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bioinspired algorithm for 2D Packing Problem 7th

7th Computer Science On-Line Conference (CSOC)

作者： Kureichik, Vladimir Kureichik, Liliya Kureichik, Vladimir, Jr. Zaruba, Daria Southern Fed Univ Rostov Na Donu Russia

ISBN: (纸本)9783319911892

The paper considers one of the most important problem of resource allocation - packing of units within 2D space. The problem is NP-hard. A problem formulation is made, as well as restrictions and boundary conditions are found out. To solve the considered problem the authors suggest to use firefly optimization algorithm on the basis of which there are developed a bioinspired algorithm. This algorithm allows to obtain sets of quazi-optimal solutions for the 2D packing problem within polynomial time. Also, there are suggested mechanisms for encoding and decoding of alternative solutions. and presented a scheme of firefly algorithm for 2D packing problem. On the basis of the suggested algorithm there are developed software for computational experiments on benchmarks. Experimental investigations were carried out taking into account time and quality of alternative solutions. As a result, experiments shows the effectiveness of the developed algorithm.

关键词： Packing problem Optimization bioinspired algorithm Firefly algorithm

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bioinspired Bare Bones Mayfly algorithm for Large-Scale Spherical Minimum Spanning Tree

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 10卷 830037页

作者： Zhang, Tian Zhou, Yongquan Zhou, Guo Deng, Wu Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin Peoples R China

Mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid adjusting parameters, a bioinspired bare bones mayfly algorithm (BBMA) is proposed. The BBMA adopts Gaussian distribution and Levy flight, which improves the convergence speed and accuracy of the algorithm and makes better exploration and exploitation of the search region. The minimum spanning tree (MST) problem is a classic combinatorial optimization problem. This study provides a mathematical model for solving a variant of the MST problem, in which all points and solutions are on a sphere. Finally, the BBMA is used to solve the large-scale spherical MST problems. By comparing and analyzing the results of BBMA and other swarm intelligence algorithms in sixteen scales, the experimental results illustrate that the proposed algorithm is superior to other algorithms for the MST problems on a sphere.

关键词： mayfly algorithm bare bones mayfly algorithm large-scale spherical MST Prufer code bioinspired algorithm

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bioinspired Cooperative Wireless Energy Transfer for Lifetime Maximization in Multihop Networks

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IEEE INTERNET OF THINGS JOURNAL 2021年第3期8卷 1611-1622页

作者： Choi, Hyun-Ho Lee, Kisong Hankyong Natl Univ Sch ICT Robot & Mech Engn Anseong 17579 South Korea Dongguk Univ Dept Informat & Commun Engn Seoul 04620 South Korea

To extend the lifetime of a multihop network by addressing energy shortages in wireless nodes, we apply wireless energy transfer (WET) technology to the multihop transmission. Considering a linear multihop topology, we establish a system model for a WET-enabled multihop transmission and formulate an optimization problem that obtains the optimal WET time of each node to maximize the lifetime of multihop networks. To solve this problem, we adopt a flocking model inspired by the similarity between flocking behaviors and WET-enabled multihop transmissions. Applying the underlying principles of the flocking model, we propose a bioinspired cooperative WET (BiCoWET) algorithm, in which each node adjusts its own WET time to equalize the lifetime of all nodes in a distributed manner. Theoretical analysis verifies that the proposed BiCoWET algorithm achieves optimality with exponential convergence. The intensive simulation shows that the proposed BiCoWET outperforms the conventional multihop transmission methods without WET and maximizes the network lifetime by equalizing the lifetime of all nodes.

关键词： Spread spectrum communication Peer-to-peer computing Wireless sensor networks Relays Throughput Wireless communication Biological system modeling bioinspired algorithm cooperation lifetime maximization multihop network wireless energy harvesting (WEH) wireless energy transfer (WET)

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Tracking and Sensor Coverage of Spatio-temporal Quantities Using a Swarm of Artificial Foraging Agents

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Journal of Bionic Engineering 2016年第4期13卷 679-689页

作者： John Oluwagbemiga Oyekan Dongbing Gu Huosheng Ha School of Aerospace Transport and Manufacturing Cranfield University College Road Cranfield MK43 OAL UK School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park Colchester CO4 3SQ UK

