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Stochastic biogeography-based learning improved RIME algorithm: application to image segmentation of lupus nephritis

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 14185-14229页

作者： Zheng, Boli Chen, Yi Wang, Chaofan Heidari, Ali Asghar Liu, Lei Chen, Huiling Chen, Xiaowei Chen, Peirong Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Rheumatol & Immunol Wenzhou 325000 Peoples R China

Lupus nephritis (LN) is the most common symptom of systemic lupus erythematosus, emphasizing its importance in the field of medicine. The growing frequency of LN has increased the need for effective image segmentation algorithms. With the increasing prevalence of LN, the demand for efficient image segmentation techniques has grown. To enhance the efficiency of image segmentation of LN, many researchers employ a methodology that integrates multi-threshold image segmentation (MTIS) with metaheuristic algorithms (MAs). However, conventional MAs-based MTIS methods tend to converge towards local optima and have slow convergence rates, resulting in poor segmentation results within a limited iteration number. To address these challenges, this study proposes an advanced optimization algorithm termed Biogeography-based Learning Rime Optimization algorithm (BLRIME) and integrates it with the MTIS approach for LN image segmentation. MTIS employs a non-local means 2D histogram to gather image information and uses 2D Renyi's entropy as the fitness function. BLRIME builds upon the foundation of the RIME algorithm, incorporating two significant strategies. Firstly, the introduction of piecewise chaotic mapping (PCM) ameliorates the quality of the initial solution provided by the algorithm. Secondly, a stochastic biogeography-based learning strategy (SBLS) prevents the RIME algorithm from falling into the local optimum early. SBLS is proposed by this study based on the biogeography-based learning strategy. In order to assess the efficacy of the BLRIME, this paper devises a series of experiments to compare it with similar algorithms presented at IEEE CEC 2017. Experimental studies have been conducted to provide empirical evidence demonstrating the superior rates of convergence and precision achieved by BLRIME. Subsequently, the BLRIME-based MTIS algorithm is employed to segment the LN images compared to other peer algorithms. Furthermore, the peak signal-to-noise ratio, feature simi

关键词： swarm intelligence algorithm Chaotic mapping Rime optimization algorithm Renal pathology Biogeography-based strategy

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An efficient hybrid approach based on PSO, ABC and k-means for cluster analysis

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 19321-19339页

作者： Pu, Qiumei Gan, Jingkai Qiu, Lirong Duan, Jiaxin Wang, Hui Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

Particle swarm Optimization (PSO) algorithm is one of the typical example of swarm intelligence (SI) algorithm. This article addresses such problems of PSO algorithm as random initial position of each particle, unsmooth speed weight change, and poor search ability, and proposes an optimization algorithm-hybrid PSO (HPSO) algorithm to solve these problems. This algorithm makes comprehensive improvements to the PSO clustering algorithm by using the K-means clustering algorithm to generate initial clustering centers, adopting a negative exponential function model to update the weight of velocity when constructing the "position-velocity" model, and introducing the "search restriction" mechanism, and the "fly-back" mechanism and auxiliary search methods such as the single point crossover operator in the Artificial Bee Colony (ABC) algorithm. Furthermore, experimental results were analyzed and verified. The experiment compares HPSO algorithm with K-Means algorithm, PSO algorithm, and other two typical improved algorithms from the literature on six of the UCI standard clustering test data sets. The results indicate that HPSO algorithm has good performance in stability, clustering effectiveness, robustness and global search ability.

关键词： swarm intelligence algorithm Particle swarm optimization algorithm HPSO algorithm K-means algorithm Single point crossover operator

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Multi-strategy chimp optimization algorithm for global optimization and minimum spanning tree

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SOFT COMPUTING 2024年第3期28卷 2055-2082页

作者： Du, Nating Zhou, Yongquan Luo, Qifang Jiang, Ming Deng, Wu Guangxi Univ Nationalities Coll Artificial Intelligenc Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Guangxi Inst Digital Technol Nanning 530000 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity;Sine Cosine algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness.

