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检索条件"主题词=Swarm intelligence algorithm"

共 181 条记录，以下是131-140 订阅

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Optimized Two Dimensional Wavelet Filter from BAT algorithm 7

Optimized Two Dimensional Wavelet Filter from BAT Algorithm

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7th IEEE International Conference on Smart Structures and Systems (ICSSS)

作者： Ravi, Renjith, V Subramaniam, Kamalraj MEA Engn Coll Elect & Commun Engn Malappuram Kerala India Karpagam Acad Higher Educ Fac Engn Elect & Commun Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781728172231

Due to the effect of a quantization error, it is not possible to fully restore the original image in the lossy wavelet-based image compression. However, the quantization error can be minimized by optimizing or evolving the filter bank. In this work, the coefficients of the standard wavelet filter and its inverse filter were optimized by evolution of Bat algorithm. The optimized wavelet filters were used with the SPIHT encoder/decoder for image compression. The performance of the optimized filters in reconstruction during the decompression process was investigated using the metrics PSNR, MSE and SSIM. The results obtained show that the proposed filter outperforms standard wavelet filters by minimizing the error between the original and the decompressed image.

关键词： Optimized Wavelet Filter Evolved Wavelet Filter DWT Optimization Adaptive DWT BAT algorithm swarm intelligence algorithm

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DITGOssi: a two-stage invasive tumor growth optimization algorithm for the detection of SNP-SNP interactions

DITGOssi: a two-stage invasive tumor growth optimization alg...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Tan, Kaiwen Dong, Shoubing Zhou, Jing Hu, Jinlong South China Univ Technol Commun & Comp Network Lab Guangdong Sch Comp Sci & Engn Wushan Rd 381 Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538654880

Detecting SNP-SNP interactions for complex diseases is a computationally complex task in genome-wide association studies (GWAS). The number of single-nucleotide polymorphism (SNP) is so large that many powerful methods can't be adopted to detect potential SNP-SNP interactions, therefore, trade-off between detection time and detection power is the key point of SNP-SNP interactions detection. In this paper, based on swarm optimization algorithm Invasive Tumor Growth Optimization (ITGO), a two-stage algorithm called DITGOssi is proposed to detect SNP-SNP interactions in case-control study, which consists of a basic DITGOssi algorithm and an improved two-stage strategy. The basic DITGOssi algorithm is a discrete ITGO algorithm, and the improved two-stage strategy has been applied to enhance the global search capability of basic DITGOssi algorithm. The experimental results in the simulation datasets indicate that our algorithm outperforms some recent algorithms in terms of detection power and computational complexity.

关键词： swarm intelligence algorithm GWAS SNP-SNP interactions

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Modified Printed Bow-Tie Antenna for RF Energy Harvesting Applications

Modified Printed Bow-Tie Antenna for RF Energy Harvesting Ap...

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IEEE Workshop on Microwave Theory and Techniques in Wireless Communications (MTTW)

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Nikolaidis, Spyridon Goudos, Sotirios K. Aristotle Univ Thessaloniki Phys Dept Thesssaloniki Greece

ISBN: (纸本)9781728193984

As the power requirements of electronic systems in wireless sensor networks are progressively reducing, radio frequency (RF) energy harvesting (EH) technique is becoming increasingly interesting during the last years. In this paper, we design and optimize a modified printed bow-tie antenna, operating in the frequency band of 5G NR mobile communication networks. For the optimization process, we have selected the Monarch Butterfly algorithm that combines swarm intelligence and evolutionary characteristics. Computed results exhibit a quite acceptable performance of the optimized antenna as an RF energy harvester in the desired frequency band.

关键词： radio frequency energy harvesting bow-tie antenna monarch butterfly optimization evolutionary algorithm swarm intelligence algorithm

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An Artificial Bee Colony algorithm for Minimum Weight Dominating Set

An Artificial Bee Colony Algorithm for Minimum Weight Domina...

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IEEE Symposium on swarm intelligence (SIS)

作者： Nitash, C. G. Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Telangana India

ISBN: (纸本)9781479944583

The minimum weight dominating set (MWDS) problem is a classic NP-Hard optimisation problem with a wide range of practical applications. As a result, many algorithms have been proposed for this problem. Several greedy and approximation algorithms exist which provide good results for unit disk graphs with smooth weights. However, these algorithms do not perform well when applied to general graphs. There are a few metaheuristics in the literature such as genetic algorithms and ant colony optimisation algorithm, which also work for general graphs. In this paper, a swarm intelligence algorithm called artificial bee colony (ABC) algorithm is presented for the MWDS problem. The proposed ABC algorithm is compared with other metaheuristics in the literature and shown to perform better than any of these metaheuristics, both in terms of solution quality and time taken.

