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Antenna design using animal migration optimisation algorithm

JOURNAL OF ENGINEERING-JOE 2016年第8期2016卷 298-301页

作者： Ulker, Ezgi Deniz Ulker, Sadik European Univ Lefke Dept Comp Engn TR-10 Lefke Mersin Turkey European Univ Lefke Dept Elect & Elect Engn TR-10 Lefke Mersin Turkey

The use of swarm intelligence algorithms for solving complex optimisation problems is an interesting area since they can produce remarkable results. The animal migration optimisation (AMO) algorithm is one of the swarm intelligence algorithms which is derived from the migration behaviour of animals. The implementation of AMO algorithm for complex array optimisation problems is given. The experimental studies show that the AMO algorithm produces high-quality results and it is capable to overcome strong local optimum points by using its attributes. Also, it has few number of steps which are quite easy to implement. The AMO can be suggested as a powerful algorithm for optimisation problems including complex array designs when the high solution quality is desired.

关键词： design engineering swarm intelligence dipole antennas antenna feeds optimisation variable length centre fed dipole AMO algorithm migration behaviour complex array optimisation problems swarm intelligence algorithms animal migration optimisation algorithm antenna design

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Performance Comparison of Cloudlet Scheduling Policies 1

Performance Comparison of Cloudlet Scheduling Policies

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1st International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)

作者： Geetha, V. Devi, R. Aruna Ilavenil, T. Begum, S. Marjana Revathi, S. Pondicherry Engn Coll Dept Informat Technol Pondicherry India

ISBN: (纸本)9781467367257

Cloud computing is widely accepted as the best computing and storage model for low cost and high resource utilization. User requests from public can be run in data centers of large datacenters whey they are idle. This brings in high profit for cloud providers and low cost for cloud users. The profit and response time can be improved by an optimal scheduling policy. This paper does a survey on various classes of existing cloudlet scheduling algorithms. Researchers have proposed linear algorithms, genetic algorithms and swarm intelligence algorithms. The merits and demerits of these classes of algorithms are analyzed in this paper.

关键词： Cloud Virtual Machine Cloudlet cloudlet Scheduling policies swarm intelligence algorithms Linear algorithms Genetic algorithms

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The Piranha Foraging Optimization Algorithm Oriented to Numerical Optimization Problems 7

The Piranha Foraging Optimization Algorithm Oriented to Nume...

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7th International Conference on Robotics and Automation Engineering (ICRAE)

作者： Cao, Shuai Qian, Qian Cao, Yongjun Li, Wenwei Zhou, Lei He, Hongan Huang, Weixi Liang, Jianan Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming Yunnan Peoples R China South China Robot Innovat Res Inst Robot Lab Foshan Peoples R China South Glass Technol Co LTD Foshan Peoples R China

ISBN: (纸本)9781665489188

This paper provides an efficient meta-heuristic optimization algorithm for solving continuous optimization problems in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The algorithm is inspired by the flexible and mobile foraging behavior of piranhas, which are classified into three modes of localized group attack, bloodthirsty swarming attack, and scavenging foraging, and two dynamic search behaviors of exploration and exploitation are constructed by simulating the above behaviors. PFOA allows the population to be diverse at different times of the search by means of a non-linear parameter adjustment strategy, a piranha population survival strategy and a reverse escape search strategy. Using visual means to evaluate the optimization efficiency of PFOA, this paper developed experiments on 23 standard benchmark functions and compared the results with 15 well-known meta-heuristics. Experimental results based on statistical methods such as Wilcoxon rank sum test and Friedman test in multiple dimensions (30, 50, 100 and fixed dimensions) show significant differentiation, stable and significant improvement in algorithm performance compared to other compared algorithms. The unique advantages of PFOA in terms of convergence speed and exploration utilization balance can avoid getting trapped in local optimum regions and effectively solve optimization problems with complex search spaces of multiple dimensions.

关键词： swarm intelligence algorithms piranha foraging optimization algorithm meta-heuristic algorithms

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Aperture-coupled Bowtie Patch Antenna Design for Autonomous Driving Applications

Aperture-coupled Bowtie Patch Antenna Design for Autonomous ...