关键词： Chimp optimization algorithm Opposition-based learning strategy Sine cosine algorithm Minimum spanning tree swarm intelligence algorithm

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An efficient krill herd algorithm for color image multilevel thresholding segmentation problem

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APPLIED SOFT COMPUTING 2020年第0期89卷 106063-000页

作者： He, Lifang Huang, Songwei Kunming Univ Sci & Technol Dept Elect & Commun Engn Kunming 650093 Yunnan Peoples R China Kunming Univ Sci & Technol Dept Mineral Proc Kunming 650093 Yunnan Peoples R China

The conventional thresholding methods are very efficient for bi-level thresholding, but the computational complexity may be excessively high for color image multilevel thresholding. Color image multilevel thresholding segmentation can be considered as a constrained optimization problem, therefore swarm intelligence algorithms are widely used to reduce the complexity. In this paper, an efficient krill herd (EKH) algorithm is proposed to search optimal thresholding values at different level for color images and the Otsu's method, Kapur's entropy and Tsallis entropy are employed as objective functions. Seven different algorithms, KH without any genetic operators (KH I), KH with crossover operator (KH II), KH with crossover and mutation operators (KH IV), modified firefly algorithm (MFA), modified grasshopper optimization algorithm (MGOA), bat algorithm (BA) and water cycle algorithm (WCA), are compared with the EKH algorithm. Experiments are performed on ten color benchmark images in terms of optimal threshold values, objective values, PSNR, SSIM and standard deviation of the objective values at different levels. The experimental results show that the presented EKH algorithm is superior to the other algorithms for color image multilevel thresholding segmentation. On the other hand, Kapur's entropy is found to be more accurate and robust for color image multilevel thresholding segmentation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Image segmentation Color image multilevel thresholding segmentation swarm intelligence algorithm Krill herd algorithm

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An enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based learning

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第4期20卷 6422-6467页

作者： Wang, Yufei Zhang, Yujun Yan, Yuxin Zhao, Juan Gao, Zhengming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Acad Arts Jingmen 448000 Peoples R China Jingchu Univ Technol Inst Intelligent Comp Technol Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Hubei Engn Res Ctr Specialty Flowers Biol Breeding Jingmen 448000 Peoples R China

The aquila optimization algorithm (AO) is an efficient swarm intelligence algorithm proposed recently. However, considering that AO has better performance and slower late convergence speed in the optimization process. For solving this effect of AO and improving its performance, this paper proposes an enhanced aquila optimization algorithm with a velocity-aided global search mechanism and adaptive opposition-based learning (VAIAO) which is based on AO and simplified Aquila optimization algorithm (IAO). In VAIAO, the velocity and acceleration terms are set and included in the update formula. Furthermore, an adaptive opposition-based learning strategy is introduced to improve local optima. To verify the performance of the proposed VAIAO, 27 classical benchmark functions, the Wilcoxon statistical sign-rank experiment, the Friedman test and five engineering optimization problems are tested. The results of the experiment show that the proposed VAIAO has better performance than AO, IAO and other comparison algorithms. This also means the introduction of these two strategies enhances the global exploration ability and convergence speed of the algorithm.

关键词： aquila optimizer simplified aquila optimization algorithm swarm intelligence algorithm velocity-aided global search adaptive opposition-based learning engineering problem

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Improved Initialization Method for Metaheuristic algorithms: A Novel Search Space View

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IEEE ACCESS 2021年 9卷 121366-121384页

作者： Li, Qian Bai, Yiguang Gao, Weifeng Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

As an essential step of metaheuristic optimizers, initialization seriously affects the convergence speed and solution accuracy. The main motivation of the state-of-the-art initialization method is to generate a small initial population to cover the search space as much as possible uniformly. However, these approaches have suffered from the curse of dimensionality, high computational cost, and sensitivity to parameters, which ultimately reduce the algorithm's convergence speed. In this paper, a new initialization technique named diagonal linear uniform initialization (DLU) is proposed, which follows a novel search view, i.e., adopting the diagonal subspace sampling instead of the whole space. By considering the algorithm's update mechanism, the improved sampling method dramatically improves the convergence speed and solution accuracy of metaheuristic algorithms. Compared with the other eight widely used initialization strategies, the differential evolution (DE) algorithm with DLU obtains the best performance in search accuracy and convergence speed. In the extension experiments, results show that the DLU is still effective for three swarm-based algorithms: particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Especially for the multi-objective problem, the DLU still demonstrates its powerful performance compared with other strategies.