关键词： genetic algorithms graph theory set theory swarm intelligence ABC algorithm MWDS problem NP-hard optimisation problem ant colony optimisation algorithm artificial bee colony algorithm general graphs metaheuristics minimum weight dominating set swarm intelligence algorithm undirected graph algorithm design and analysis Approximation algorithms Genetic algorithms Maintenance engineering Optimization Sociology Statistics

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An improved moth-flame algorithm based on differential evolution and shuffled frog leaping algorithm

An improved moth-flame algorithm based on differential evolu...

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Chinese Automation Congress (CAC)

作者： Li, Zhifu Zeng, Junhai Zhong, Yun Guangzhou Univ Sch Mech & Elect Engn Guangzhou Peoples R China

ISBN: (纸本)9781728140940

Generally speaking, in swarm intelligence algorithm, keeping the diversity of the population and strengthening its ability to escape the local optima will help to enhance the performance of an optimization algorithm. To strengthen these abilities, an improved moth-flame optimization (MFO) algorithm combining differential evolution (DE) and shuffled frog leaping algorithm (SFLA), namely DEFMFO, is proposed. In the proposed algorithm, the DE is used to enrich the population diversity, and the SFLA is proposed to get the optimal solution which strengthens the ability to escape the local optima. The simulation results show that, compared with particle swarm optimization (PSO), bat algorithm (BA) and classic MFO by testing on eight benchmark functions, the DEFMFO algorithm has better performance in global search and convergence speed.

关键词： swarm intelligence algorithm moth-flame optimization differential evolution

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Pattern Search Firefly algorithm for Solving Systems of Nonlinear Equations 7

Pattern Search Firefly Algorithm for Solving Systems of Nonl...

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7th International Symposium on Computational intelligence and Design (ISCID)

作者： Wang, Xiaogang Zhou, Ning Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9781479970056

It is very important problem to solve systems of nonlinear equations in scientific research and engineering computation. Most traditional algorithms have the shortcomings such as high sensitivity to initial guess of the solution and poor convergence. In this paper, we present a method to solve nonlinear equation systems by using the Firefly algorithm. It is very effective to improve computation precision that Firefly algorithm integrates with pattern search strategy. We select several nonlinear equation systems which are solved with Pattern Search Firefly algorithm, while compared with other intelligence optimization algorithms such as Particle swarm Optimization, Genetic algorithm, etc. Numerical results show that Pattern Search Firefly algorithm has availability, global convergence reliability, and high computation precision, especially has advantages in solving higher dimension systems of nonlinear equations.

关键词： Firefly algorithm Pattern search system of nonlinear equations swarm intelligence algorithm bionics algorithm Hooke-Jeeves

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Binary-Organoid Particle swarm Optimisation for Inferring Genetic Networks

Binary-Organoid Particle Swarm Optimisation for Inferring Ge...

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2010 IEEE World Congress on Computational intelligence

作者： Chanthaphavong, Santi S. Chetty, Madhu Monash Univ Gippsland Sch Informat Technol Fac Informat Technol Churchill 3842 Australia

ISBN: (纸本)9781424481262

A holistic understanding of genetic interactions is crucial in the analysis of complex biological systems. However, due to the dimensionality problem (less samples and large number of genes) of microarray data, obtaining an optimal gene regulatory network is not only difficult but also computationally expensive. In this paper, a Bayesian model for the genetic interactions using the Minimum Description Length as a scoring metric is proposed. For fast optimisation of the network structure, we propose a novel swarm intelligence algorithm called Binary-Organoid Particle swarm (BORG-swarm). In BORG-swarm we introduce the concepts of probability threshold vector and particle drift to update particle positions. Experimental studies are carried out using real-life yeast cell cycle dataset. Results indicate that existing binary swarms fail to converge and suffer from long runtimes. In constrast, BORG-swarm's fast convergence towards the global optimum becomes apparent from results of extensive simulations.

