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RWFOA: a random walk-based fruit fly optimization algorithm

SOFT COMPUTING 2020年第16期24卷 12681-12690页

作者： Chen, Chong Shandong Polytech Jinan Peoples R China

As one kind of the classic swarm intelligence (SI) algorithms, fruit fly optimization algorithm has been widely used in many aspects, such as multiple objective optimization and service computing. However, due to the limitation that introduced by the initial population location, its global optimization ability is limited. Therefore, we propose a random walk-based fruit fly optimization algorithm, namely RWFOA, to enhance its global optimization ability. RWFOA employs the random walk mechanism to dynamically adjust the position of the fruit fly population, which can reduce the impact of the initial population location and thus enhance the global optimization ability. We conduct a comprehensive experimental evaluation of RWFOA by comparing it with three representative algorithms, i.e., the OFOA, CFOA and IFFO, over 29 widely used benchmark functions published in CEC 2015. Experimental results demonstrate that RWFOA can find better solutions in most of the selected benchmark functions.

关键词： Fruit fly optimization algorithm Random work Probability graph Roulette scheme swarm intelligence algorithm

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Multi-objective biofilm algorithm (MOBifi) for de novo drug design with special focus to anti-diabetic drugs

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APPLIED SOFT COMPUTING 2020年 96卷 106655-106655页

作者： Devi, R. Vasundhara Sathya, S. Siva Coumar, Mohane Selvaraj Pondicherry Univ Dept Comp Sci Pondicherry India Pondicherry Univ Ctr Bioinformat Pondicherry India

swarm intelligence algorithms are inspired by the collective and intelligent behavior of microorganisms, insects, birds or animals, to solve real-world optimization problems. The extraordinary intelligence and coordination observed among bacteria in biofilm formed the basis of a novel Biofilm algorithm introduced earlier. It simulates the life cycle of bacteria in a biofilm in terms of attachment, maturation and dispersal phases. Here, the Biofilm algorithm is extended to solve multi-objective problems. For the multi-objective biofilm algorithm (MOBifi), the concept of pareto dominance is followed. MOBifi is evaluated using CEC 2009 tri-objective benchmark test functions and compared with well-known multi-objective algorithms. Further, the utility of the MOBifi algorithm is substantiated with a real world multi-objective optimization problem, viz., de novo drug design (DNDD) of anti-diabetic drug-like molecules. The newly designed drug-like molecules using MOBifi are evaluated for their novelty and ability to interact with the anti-diabetic drug targets. The results showed that MOBifi could design novel molecules that could be further tested in the laboratory for diabetes treatment. The MOBifi algorithm disclosed here could be extended for other multi-objective optimization problems as well. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Biofilm algorithm Multi-objective optimization De novo drug design Computer-aided drug design Anti-diabetic molecules swarm intelligence algorithm

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Research on Jamming Resource Allocation Based on Improved Pelican Optimization algorithm

Research on Jamming Resource Allocation Based on Improved Pe...

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第二十一届分布式计算及其应用国际学术研讨会

作者： Junwei Zhao Yian Liu Hailing Song Jiangnan University School of AI and Computer Science

To get the best jamming effect of limited jamming resources in electronic warfare,this paper focuses on the problem of jamming resource *** objective function of jamming resource allocation is built,which is based on the calculation by the probability formula of j ammer pressing,and an improved Pelican Optimization algorithm is *** simulating the pelican's natural behavior of approaching prey and searching the nearby area in the process of hunting,the algorithm creates the corresponding mathematical model,and obtains the global optimal allocation strategy by solving the *** simulated results indicate that the probability of obtaining the optimal solution and optimal allocation strategy of the algorithm are better than the traditional Simulated Annealing algorithm,whether it is the situation of "single jammer to single radar" or "multijammer to single-radar".