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International Workshop on Antenna Technology (iWAT)

作者： Iliadis, Lazaros A. Boursianis, Achilles D. Rekkas, Vasileios P. Zaharis, Zaharias D. Koulouridis, Stavros Goudos, Sotirios K. Aristotle Univ Thessaloniki ELEDIA AUTH Sch Phys Thessaloniki Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece Univ Patras Dept Elect & Comp Engn Patras Greece

ISBN: (纸本)9798350334081

Autonomous driving will reshape transportation networks, offering at the same time many benefits such as safety, reliable vehicle-to-vehicle communication, and extended telecommunications. For a self-driving vehicle to navigate freely and perceive its environment, it is necessary to be equipped with many sensors. Automotive radars and antenna arrays that operate at 76-81 GHz have been proposed as key elements in future autonomous vehicles. In this work, an aperture-coupled bowtie antenna is designed utilizing hunger games search optimization. The proposed element achieves quite satisfactory values of return loss and gain, as well as broadband operation at 76 GHz - 81 GHz.

关键词： Aperture-coupling bowtie antenna 79 GHz autonomous driving hunger games search swarm intelligence algorithms

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Harris Hawks Optimization: A Formal Analysis of Its Variants and Applications 13th

Harris Hawks Optimization: A Formal Analysis of Its Variants...

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13th International Joint Conference on Computational intelligence (IJCCI) / 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Abu Khurma, Ruba Aljarah, Ibrahim Castillo, Pedro A. Univ Jordan King Abdullah 2 Sch Informat Technol Amman Jordan Univ Granada Dept Comp Architecture & Comp Technol ETSIIT Granada Spain Univ Granada CITIC Granada Spain

ISBN: (纸本)9789897585340

The Harris Hawks Optimization (HHO) is a recent meta-heuristic algorithm developed by Hideri in 2019. HHO algorithm has been widely utilized to solve many optimization problems in different fields. The primary objective of HHO is to define a fitness function that can successfully optimize a specific problem by finding the minimum or maximum value. This survey presents a thorough study of the algorithm, including its variants such as binary, hybridization, multi-objective and modifications. It highlights the main applications such as medical, engineering, machine learning, and network applications. Finally, the conclusion summarizes the current works on HHO and suggests possible future directions.

关键词： Harris Hawcks Optimization Algorithm HHO Meta-heuristic Optimization swarm intelligence algorithms

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swarm-Based Path Creation in Dynamic Environments for Search and Rescue 12

Swarm-Based Path Creation in Dynamic Environments for Search...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Richard, William K. Majercik, Stephen M. BBN Technol 10 Moulton St Cambridge MA 02138 USA Bowdoin Coll Brunswick ME 04011 USA

No abstract available.

ISBN: (纸本)9781450311786

No abstract available.

关键词： swarm intelligence algorithms search and rescue

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swarm Robotics for Autonomous Aerial Robots: Features, algorithms, Control Techniques, and Challenges 4

Swarm Robotics for Autonomous Aerial Robots: Features, Algor...

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4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)

作者： Alqudsi, Yunes Makaraci, Murat Kocaeli Univ Fac Aeronaut & Astronaut Dept Aerosp Engn Kocaeli Turkiye Kocaeli Univ Dept Mech Engn Fac Engn Kocaeli Turkiye Taiz Univ Fac Engn IT Mech & Robot Engn Dept Taizi Yemen

ISBN: (纸本)9798350354140;9798350354133

With the aim of revolutionizing various industries, the field of swarm Robotics (SRs) continuously offers efficient solutions to complex tasks. This paper provides a comprehensive review of recent advancements and emerging trends in SR, with a specific focus on the coordination and control of swarm Aerial Robots (SARs). It examines the key features and characteristics of SR, its integration with swarm intelligence, and fundamental algorithms such as Ant Colony Optimization (ACO), Particle swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE). The paper also discusses various swarm control techniques, infrastructure requirements, and challenges associated with implementing SR in Aerial Robots. Additionally, the paper outlines future research directions, emphasizing the need for robust, energy-efficient, adaptive algorithms and ethical considerations in deploying autonomous Aerial robot swarms. This study aims to provide a solid foundation for researchers and practitioners, paving the way for future advancements and interdisciplinary collaboration in this promising field.