关键词： BORG-swarm algorithm Bayes methods Bayesian model artificial intelligence binary-organoid particle swarm optimisation biology computing complex biological system data handling dimensionality problem genetic algorithms genetic interaction genetic network genetics microarray data minimum description length network structure optimal gene regulatory network particle drift particle swarm optimisation probability probability threshold vector real-life yeast cell cycle dataset scoring metric swarm intelligence algorithm

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A Hybrid Artificial Bee Colony algorithm for Satisfiability Problems Based on Tabu Search 3

A Hybrid Artificial Bee Colony Algorithm for Satisfiability ...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Guo, Ying Zhang, Changsheng Ningxia Inst Technol Coll Elect & Informat Engn Shizuishan Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781509063529

For satisfiability problems, a hybrid artificial bee colony algorithm named T-ABCSAT is proposed based on tabu search. It adopts the basic model of artificial bee colony algorithm, so has better balance of global search and local search. Moreover, by introducing tabu search, capability of local search is enhanced. Experimental and comparative results demonstrate that the effectiveness of T-ABCSAT is obviously better than that of the algorithm without tabu search, and T-ABCSAT has better comprehensive performance than the same kind of hybrid algorithm based on genetic algorithm.

关键词： artificial bee colony algorithm satisfiability problems tabu search swarm intelligence algorithm

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In Vivo Computation for Tumor Sensitization and Targeting at Different Tumor Growth Stages

In Vivo Computation for Tumor Sensitization and Targeting at...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational intelligence (IEEE WCCI)

作者： Shi, Shaolong Chen, Yifan Gong, Zheng Lin, Xiaoyou Sharifi, Neda Yao, Xin Harbin Inst Technol Harbin Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Univ Waikato Sch Engn Hamilton New Zealand Putian Univ Sch Informat Engn Putian Peoples R China

ISBN: (纸本)9781728169293

We look into the novel paradigm of in vivo computation for tumor sensitization and targeting (TST), which aims at detecting a tumor by considering TST as a computational process. Nanorobots are utilized as computational agents to search for the tumor in the high-risk tissue with the aided knowledge of the tumor-triggered biological gradient field (BGF), which is similar to an optimization process. All our previous work is about the detection of tumor with a priori size, which is not convincing enough as the exact size of the tumor targeted cannot be obtained in advance. We focus on the TST for tumor with unknown size by considering the tumor growth process in this paper. The weak priority evolution strategy (WP-ES) based in vivo computational algorithm proposed in our previous work is utilized for the TST at three tumor growth stages for two representative landscapes by considering the nanorobots' lifespans and other realistic constraints. Furthermore, we propose the "tension and relaxation (T-R)" principle, which is used for the actuating of nanorobots in the TST process for the tumor with unknown size. The experimental results demonstrate the effectiveness of the proposed in vivo computational algorithm and principle for the TST at different tumor growth stages.

关键词： Tumor sensitization and targeting in vivo computation nanorobots tumor growth stages swarm intelligence algorithm

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Optimal Control and Performance Enhancement of DC-DC Bidirectional SEPIC Converter 13

Optimal Control and Performance Enhancement of DC-DC Bidirec...

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IEEE 13th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Mahmud, Al Jaber Mithun, Mehedi Hasan Khan, Md. Ashik Faisal, Fahim Nishat, Mirza Muntasir Hoque, Md Ashraful Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (数字)9781665492997

ISBN: (纸本)9781665492997

This paper represents an inspective work on the closed-loop stability of the Bidirectional SEPIC converter by implementing the Firefly algorithm (FA) and Particle swarm Optimization (PSO) for scheming an optimized PID controller to improve the Percentage of Overshoot (%OS), Rise Time (Tr), Settling Time (Ts), and Peak Amplitude (PA). At first, the transfer function of the converter's open-loop system was determined after which the converter's closed-loop arrangement was introduced with the controller's help. The algorithms were evaluated in the system through different fitness functions: IAE, ITAE, ISE, and ITSE. MATLAB is used to carry out all the simulations. After analyzing the performances for the case of the Bidirectional SEPIC converter, PSO-PID (ITSE) could be chosen as a more optimized controller than FA-based PID controllers as to performance parameters. In this case, values of overshoot (0.2674%) and PA (1.0027) were lower than FA-PID for error function. For rise time, the value of FA-PID (ITAE) was better (0.0670 sec), and for settling time, the value of FA-PID (ITSE) was better (0.1217 sec). Hence, swarm intelligence algorithms-based optimized PID controller provides more optimized results and performs far better than conventional PID controller for Bidirectional SEPIC converter.

关键词： swarm intelligence algorithm Bidirectional SEPIC Optimal Control PID Controller

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