Using a network of mobile sensors to track and map a dynamic spatio-temporal process in the environment is one of the current challenges in multi-agent systems. In this work, a distributed probabilistic multi-agent algorithm inspired by the bacte- rium foraging behavior is presented. The novelty of the algorithm lies in being capable of tracking and mapping a spa- tio-temporal quantity without the need of machine learning, estimation algorithms or future planning. This is unlike most current techniques that rely heavily on machine learning to estimate the distribution as well as the profile of spatio-temporal quantities. The experimental studies carried out in this work show that the algorithm works well by following the concentration gradient of a dynamic plume created under diffusive conditions. Furthermore, the algorithm is inherently capable of finding the source of a diffusive spatio-temporal quantity as well as performing environmental exploration. It is computationally tractable for simple agents, shown to adapt to its environment and can deal successfully with noise in sensor readings as well as in robot dynamics.

关键词： bioinspired algorithm artificial foraging swarm spatio-temporal mapping bacterium

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Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

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Journal of Bionic Engineering 2023年第3期20卷 1153-1174页

作者： Xin Wang Xiaogang Dong Yanan Zhang Huiling Chen School of Mathematics and Statistics Changchun University of TechnologyChangchunJilin130012China School of Management Xi’an Jiaotong UniversityXi’anShaanxi710049China Information Construction Office Changchun University of TechnologyChangchunJilin130012China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China

Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.

关键词： Harris hawks optimization bioinspired algorithm Global optimization Engineering optimization Feature selection

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Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment

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JOURNAL OF SUPERCOMPUTING 2022年第7期78卷 10176-10190页

作者： Kandan, M. Krishnamurthy, Anbazhagan Selvi, S. Arun Mozhi Sikkandar, Mohamed Yacin Aboamer, Mohamed Abdelkader Tamilvizhi, T. Aditya Engn Coll Dept CSE Surempalem 533437 Andhra Pradesh India Velammal Inst Technol Dept CSE Chennai 601204 Tamil Nadu India Holycross Engn Coll Thoothukudi 628851 Tamil Nadu India Majmaah Univ Coll Appl Med Sci Dept Med Equipment Technol Al Majmaah 11952 Saudi Arabia Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala E Dept Informat Technol Chennai Tamil Nadu India

Large-scale applications of the Internet of Things (IoT) necessitate significant computing tasks and storage resources that are progressively installed in the cloud environment. Related to classical computing models, the features of the cloud, such as pay-as-you-go, indefinite expansions, and dynamic acquisition, signify various services to these applications utilizing the IoT structure. A major challenge is to fulfill the quality of service necessities but schedule tasks to resources. The resource allocation scheme is affected by different undefined reasons in real-time platforms. Several works have considered the factors in the design of effective task scheduling techniques. In this context, this research addresses the issue of resource allocation and management in an IoT-enabled CC environment by designing a novel quasi-oppositional Aquila optimizer-based task scheduling (QOAO-TS) technique. The QOAO technique involves the integration of quasi-oppositional-based learning with an Aquila optimizer (AO). The traditional AO is stimulated by Aquila's behavior while catching the prey, and the QOAO is derived to improve the performance of the AO. The QOAO-TS technique aims to fulfill the makespan by accomplishing the optimum task scheduling process. The proposed QOAO-TS technique considers the relationship among task scheduling and satisfies the client's needs by minimizing the makespan. A wide range of simulations take place, and the results are investigated in terms of the span, throughput, flow time, lateness, and utilization ratio.

关键词： Cloud computing Internet of Things Task scheduling Objective function Makespan bioinspired algorithm

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Exploration-exploitation model of moth-inspired olfactory navigation

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2024年第216期21卷 20230746-20230746页

作者： Lazebnik, Teddy Golov, Yiftach Gurka, Roi Harari, Ally Liberzon, Alex Ariel Univ Dept Math Ariel Israel UCL Canc Inst Dept Canc Biol London England Volcani Ctr Dept Entomol Rishon Leziyyon Israel Coastal Carolina Univ Dept Phys & Engn Sci Conway SC USA Tel Aviv Univ Sch Mech Engn Turbulence Struct Lab Tel Aviv Israel

