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Removal of hydrochlorothiazide from molecular liquids using carbon nanotubes: Radial basis function neural network modeling and culture algorithm optimization

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JOURNAL OF MOLECULAR LIQUIDS 2021年 324卷 114766-114766页

作者： Vafaei, Aazam Ghaedi, Abdol Mohammad Avazzadeh, Zeinab Kiarostami, Vahid Agarwal, Shilpi Gupta, Vinod Kumar Islamic Azad Univ Dept Chem Gachsaran Branch Gachsaran Iran Islamic Azad Univ Dept Chem North Tehran Branch Tehran *** Iran King Abdulaziz Univ Ctr Excellence Adv Mat Res CEAMR Jeddah Saudi Arabia

The removal of hydrochlorothiazide (HCT) from molecular liquids on multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs) was studied in a batch system. In this present work, multiple linear regression (MLR) and radial basis function neural network (RBFNN) were utilized to forecast the adsorption removal percentage of HCT by both adsorbents. The influences of process variables on the removal efficiency were optimized by culture algorithm (CA) optimization. The results displayed the RBFNN was better than the MLR to simulate removal of HCT by two adsorbents. The optimal RBFNN model using the test dataset predicted HCT removal (%) with the coefficient of determination (R-2) values of 0.8460 and 0.9438;mean squared error (MSE) values of 0.0117 and 0.0010, respectively for SWCNTs and MWCNTs. At the optimum value of parameters, the adsorption isotherms could be fitted well by the Langmuir model with adsorption capacity values of 66.225 mg g(-1) for MWCNTs and 45.662 mg g(-1) for SWCNTs. It was also found that the pseudo second-order and intraparticle diffusion models were more suitable for explaining the adsorption mechanism by CNTs. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Single walled carbon nanotube Multi walled carbon nanotube Hydrochlorothiazide Radial basis function culture algorithm

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CC-CSA: A culture&chaos-inspired clonal selection algorithm for abnormal detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期39卷 1289-1301页

作者： Xi, Liang Wang, Ruidong Zhang, Fengbin Sun, Yuezhongyi Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin Peoples R China

The clonal selection algorithm(CSA) is a core method in artificial immune system, which is famous for its intelligent evolution in artificial intelligence application. However, There are some shortcomings in the algorithm, such as local optima and low convergence speed, which make its practical effects not ideal. culture algorithm(CA) is driven by knowledge, which can significantly improve the evolutionary efficiency. Chaos mechanism can make the algorithm have better problem space coverage ability. Therefore, a culture&chaos-inspired CSA(CC-CSA) is proposed in this paper to deal with the problems mentioned before. CC-CSA adopts the double-layer evolutionary framework of CA to extract knowledge and guide the crossover and chaotic mutation operation to complete the evolution process. The implicit knowledge is used to adaptively control the chaotic mutation scale, guide the individuals to jump out of the local optima, and realize the accurate search in the latter evolution cycle to gradually approach the optimal solution. It can be seen from the mathematical model analysis that CC-CSA can converge to the global optimal solution. Compared with the experimental results of the original CSA and its representative, up-to-date improved methods, CC-CSA has the fastest convergence speed and the best detection performances. It is also proved that CC-CSA can solve the problems of local optima and slow convergence speed by using the knowledge guidance of CA's double-layer framework and good coverage ability of chaos mechanism to the problem space.

关键词： Artificial immune system clonal selection algorithm culture algorithm chaos mechanism abnormal detection

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culture-based Artificial Bee Colony with heritage mechanism for optimization of Wireless Sensors Network

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APPLIED SOFT COMPUTING 2019年 79卷 59-73页

作者： Saad, Eman Elhosseini, Mostafa A. Haikal, Amira Y. Mansoura Univ Commun & Informat Technol Ctr Mansoura Egypt Mansoura Univ Comp Engn & Control Syst Dept Mansoura Egypt

In this paper, a hybridization model based on culture algorithm and Artificial Bee Colony is proposed. The objective of the hybrid model is mainly to get benefit of the previous knowledge gained by predecessor foragers which help bees searching for food sources in potential positions. The proposed CB-ABC focuses on the kind of information in the belief space that should be stored to reduce promising solutions' area. Moreover, CB-ABC divides the population into two groups of individuals, one group updates by the heritage of best previous solutions and the other group updates by self-adaptive information. The performance of the new algorithm has been validated on a variety of numerical testbench functions (ranging from CEC 2005 and CEC 2017) and compared to standard ABC and other variants named ABCM, HPA and CABCA as well. The proposed algorithm proves its success as well when applied on Wireless Sensor Network (WSN) to locate them accurately. To validate the performance of CB-ABC it is compared with Particle Swarm Optimization (PSO), Genetic algorithm (GA), Cuckoo Search (CS), Firefly algorithm (FA) and Parallel Firefly algorithm (PFA). CB-ABC shows the least average error value compared to the standard ABC as well as the other algorithms previously mentioned in the comparison. Moreover, CB-ABC succeeds in reducing the number of iterations as well as function evaluations to 17% of those of the standard ABC to 20% of those obtained by ABCM during solving WSN localization problem. CB-ABC's parametric study (heritage size and offspring ratio) has been carried out as well. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Artificial Bee Colony culture algorithm Wireless sensors localization

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cultured Artificial Fish Swarm algorithm: An Experimental Evaluation 2

Cultured Artificial Fish Swarm Algorithm: An Experimental Ev...

