is proposed to improve the search performance of the algorithm as well as to increase the search accuracy and global search capability, which can commendably achieve the balance of exploration and exploitation. The effectiveness of MASPE-HGSA is verified by comparing with original HGS algorithm and several classical algorithms using 23 benchmark functions, CEC2014 test set and three advanced algorithmic problems from classical engineering. Experimental results show that the performance of MASPE-HGSA is significantly better than other similar algorithms. The proposed algorithm can effectively search for high-quality solutions and prevent premature convergence, with better convergence robustness than the original HGS algorithm. In addition, an analysis of probability-based algorithm mechanisms approaching zero is presented in this paper from a theoretical perspective, while providing a reference and verification method for the design of algorithm mechanisms.

关键词： swarm intelligence algorithms hunger games search multi-stage adaptive sequential parameter exploration mechanism engineering design problem

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Review of the grey wolf optimization algorithm: variants and applications

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NEURAL COMPUTING & APPLICATIONS 2024年第6期36卷 2713-2735页

作者： Liu, Yunyun As'arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz Mohamad, Hesham Chongqing Coll Elect Engn Sch Intelligent Mfg & Automobile Chongqing Peoples R China Univ Putra Malaysia Fac Engn Seri Kembangan Malaysia

One of the most widely referenced swarm intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research.

关键词： GWO swarm intelligence algorithms Variants Applications

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Optimized tuning of power oscillation damping controllers using probabilistic approach to enhance small-signal stability considering stochastic time delay

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ELECTRICAL ENGINEERING 2019年第3期101卷 969-982页

作者： Gurung, Samundra Jurado, Francisco Naetiladdanon, Sumate Sangswang, Anawach King Mongkuts Univ Technol Thonburi Dept Elect Engn Bangkok 10140 Thailand Univ Jaen Dept Elect Engn EPS Linares Jaen 23700 Spain

Communication latency which inherently occurs in wide area measurement system greatly degrades small-signal stability (SSS) and is stochastic in nature, and thus, the current power oscillation damping controllers (PODCs) designed to improve SSS must consider this crucial factor. This paper proposes a probabilistic method to tune the parameters of PODCs incorporated in renewable farms to improve SSS under stochastic time delay and under other power system uncertainties arising due to renewable energy resources and loads. The proposed method is composed of two stages: The first stage quantifies the effect of time delay and other power system uncertainties on SSS, and the second stage uses this information to formulate an optimization problem. This optimization problem is solved with the help of four swarm intelligence-based optimization algorithms which are: bat algorithm, cuckoo search algorithm, firefly algorithm, and particle swarm optimization algorithm. The solutions from all these four optimization algorithms are compared, and the best result is used to optimize the parameters of the PODCs. All the analyses were conducted on a modified IEEE 68 bus system. The results show that the PODCs tuned using the proposed method greatly enhances the SSS margin under different scenarios and are probabilistically robust to the varying time delay and other power system uncertainties.

关键词： Cumulant Probability function swarm intelligence algorithms Wide area damping controller

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Chameleon swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

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IEEE ACCESS 2022年 10卷 74152-74163页

作者： Uluslu, Ahmet Istanbul Univ Cerrahpasa Dept Elect & Automat TR-34500 Istanbul Turkey

Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the Chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the Chameleon swarm algorithm.

关键词： Optimization Antennas Microstrip antennas Heuristic algorithms Mathematical models Substrates Patch antennas Quad-band eCSA patch antenna multidimensional optimization optimization techniques chameleon swarm algorithm evolutionary algorithms swarm intelligence algorithms

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A Review on Computational intelligence Techniques in Cloud and Edge Computing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL intelligence 2020年第6期4卷 742-763页

作者： Asim, Muhammad Wang, Yong Wang, Kezhi Huang, Pei-Qiu Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

关键词： Cloud computing (CC) edge computing (EC) computational intelligence evolutionary algorithms swarm intelligence algorithms fuzzy system learning based system

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Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013-2023

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BMC PULMONARY MEDICINE 2024年第1期24卷 536页

作者： Zhang, Yan Ma, Huan Wang, Hua Xia, Qing Wu, Shasha Meng, Jing Zhu, Panpan Guo, Zhilong Hou, Jing Anhui Chest Hosp Dept TB 3 397 Jixi Rd Hefei 230000 Peoples R China 901th Hosp Joint Logist Support Force PLA Dept Oncol Hefei 230032 Peoples R China Anhui Chest Hosp Dept TB 8 Hefei Peoples R China

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

关键词： Time-series analysis Tuberculosis Feature engineering Neural network swarm intelligence algorithms

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