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Chimp optimization algorithm in multilevel image thresholding and image clustering

EVOLVING SYSTEMS 2023年第4期14卷 605-648页

作者： Eisham, Zubayer Kabir Haque, Md Monzurul Rahman, Md Samiur Nishat, Mirza Muntasir Faisal, Fahim Islam, Mohammad Rakibul Islamic Univ Technol Dept EEE Gazipur Bangladesh

Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp optimization algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm optimization algorithm, Whale optimization algorithm, Salp Swarm algorithm, Harris Hawks optimization algorithm, Moth Flame optimization algorithm, Grey Wolf optimization algorithm, Archimedes optimization algorithm, African Vulture optimization algorithm using two popular thresholding techniques-Kapur's entropy method and Otsu's class variance method. The results demonstrate the effectiveness and competitive performance of Chimp optimization algorithm.

关键词： Thresholding Clustering optimization algorithm Metaheuristic ChOA

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A novel parallel hybrid intelligence optimization algorithm for a function approximation problem

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第1期63卷 325-336页

作者： Deng, Wu Chen, Rong Gao, Jian Song, Yingjie Xu, Junjie Dalian Maritime Univ Informat Sci & Technol Inst Dalian 116026 Peoples R China Dalian Jiaotong Univ Software Inst Dalian Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Coll Elect Engn Chongqing 630044 Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Zigong Peoples R China Dalian Univ Key Lab Adv Design & Intelligent Comp Dalian 116622 Peoples R China

A novel parallel hybrid intelligence optimization algorithm (PHIOA) is proposed based on combining the merits of particle swarm optimization with genetic algorithms. The PHIOA uses the ideas of selection, crossover and mutation from genetic algorithms (GAs) and the update velocity and situation of particle swarm optimization (PSO) under the independence of PSO and GAs. The proposed algorithm divides the individuals into two equation groups according to their fitness values. The subgroup of the top fitness values is evolved by GAs and the other subgroup is evolved by the PSO algorithm. The optimal number is selected as a global optimum at every circulation which shows better results than both PSO and GAs, then improves the overall performance of the algorithm. The PHIOA is used to optimize the structure and parameters of the fuzzy neural network. Finally, the experimental results have demonstrated the superiority of the proposed PHIOA to search the global optimal solution. The PHIOA can improve the error accuracy while speeding up the convergence process, and effectively avoid the premature convergence to compare with the existing methods. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： Function approximation problem Genetic algorithms Particle swarm optimization Fuzzy neural network Parallel hybrid intelligence optimization algorithm

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Combination of binary particle swarm optimization algorithm and discrete dipole approximation method to investigate the plasmonic circuit-based coherent perfect absorption filter

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OPTICAL AND QUANTUM ELECTRONICS 2017年第3期49卷 1-12页

作者： Mohamadrezaee, Mehdi Shirali, Mehdi Akhlaghi, Majid Kaboli, Milad Islamic Azad Univ Omidiyeh Branch Dept Elect & Elect Omidiyeh *** Iran

Here, we suggest the possibility of optical circuit design approach by employing the binary optimization of plasmonic nano rods. The proposed mechanism is based on combination of binary particle swarm optimization (BPSO) algorithm and discrete dipole approximation method. BPSO, a group of birds including a matrix with binary entries responsible for controlling nano rods in the array, shows the presence with symbol of ('1') and the absence with ('0'). The current research represents a nanoscale and compact four channels plasmonic Demultiplexer as optical circuit. It includes eight coherent perfect absorption (CPA)-type filters. The operation principle is based on the absorbable formation of a conductive path in the dielectric layer of a plasmonic nano-rods waveguide. Since the CPA efficiency depends strongly on the number of plasmonic nano-rods and the nano rods location, an efficient binary optimization method based the BPSO algorithm is used to design an optimized array of the plasmonic nano-rod in order to achieve the maximum absorption coefficient in the 'off' state.