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2nd International Conference of the IEEE-Nigeria-Computer-Chapter (NigeriaComputConf)

作者： Imam, Maryam Lami Adebiyi, Busayo H. Bello-Salau, Habeeb Olarinoye, Gbenga A. Momoh, Muyideen O. Univ Jos Dept Elect & Elect Engn Jos Nigeria Ahmadu Bello Univ Dept Comp Engn Zaria Nigeria Ahmadu Bello Univ Dept Elect Engn Zaria Nigeria

ISBN: (纸本)9781728107134

In this work, a Weighted Cultural Artificial Fish Swarm algorithm (wCAFSA) which is an amendment of standard artificial fish swarm algorithm (AFSA) is proposed. This algorithm can adaptively select its parameters at every generation in order to reduce the ease at which standard AFSA falls into local optimal We first introduce inertial weight to adaptively determine visual distance and step size of AFSA thereafter, the Situational and Normative knowledge inherent in cultural algorithm are used to develop new variants of weighted cultural AFSA (wCAFSA Ns, wCAFSA sd, wCAFSA Ns+Sd and wCAFSA Ns+Nd). A collection of sixteen (16) optimization benchmark functions are used to test the performance of the algorithms. The simulation results disclosed that all the new variants of the wCAFSA outclassed the AFSA.

关键词： Artificial Fish Swarm algorithm Inertia Weight culture algorithm Test Functions

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cultured Artificial Fish Swarm algorithm: An Experimental Evaluation

Cultured Artificial Fish Swarm Algorithm: An Experimental Ev...

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International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf)

作者： Maryam Lami Imam Busayo H. Adebiyi Habeeb Bello-Salau Gbenga A. Olarinoye Muyideen O. Momoh Department of Electrical and Electronics Engineering University of Jos Jos Nigeria Department of Computer Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria

ISBN: (纸本)9781728107141

In this work, a Weighted Cultural Artificial Fish Swarm algorithm (wCAFSA) which is an amendment of standard artificial fish swarm algorithm (AFSA) is proposed. This algorithm can adaptively select its parameters at every generation in order to reduce the ease at which standard AFSA falls into local optimal. We first introduce inertial weight to adaptively determine visual distance and step size of AFSA thereafter, the Situational and Normative knowledge inherent in cultural algorithm are used to develop new variants of weighted cultural AFSA (wCAFSA Ns, wCAFSA sd, wCAFSA Ns+Sd and wCAFSA Ns+Nd). A collection of sixteen (16) optimization benchmark functions are used to test the performance of the algorithms. The simulation results disclosed that all the new variants of the wCAFSA outclassed the AFSA.

关键词： Artificial fish swarm algorithm Inertia weight culture algorithm Test functions dike swarms Function algorithms local optimum step size Fishes

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Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2016年第4期8卷 1-14页

作者： Chen, Wen-Jong Lin, Cai-Xuan Chen, Yan-Ting Lin, Jia-Ru Natl Changhua Univ Educ Dept Ind Educ & Technol 1 Jin De Rd Changhua 500 Taiwan

This article combined Taguchi method and analysis of variance with the culture-based quantum-behaved particle swarm optimization to determine the optimal models of gating system for aluminium (Al) A356 sand casting part. First, the Taguchi method and analysis of variance were, respectively, applied to establish an L-27(3(8)) orthogonal array and determine significant process parameters, including riser diameter, pouring temperature, pouring speed, riser position and gating diameter. Subsequently, a response surface methodology was used to construct a second-order regression model, including filling time, solidification time and oxide ratio. Finally, the culture-based quantum-behaved particle swarm optimization was used to determine the multi-objective Pareto optimal solutions and identify corresponding process conditions. The results showed that the proposed method, compared with initial casting model, enabled reducing the filling time, solidification time and oxide ratio by 68.14%, 50.56% and 20.20%, respectively. A confirmation experiment was verified to be able to effectively reduce the defect of casting and improve the casting quality.