关键词： Autonomous Robot swarms swarm intelligence algorithms Robotic Coordination Challenges swarm Control Techniques Multi-Robot Systems

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A New Voting Approach Based on False Candidates 25

A New Voting Approach Based on False Candidates

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25th International-Business-Information-Management-Association Conference

作者： Mogos, Andrei-Horia Mogos, Bianca Florea, Adina Magda Univ Politehn Bucuresti Bucharest Romania Univ Bucharest Bucharest Romania

ISBN: (纸本)9780986041945

In the last decades, social choice theory had a significant impact over social sciences, political sciences, and economic sciences. Recently, a new research area, computational social choice, which brings together social choice theory and computer science, gained an important popularity. An important topic in social choice theory is the theory of voting which has a lot of applications in computer science. In this paper we propose a new voting approach based on false candidates: first, we define a new type of voting scheme (i.e. the preferences of the voters over the set of candidates) called Q - voting scheme and then we propose variants for Borda Count, Condorcet and Black's Rule voting methods that can be used for Q - voting schemes. We also introduce a formal approach and propose and prove three theorems related to the analyzed subject. In addition, we use Q - voting schemes for comparing three swarm intelligence algorithms;during the comparison process we apply an algorithm, also proposed in this paper, which automatically generates Q - voting schemes.

关键词： voting methods false candidates Borda Count Condorcet Black's Rule swarm intelligence algorithms

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Grid Ant Colony Optimization Applied to a Multi-Robotic Garbage Collection System 2

Grid Ant Colony Optimization Applied to a Multi-Robotic Garb...

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2nd Brazilian Robotics Symposium (SBR) / 11th Latin American Robotics Symposium (LARS) / 6th Robocontrol Workshop on Applied Robotics and Automation

作者： Sales, Daniel O. Dias, Mauricio A. Osorio, Fernando S. Univ Sao Paulo Inst Math Sci & Computat ICMC Mobile Robot Lab LRM Sao Carlos SP Brazil

ISBN: (纸本)9781479967117

Garbage recycling and collection problem is an interesting problem that researchers are applying swarm intelligence algorithms to solve. Some previous approaches used particle swarm optimization, immune systems and ant colony optimization algorithms and achieved good results. Ant colony optimization is a well-known swarm intelligence algorithm that is normally used to solve computational problems which can be reduced to finding good paths in graphs. A multi-robotic system can be applied to solve this problem but it will need a control algorithm to accomplish the task. Applying the regular ant colony optimization algorithm to control the multi-robotic system is not a trivial task due to the graph representation needed. This work proposes modifications in the ant colony optimization algorithm that uses grid representation and applies the modified algorithm to solve this problem. The results showed a decrease of one order of magnitude in the number of iterations needed to solve the problem compared to the previous version of the algorithm. Considering the results the proposed algorithm showed to be able to control a multi-robotic system for the chosen problem.

关键词： ant colony optimisation mobile robots multi-robot systems refuse disposal ant colony optimization algorithms computational problems garbage collection problem garbage recycling grid ant colony optimization immune systems multirobotic garbage collection system multirobotic system particle swarm optimization swarm intelligence algorithms Algorithm design and analysis Ant colony optimization Control systems Heuristic algorithms Particle swarm optimization Recycling Robots ACO Evolvable Systems Grid ACO Multi-Robotics Robotics Particle swarm optimization Ant colony optimization Garbage disposal Algorithm design and analysis multi-robot systems Immune System Heuristic algorithms ant algorithm control systems Mobile robots ADMINISTRATIVE CONSENT ORDER Robot robotics Recycling Graph representations

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Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization 1

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17th International Conference on Hybrid Intelligent Systems (HIS)

作者： Strumberger, Ivana Bacanin, Nebojsa Tuba, Milan Singidunum Univ Fac Informat & Comp 32 Danijelova St Belgrade 11000 Serbia State Univ Novi Pazar Dept Tech Sci Vuka Karadzica Bb 36300 Novi Pazar Serbia

ISBN: (数字)9783319763514

ISBN: (纸本)9783319763514;9783319763507

This paper introduces hybridized elephant herding optimization algorithm (EHO) adopted for solving constrained optimization problems. EHO is one of the latest swarm intelligence metaheuristic and the implementation of the EHO for constrained optimization was not found in literature. In order to evaluate the performance of the hybridized EHO algorithm, we conducted tests on 13 standard constrained benchmark functions. To prove efficiency and robustness of the hybridized EHO, a comparative analysis with basic EHO implementation, as well as with other state-of-the-art algorithms, such as firefly algorithm, seeker optimization algorithm and self-adaptive penalty function genetic algorithm was performed. Experiments show that the hybridized EHO on average outperforms other algorithms used in comparative analysis.

关键词： Elephant herding optimization swarm intelligence algorithms Metaheuristics Constrained optimization problems

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