关键词： Plasmonic nano particles Optical circuit optimization algorithm

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Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model

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NEURAL COMPUTING & APPLICATIONS 2021年第8期33卷 3503-3519页

作者： Fang, Qiancheng Hoang Nguyen Xuan-Nam Bui Trung Nguyen-Thoi Zhou, Jian Huanghuai Univ Inst Architecture Engn Zhumadian 463000 Henan Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res Duc Thang Ward 18 Vien St Hanoi Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Cent South Univ Sch Resources & Safety Engn Changsha 410083 Hunan Peoples R China

This paper proposes a new soft computing model (artificial intelligence model) for modeling rock fragmentation (i.e., the size distribution of rock (SDR)) with high accuracy, based on a boosted generalized additive model (BGAM) and a firefly algorithm (FFA), called FFA-BGAM. Accordingly, the FFA was used as a robust optimization algorithm/meta-heuristic algorithm to optimize the BGAM model. A split-desktop environment was used to analyze and calculate the size of rock from 136 images, which were captured from 136 blasts. To this end, blast designs were collected and extracted as the input parameters. Subsequently, the proposed FFA-BGAM model was evaluated and compared through previous well-developed soft computing models, such as FFA-ANN (artificial neural network), FFA-ANFIS (adaptive neuro-fuzzy inference system), support vector machine (SVM), Gaussian process regression (GPR), and k-nearest neighbors (KNN) based on three performance indicators (MAE, RMSE, andR(2)). The results indicated that the new intelligent technique (i.e., FFA-BGAM) provided the highest accuracy in predicting the SDR with an MAE of 0.920, RMSE of 1.213, andR(2)of 0.980. In contrast, the remaining models (i.e., FFA-ANN, FFA-ANFIS, SVM, GPR, and KNN) yielded lower accuracies in predicting the SDR, i.e., MAEs of 1.248, 1.661, 1.096, 1.573, 1.237;RMSEs of 1.598, 2.068, 1.402, 2.137, 1.717;andR(2)of 0.967, 0.968, 0.972, 0.940, 0.963, respectively.

关键词： Rock size distribution Rock fragmentation FFA-BGAM Blasting optimization algorithm Hybrid technique

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A novel chaos optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第16期76卷 17405-17436页

作者： Feng, Junhong Zhang, Jie Zhu, Xiaoshu Lian, Wenwu Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Guangxi Peoples R China Yulin Normal Univ Guangxi Coll & Univ Key Lab Complex Syst Optimiza Yulin 537000 Guangxi Peoples R China

In this paper, by analyzing the best chaotic sequences generated by sixteen different chaotic maps, a novel chaos optimization algorithm is presented. It can intelligently base on different chaotic maps to select different strategies so as to map the chaotic variables into the optimization variables. For the proposed algorithm, the obtained best values, the run time, and the role of the first and the second stage search by using different chaotic maps are also analyzed and compared. The simulation results implemented on several classic test functions demonstrate that the proposed algorithm has a high performance and an outstanding efficiency.

关键词： Chaos optimization Chaotic map optimization algorithm

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Master-slave model-based parallel chaos optimization algorithm for parameter identification problems

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NONLINEAR DYNAMICS 2016年第3期83卷 1727-1741页

作者： Yuan, Xiaofang Zhang, Ting Dai, Xiangshan Wu, Lianghong Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Sci & Technol Coll Informat & Elect Engn Xiangtan 411201 Peoples R China

The parameter identification problem can be formalized as a multi-dimensional optimization problem, where an objective function is established minimizing the error between the estimated and measured data. In this article, a master-slave model (MSM)-based parallel chaos optimization algorithm (PCOA) (denoted as MSM-PCOA) is proposed for parameter identification problems. The MSM-PCOA is a novel global optimization algorithm, where twice carrier wave chaos search is employed as the master model, while the migration and crossover operation are used as the slave model. The MSM-PCOA is applied to the parameter identification of two different complex systems: bidirectional inductive power transfer system and chaotic systems. Simulation results, compared with other optimization algorithms, show that MSM-PCOA has better parameter identification performance.