关键词： Taguchi method analysis of variance response surface methodology Al A356 culture algorithm quantum-behaved particle swarm optimization

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Optimization deployment of wireless sensor networks based on culture-ant colony algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2015年 250卷 58-70页

作者： Sun, Xuemei Zhang, Yiming Ren, Xu Chen, Ke Tianjin Polytech Univ Sch Comp Sci & Software Tianjin 300382 Peoples R China

The optimization of nodes deployment is one of the most active research areas in wireless sensor networks. In this paper, we propose an improved culture algorithm-ant colony algorithm (CA-ACA) to solve the problem of nodes deployment. Double evolution mechanism of culture algorithm is integrated into the improved ant colony optimization algorithm within the population space as an evolutionary strategy, and then directs the search of population space through the elites of continuous evolution in belief space. The introduction of culture algorithm makes the search for optimization faster and better stability of CA-ACA than traditional ones. In addition, greedy strategy is introduced for the situation of sparsely monitored points, which makes CA-ACA be suitable for any environment. Furthermore, we also investigate the convergence judging method which makes CA-ACA avoid premature convergence so as to achieve the purpose of global optimization. A large number of simulation experiments have been conducted and the results not only demonstrate the validity of CA-ACA, but also verify that CA-ACA algorithm can optimize the number of sensors deployed in network under the conditions of guaranteed connectivity and coverage. Current results are of great significance to effectively design the optimal deployment of nodes in wireless and mobile sensor networks. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Wireless sensor networks Nodes deployment Coverage Connectivity culture algorithm Ant colony algorithm

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Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition

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INFORMATION SCIENCES 2016年 326卷 315-333页

作者： Liu, Zhi-Zhong Chu, Dian-Hui Song, Cheng Xue, Xiao Lu, Bao-Yun Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo 454000 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China

Inspired by the evolution process of human intelligence and the social learning theory, a new swarm intelligence algorithm paradigm named the social learning optimization (SLO) algorithm is proposed. SLO consists of three co-evolution spaces: the bottom is the micro-space, where genetic evolution occurs;the middle layer is the learning space, where individuals enhance their intelligence through imitation learning and observational learning;knowledge is extracted from the middle layer and delivered to the top layer, which is called the belief space, where culture is established through knowledge accumulation and used to guide individuals' genetic evolution in the micro-space regularly. SLO is an optimization algorithm model for optimization problems, and a concrete algorithm could be generated by embodying SLO's three evolution spaces. Moreover, to demonstrate how to employ SLO and verify its superiority, this paper proposes the specific SLO (S-SLO) to solve the problem of QoS-aware cloud service composition. S-SLO is constructed by integrating the improved differential evolutionary (DE) algorithm and improved social cognitive optimization (SCO) into the micro-space and the learning space, respectively. Finally, experimental results and performance comparison show that the S-SLO is both effective and efficient. This work is expected to explore a novel swarm intelligence optimization model with better search capabilities and convergence rates, as well as to extend the theory of the swarm intelligence optimization algorithm. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence Social learning optimization algorithm Differential evolutionary algorithm Social cognitive optimization algorithm culture algorithm Cloud service composition

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Multi-spectral Remote Sensing Images Classification Method Based on Adaptive Immune Clonal Selection culture algorithm

Multi-spectral Remote Sensing Images Classification Method B...

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7th International Conference on Intelligent Computing (ICIC)

作者： Guo, Yi-Nan Xiao, Dawei Zhang, Shuguo Cheng, Jian China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9783642247279

In immune clonal selection algorithm for remote sensing images classification problem, only clonal selection mechanism is adopted. It makes the exploitation and exploration of the algorithm limited. To solve above problem, adaptive immune clonal selection culture algorithm is introduced in the paper. It fully uses the dual evolution mechanism of culture algorithm to extract implicit knowledge in belief space. According to the evolution situation noted in topological knowledge, a hybrid selection strategy integrating clonal selection and (mu+lambda) selection are proposed in population space. Simulation results indicate that the classification method based on adaptive immune clonal selection culture algorithm can improve the classifier performance better.

关键词： Immune clonal selection remote sensing images classification culture algorithm

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Reliable Web service composition based on QoS dynamic prediction

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SOFT COMPUTING 2015年第5期19卷 1409-1425页

作者： Liu, Zhi Zhong Jia, Zong Pu Xue, Xiao An, Ji Yu Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo 454000 Peoples R China

Web service dynamic composition is a key technology for creating value-added services by composing available services and applications. With the rapid development of web service, cloud computing, big data and internet of things, more and more services with identical functionality and different Quality of Service (QoS) are available;moreover, QoS of Web services are highly dynamic, so, how to create composite Web services reliably and efficiently is still an open issue. For this problem, this paper proposes a reliable Web service composition method based on global QoS constraints decomposition and QoS dynamic prediction. The approach includes two critical phases: firstly, before service composition, global QoS constraints are decomposed into local constraints, and the problem of Web service dynamic composition is transformed to a local optimization problem;secondly, during the running time, optimal Web service is selected for the current abstract service based on predicted QoS values. Experiment results show that our approach can greatly enhance the reliability of composite Web service.

关键词： Web service dynamic composition Global QoS constraints decomposition Genetic algorithm culture algorithm QoS prediction Case-based reasoning

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