关键词： Statistics Sociology Optimization Convergence Hypercubes Search problems Licenses Optimization metaheuristic algorithm initialization evolutionary algorithm swarm intelligence algorithm multi-objective optimization

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Locality sensitive hashing-aware fruit fly optimization algorithm and its application in edge server placement

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-15页

作者： Cao, Qian Liu, Bo Jin, Ying Chaohu Univ Coll Informat Engn Hefei Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Hefei Univ Sch Artif Intelligence & Big Data Hefei Peoples R China

As is well known that the global optimization ability of the Fruit fly Optimization algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a Fruit Fly Optimization algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect.

关键词： Fruit fly optimization algorithm Locality sensitive hashing swarm intelligence algorithm Global optimization capacity Edge server placement problem Edge computing

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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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An evacuation simulation method based on an improved artificial bee colony algorithm and a social force model

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APPLIED intelligence 2021年第1期51卷 100-123页

作者： Zhao, Yuan Liu, Hong Gao, Kaizhou Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan 250014 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China

Simulation modeling is an important tool for simulating crowd behavior and studying the law of crowd evacuation. It is of great significance for exploring evacuation management methods in emergency situations. The real-time change of evacuation is the main challenge of simulation modeling. In the evacuation simulation, it is difficult for people to choose a suitable route according to the change of evacuation dynamics. This paper proposes a new evacuation simulation method which combines an improved artificial bee colony algorithm for dynamic path planning and SFM (Social Force Model) for simulating the movement of pedestrians, to providing pedestrians with timely route selection. In the path planning layer, we developed a MABCM (Multiple-subpopulations Artificial Bee Colony with Memory) algorithm and proposed a new exit evaluation strategy. These methods can plan a route with the shortest evacuation time for pedestrians according to the dynamic changes of evacuation and improve evacuation efficiency. In the simulated motion layer, we use the SFM to avoid collisions and achieve the reproduction of the evacuation scene. We verified the performance of the proposed MABCM on the CEC 2014 benchmark suite, and the results show that it is superior to the four existing artificial bee colony algorithms in most cases. The proposed crowd evacuation method is verified on an existing SFM platform. The experimental results indicate that the proposed method can efficiently evacuate a dense crowd in multiple scenes and can effectively shorten evacuation time.

关键词： Artificial bee colony algorithm Crowd evacuation Computer simulation swarm intelligence algorithm

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FMFO: Floating flame moth-flame optimization algorithm for training multi-layer perceptron classifier

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APPLIED intelligence 2023年第1期53卷 251-271页

作者： Yang, Zhenlun Guangzhou Panyu Polytech Guangzhou Peoples R China

As one of the most popular artificial neural networks, multi-layer perceptron (MLP) has been employed to solve classification problems in many applications. The main challenge in MLP application is finding the ideal set of network connection weights and biases in the training process, which minimizes the error of MLP in processing datasets. To efficiently address this challenge, numerous swarm intelligence (SI) algorithms with powerful search capabilities have been adopted for training MLP classifiers. However, these existing algorithms often suffer from problems of local optima stagnation, premature convergence, and inefficient search. In this study, a novel floating flame moth-flame optimization (FMFO) algorithm with remarkable exploitation and exploration search capabilities is proposed, offering an advantageous option for training MLP classifiers. To verify the performance of the proposed FMFO in training MLP classifiers, the FMFO-based MLP training approach (FMFO-MLP) is evaluated on eleven classification datasets that represent a wide range of the variable dimension scale. In addition, some recently developed well-known and state-of-the-art SI algorithms are applied to compare with the proposed FMFO. Experimental results demonstrate that the proposed FMFO outperforms the other competing algorithms in terms of approximating the optimal objective function value and achieving classification accuracy. Moreover, the proposed FMFO achieves a competitive computational efficiency in the experiment, confirming that it is an efficient optimizer for training MLP classifiers in practical applications.

关键词： Moth-flame optimization swarm intelligence algorithm Multi-layer perceptrons Neural network Floating flame

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