关键词： Parameter identification optimization algorithm Chaos Parallel chaos optimization algorithm (PCOA) Master-slave model

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A new optimization algorithm for the design of aperiodic multilayer mirror for 17.1 nm and 30.4 nm

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OPTOELECTRONICS AND ADVANCED MATERIALS-RAPID COMMUNICATIONS 2013年第3-4期7卷 167-172页

作者： Zhu, Huaxin Gao, Jinsong Wang, Tongtong Zhao, Jingli Liu, Guilin Li, Shuai Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Opt Syst Adv Mfg Technol Changchun 130033 Peoples R China

A new algorithm, Genetic/Tabu hybrid algorithm (GTHA) to optimize the aperiodic multilayer mirrors which combines the advantages of genetic algorithm (GAs) with tabu search (TS) algorithm is proposed in the extreme ultraviolet (EUV) range. Aperiodic multilayers are designed using GTHA for the selection of Fe-IX and He-II emission lines contrasting to the traditional periodic multilayers for a single wavelength. Materials of Mo and Si are selected for their high stability and fairly high reflectivity. High reflectance of 48.62% for the Fe-IX line (lambda=17.1nm) and reflectance of 20.57% for the He-II line (lambda=30.4nm) are reached by the new algorithm. Comparisons between aperiodic multilayers found by GTHA and the ones optimized using GAs indicate the effectiveness and reliability of the new hybrid algorithm. The aperiodic designs are compared with the periodic ones as well. And the aperiodic multilayers found by GTHA also have the better performance than periodic ones. The practicability of the aperiodic design optimized by GHTA is verified by the sensitivity analyses to thicknesses errors, which indicated it more feasible to fabricate the aperiodic multilayers in practice.

关键词： Optical thin Elm optimization algorithm Aperiodic multilayers

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Dove Swarm optimization algorithm

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IEEE ACCESS 2022年 10卷 46690-46696页

作者： Su, Mu-Chun Chen, Jieh-Haur Utami, Andina Mugi Lin, Shih-Chieh Wei, Hsi-Hsien Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Natl Cent Univ Dept Civil Engn Taoyuan Taiwan Natl Cent Univ Res Ctr Smart Construct Taoyuan 320317 Taiwan Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China

Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.

关键词： optimization Particle swarm optimization Genetic algorithms Birds Neurons Statistics Sociology Swarm intelligence optimization algorithm computational intelligence

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An improved grasshopper optimization algorithm based on dynamic dual elite learning and sinusoidal mutation

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COMPUTING 2022年第5期104卷 981-1015页

作者： Chen, Lei Tian, Yu Ma, Yunpeng Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Sci Tianjin Peoples R China

Grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms.

关键词： Grasshopper optimization algorithm Dynamic elite learning Sinusoidal mutation optimization algorithm Meta-heuristic algorithm

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A New Class Topper optimization algorithm with an Application to Data Clustering

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2020年第4期8卷 948-959页

作者： Das, Pranesh Das, Dushmanta Kumar Dey, Shouvik Natl Inst Technol Nagaland Comp Sci & Engn Dimapur 797103 India Natl Inst Technol Nagaland Dimapur 797103 India Jadavpur Univ Comp Sci Kolkata 700103 India

In this paper, a new Class Topper optimization (CTO) algorithm is proposed. The optimization algorithm is inspired from the learning intelligence of students in a class. The algorithm is population based search algorithm. In this approach, solution is converging towards the best solution. This may lead to a global best solution. To verify the performance of the algorithm, a clustering problem is considered. Five standard data sets are considered for real time validation. The analysis shows that the proposed algorithm performs very well compared to various well known existing heuristic or meta-heuristic optimization algorithms.

关键词： optimization Clustering algorithms Heuristic algorithms Data analysis Whales Dolphins Sociology Data clustering optimization algorithm learning intelligence data analysis and nature inspired optimization

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