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International Workshop on Antenna Technology (iWAT)

作者： Boursianis, Achilles D. Goudos, Sotirios K. Yioultsis, Traianos, V Siakavara, Katherine Rocca, Paolo Aristotle Univ Thessaloniki ELEDIA Res Ctr ELEDIA AUTH Dept Phys Thessaloniki Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece Aristotle Univ Thessaloniki Dept Phys Thessaloniki Greece Univ Trento ELEDIA UniTN DISI Trento Italy

ISBN: (纸本)9781728126401

Multiple Input Multiple Output (MIMO) antennas will be a part of every new user device in 5G networks. In this paper, we design a MIMO antenna for 5G networks using a nature-inspired algorithm, namely the Salp swarm Algorithm (SSA). We select a bow tie antenna as the element of the MEMO operating in n257 frequency band (center frequency at 28GHz). Firstly, we design and optimize the antenna element using the SSA algorithm. Secondly, a MIMO antenna is designed using the optimal dimensions of the antenna element. Numerical results demonstrate that the proposed MIMO antenna has a small size and exhibits wideband operation in the desired frequency hand.

关键词： 5G mobile communications bow tie antenna MEMO antenna salp swarm algorithm swarm intelligence algorithms

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DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

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algorithms 2021年第11期14卷 314页

作者： Nadimi-Shahraki, Mohammad H. Moeini, Ebrahim 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 Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

In this paper, a discrete moth-flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth-flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.

关键词： community detection complex network optimization metaheuristic algorithms swarm intelligence algorithms moth-flame optimization algorithm

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Optimized tuning of power oscillation damping controllers using probabilistic approach to enhance small-signal stability considering stochastic time delay

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ELECTRICAL ENGINEERING 2019年第3期101卷 969-982页

作者： Gurung, Samundra Jurado, Francisco Naetiladdanon, Sumate Sangswang, Anawach King Mongkuts Univ Technol Thonburi Dept Elect Engn Bangkok 10140 Thailand Univ Jaen Dept Elect Engn EPS Linares Jaen 23700 Spain

Communication latency which inherently occurs in wide area measurement system greatly degrades small-signal stability (SSS) and is stochastic in nature, and thus, the current power oscillation damping controllers (PODCs) designed to improve SSS must consider this crucial factor. This paper proposes a probabilistic method to tune the parameters of PODCs incorporated in renewable farms to improve SSS under stochastic time delay and under other power system uncertainties arising due to renewable energy resources and loads. The proposed method is composed of two stages: The first stage quantifies the effect of time delay and other power system uncertainties on SSS, and the second stage uses this information to formulate an optimization problem. This optimization problem is solved with the help of four swarm intelligence-based optimization algorithms which are: bat algorithm, cuckoo search algorithm, firefly algorithm, and particle swarm optimization algorithm. The solutions from all these four optimization algorithms are compared, and the best result is used to optimize the parameters of the PODCs. All the analyses were conducted on a modified IEEE 68 bus system. The results show that the PODCs tuned using the proposed method greatly enhances the SSS margin under different scenarios and are probabilistically robust to the varying time delay and other power system uncertainties.

关键词： Cumulant Probability function swarm intelligence algorithms Wide area damping controller

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A modified salp swarm algorithm for task assignment problem

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APPLIED SOFT COMPUTING 2020年 94卷 106445-106445页

作者： El-Ashmawi, Walaa H. Ali, Ahmed F. Suez Canal Univ Fac Comp&Informat Dept Comp Sci Ismailia Egypt Misr Int Univ Fac Comp Sci Cairo Egypt

The task assignment problem (TAP) is one of the standard combinatorial optimization problems (COPs) in the field of discrete optimization. TAP is known to be an NP-complete problem due to the difficulty to obtain the exact solution of it in polynomial time. Thus, it is essential to develop and/or propose an optimization algorithm to solve this problem. In TAPs, a set of tasks is assigned to a set of machines to both effectively minimize the communication and execution cost. This paper presents a modified Salp swarm Algorithm (SSA), to solve not only task assignment problems but also, fundamental combinatorial optimization problems in engineering and real-world scientific domains. The modified salp algorithm takes place in the integration with the Local Refinement Heuristic (LRH) approach to enhance a given assignment along with operators. The modified algorithm is named Modified Salp Local Refinement Heuristic (MSLRH). To the best of our knowledge, this paper is the first of its kind to attempt using the SSA in task assignment problems. The MSLRH algorithm is tested on different benchmark datasets (tree structure and general graph), including various tasks and machines for each dataset. In addition, it compared with the most known meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle swarm Optimization (PSO) algorithm, and Jaya algorithm (JAYA) to investigate the effectiveness of the MSLRH algorithm in terms of average assignment allocation cost. From tree structure dataset (e.g., 200 tasks assigned to 8 machines), the proposed MSLRH algorithm has achieved a minimum average assignment cost better than GA by 62% and better than PSO and JAYA by 42%. From the general graph dataset (e.g., 209 tasks assigned to 16 machines), the proposed MSLRH algorithm has better results than other algorithms up to 60%. From various and extensive experimental results, the proposed algorithm has proven the effectiveness in solving the task assignment problem. (C) 2020 El