Navigation of male moths towards females during the mating search offers a unique perspective on the exploration-exploitation (EE) model in decision-making. This study uses the EE model to explain male moth pheromone-driven flight paths. Wind tunnel measurements and three-dimensional tracking using infrared cameras have been leveraged to gain insights into male moth behaviour. During the experiments in the wind tunnel, disturbance to the airflow has been added and the effect of increased fluctuations on moth flights has been analysed, in the context of the proposed EE model. The exploration and exploitation phases are separated using a genetic algorithm to the experimentally obtained dataset of moth three-dimensional trajectories. First, the exploration-to-exploitation rate (EER) increases with distance from the source of the female pheromone is demonstrated, which can be explained in the context of the EE model. Furthermore, our findings reveal a compelling relationship between EER and increased flow fluctuations near the pheromone source. Using an olfactory navigation simulation and our moth-inspired navigation model, the phenomenon where male moths exhibit an enhanced EER as turbulence levels increase is explained. This research extends our understanding of optimal navigation strategies based on general biological EE models and supports the development of bioinspired navigation algorithms.

关键词： physical simulation biological signal processing bioinspired algorithm navigation performance

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Parallel Approach to the Firefly algorithm 44

Parallel Approach to the Firefly Algorithm

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44th Latin American Computing Conference on Informatics (CLEI)

作者： Linhares, Gabriel Costa, Guilherme Jacobson, Leonardo Rodrigues, Lucio Ballabenute, Vitor Rodrigues, Paulo Sergio Wachs-Lopes, Guilherme Alberto Ctr Univ FEI Comp Sci Dept Sao Bernardo Do Campo Brazil

ISBN: (纸本)9781728104379

Distributed computing is a form of computation in which many calculations are made at the same time, operating under the understanding that big problems can sometimes be divided into little ones, that them are solved at the same time. This paper has the objective of introducing the distributed computing concept to an optimized version of the Firefly algorithm (FA) and based on the results discuss if the proposed distributed version shows itself to be superior or inferior to the regular existing algorithm.

关键词： Optimization Distributed Computing bioinspired algorithm Firefly

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A Hybrid Firefly algorithm 2

A Hybrid Firefly Algorithm

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2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Qi, Xiangbo Zhu, Sihan Zhang, Hao Chinese Acad Sci Shenyang Inst Automat Shenyang Liaoning Peoples R China Northeast Yucai Foreign Language Sch Shenyang Liaoning Peoples R China

ISBN: (纸本)9781467389792

Firefly algorithm (FA) is a bio-inspired algorithm simulating the flashing behavior of fireflies. In original algorithm, all the fireflies are unisex and only the flashing behavior is simulated. This paper presents a novel hybrid firefly algorithm (HFA) adding mating behavior in original firefly algorithm. The new algorithm is compared with FA and other three well-known bio-inspired algorithms on fifteen benchmark functions. The experimental results show HFA has a good optimizing ability on most benchmark functions and is proved to have significant improvement over canonical FA and several other comparison algorithms.

关键词： firefly algorithm hybrid firefly algorithm bioinspired algorithm

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Partitioning of VLSI Fragments Based on the Model of Glowworm's Behavior 19

Partitioning of VLSI Fragments Based on the Model of Glowwor...

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19th IEEE International Conference on Soft Computing and Measurements (SCM)

作者： Kureichik, Vladimir V. Zaporozhets, Dmitry Yu. Zaruba, Daria V. Southern Fed Univ Comp Aided Design Dept Rostov Na Donu Russia

ISBN: (纸本)9781467389198

The paper deals with one of the most important VLSI design problem - partitioning of VLSI fragments. This problem belongs to the NP-hard problems class, and there are no accuracy method to solve it. The authors suggest a new heuristic approach based on the glowworm's behavior in nature. This method allows effectively to solve the problem of preliminary convergence by the use of probabilistic heuristics. A modified statement of the partitioning problem is made. The modification consists in simulation of circuit as a bigraph, where each vertices simulate either an element or a net, and each edges-as incidence relation between them. To carry out an computational experiments there were developed a software. As a result the authors have received an estimation of time complexity, represented by O(n2).

关键词： VLSI partitioning problem bioinspired algorithm glowworm swarm optimization optimization Introduction

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