关键词： component resource allocation radar jamming swarm intelligence algorithm Pelican Optimization algorithm

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Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs

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MACHINE VISION AND APPLICATIONS 2021年第5期32卷 105-105页

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

Today, advances in 3D modeling make it possible to identically reproduce objects, animals, humans and even entire scenes. The broad applications concern video games, virtual reality or augmented reality and cinema, for example. In this article, we propose a new method to build a 3D scene directly from several complementary photographs. The positions of the objects for which we already have a 3D model will be determined by triangulation, thanks to the information extracted from the photographs, such as the outline of the objects on the images. Each pixel of the images is converted into a value that gives its distance to the nearest outline. The 3D model of the objects is then projected on the converted images, and the triangulation is done using a cost function that gives the distance of each projection of the objects to their respective outlines. A projection is considered perfect when its distance to its outlines is null, which means that the cost function gives a score of zero as well. We propose to solve this optimization problem by means of two algorithms, namely Simulated Annealing (SA) and quantum particle swarm optimization (QUAPSO).

关键词： Simulated Annealing Particle swarm optimization swarm intelligence algorithm Auto-adaptive algorithm 3D model

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An improved grey wolf optimizer for solving engineering problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 166卷 113917-113917页

作者： Nadimi-Shahraki, Mohammad H. Taghian, Shokooh Mirjalili, Seyedali Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The IGWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-ofthe-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

关键词： Optimization Metaheuristic swarm intelligence algorithm Grey wolf optimizer Improved grey wolf optimizer Engineering optimization problems algorithm Artificial intelligence

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Improving machine learning accuracy in diagnosing diseases using feature selection based on the fruit- fly algorithm 6

Improving machine learning accuracy in diagnosing diseases u...

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6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Haghighi, Mehdi Salkhordeh Hoseini, Mohammad Javad Mashhadi Sadjad Univ Technol Fac Comp Engn & IT Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781728186290

Data mining methods can be used to classify people as sick or healthy and to diagnose diseases. One of the important challenges in diagnosing the disease using learning methods such as multilayer artificial neural network is the optimal selection of its parameters that can be solved to some extent with meta-heuristic methods, but these techniques are not without problems because in these methods meta-heuristic algorithm To a certain extent, it can only reduce the error, and to reduce the machine learning error, in addition to the optimal selection of learning parameters, it is necessary to consider and select the appropriate features. The proposed method has two layers and in the first layer the selection of weight and bias of the multilayer neural network is done using the fruit fly algorithm and in the input layer, the feature selection phase is done using the binary version of the fruit fly optimization algorithm to learn to Do it only on the important features and reduce the problem space and increase the learning speed and accuracy. The results of tests on several disease datasets in the UCI database show that the accuracy, sensitivity, and diagnosis of the proposed method are 98.36%, 98.12%, and 98.08%, respectively. The proposed method is more accurate in diagnosing diabetes than the PSO, FA, SHO, and HHO algorithms.

关键词： Feature selection data mining fruit fly algorithm machine learning Disease diagnosis swarm intelligence algorithm

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An Improved Sparrow Search algorithm 18

An Improved Sparrow Search Algorithm

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18th IEEE Int Symp on Parallel and Distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Song, Wei Liu, Song Wang, Xiaochun Wu, Weiguo Xi An Jiao Tong Univ Fac Elect & Informat Engn Xian Peoples R China

ISBN: (纸本)9781665414852

The sparrow search algorithm (SSA) is a relatively new swarm intelligence heuristic algorithm. It has fast convergence speed, strong optimization ability and more extensive application scenarios compared with traditional heuristic search methods. And thus, the SSA is attracting the attention of researchers in different fields. However, there are deficiencies of initial population quality, search ability, and population diversity in the SSA. Therefore, this paper proposes an improved sparrow search algorithm (ISSA). The ISSA uses skew tent map-based chaotic method to produce initial population for a higher quality of convergence. For the location update of the producer sparrows during the iterations, the ISSA introduces a non-linear decreasing weight, promoting both exploration and exploitation of the search space, to improve the convergence and search precision. And the mutation strategy is employed to update the location of the scrounger sparrows with lower energy and the chaotic search is combined with the local exploitation for the scroungers with higher energy, which can enhance the diversity and avoid trapping in local optimum. Simulation experiments are carried out on 26 benchmark test functions. And the results show that the ISSA is superior to or at least competitive to the SSA in the convergence properties of accuracy, speed, and stability.

关键词： swarm intelligence algorithm sparrow search algorithm chaotic search non-linear decreasing weight convergence accuracy

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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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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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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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