关键词： Task assignment problem Salp swarm algorithm swarm intelligence algorithms Task interchange graph Local refinement heuristic

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Adjusted Artificial Bee Colony Algorithm for the Minimum Weight Triangulation 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Alihodzic, Adis Smajlovic, Haris Tuba, Eva Hrosik, Romana Capor Tuba, Milan Univ Sarajevo Fac Math Sarajevo Bosnia & Herceg Singidunum Univ Fac Informat & Comp Belgrade Serbia Univ Dubrovnik Maritime Dept Dubrovnik Croatia Singidunum Univ Fac Informat & Comp Belgrade Serbia

ISBN: (纸本)9789811307614;9789811307607

The minimum weight triangulation is a well-known NP-hard problem often used for the construction of triangulated random network models of land contours. Since it is an intractable problem, the required computational time for an exhaustive search algorithm grows exponentially with the number of points in 2D space. Nature-inspired swarm intelligence algorithms are prominent and efficient optimization techniques for solving that kind of problems. In this paper, we adjusted the artificial bee colony algorithm for the minimum weight triangulation problem. Our adjusted algorithm has been implemented and tested on several randomly generated instances of points in the plane. The performance of our proposed method was compared to the performance of other stochastic optimization algorithms, as well as with the exhaustive search for smaller instances. The simulation results show that our proposed algorithm in almost all cases outperforms other compared algorithms.

关键词： swarm intelligence algorithms Artificial bee colony ABC Minimum weight triangulation

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Digital Filter Design Using Improved Teaching-Learning-Based Optimization

Digital Filter Design Using Improved Teaching-Learning-Based...

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作者： Miao Zhang University of Windsor

学位级别：博士

Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence. The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based l

关键词： digital filters swarm intelligence algorithms

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CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

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APPLIED SOFT COMPUTING 2019年 85卷 105583-000页

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimization Metaheuristic algorithms swarm intelligence algorithms Engineering problems Conscious neighborhood

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A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets

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APPLIED SOFT COMPUTING 2018年 69卷 784-805页

作者： Li, Jinyan Fong, Simon Wong, Raymond K. Mohammed, Sabah Fiaidhi, Jinan Sung, Yunsick Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China Huawei Software Theol CO LTD Big Data PDU Nanjing Jiangsu Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2000 Australia Lakehead Univ Dept Comp Sci Thunder Bay ON Canada Dongguk Seoul Dept Multimedia Engn 30 Pildong Ro 1gil Seoul 04602 South Korea

Imbalanced datasets can be found in a number of fields;they are commonly regarded as big data because of their sheer volume and high attribute dimensions. As the name suggests, imbalanced big datasets come with an extremely imbalanced ratio between the amount of major class and minority class samples. Traditional methods: have been attempted but still cannot fully, effectively, and reliably solve the imbalanced class classification problem, especially when the distribution of the classes is exceedingly imbalanced. In this paper, we propose a collection of algorithms to solve the problem of imbalanced datasets in binary data classification. Most traditional methods: rebalance the imbalanced dataset merely by matching the data quantities of the two classes. Our proposed algorithms, which take the form of a suite of variants, focus on guaranteeing the credibility of the classification model and reaching the greatest possible accuracy by dynamically rebalancing the training dataset with multi-objective swarm intelligence optimisation. The new algorithms are extended from those we proposed earlier, which had a single objective - first find a set of solutions that satisfy the Kappa criterion, then search for the solution in the set that offers the highest accuracy. Two mam modifications are made in the new algorithms. Multi-objective optimisation is aimed at finding a solution that satisfies several criteria at the same time, such as accuracy and identifying a list of credibility indicators. The other enhancement is the incremental operation of the multi-objective optimisation. Incremental optimisation is imperative for processing data feeds that may arrive in a streaming manner. Instead of waiting for the full data archive to be available before optimisation, incremental optimisation rebalances the data feed segment by segment on the fly. The experimental results from the suite of proposed algorithms show that they can effectively attain better and more stable performanc

关键词： swarm intelligence algorithms Dynamic multi-objective Big highly imbalanced dataset Binary classification Rebalancing algorithm

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A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

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swarm AND EVOLUTIONARY COMPUTATION 2011年第1期1卷 3-18页

作者： Derrac, Joaquin Garcia, Salvador Molina, Daniel Herrera, Francisco Univ Granada CITIC UGR Res Ctr Informat & Commun Technol Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Jaen Dept Comp Sci Jaen 23071 Spain Univ Cadiz Dept Comp Engn Cadiz 11003 Spain

The interest in non parametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality, and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC'2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Statistical analysis Nonparametric statistics Pairwise comparisons Multiple comparisons Evolutionary algorithms swarm intelligence algorithms

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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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