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

Adjusted Artificial Bee Colony Algorithm for the Minimum Wei...

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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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A New Improved Multiple Behaviors Whale Optimization Algorithm 7

A New Improved Multiple Behaviors Whale Optimization Algorit...

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7th International Conference on Natural Language Processing and Information Retrieval (NLPIR)

作者： Zhang, Yang Kanagawa Univ Dept Comp Sci Yokohama Kanagawa Japan

ISBN: (纸本)9798400709227

The Whale Optimization Algorithm (WOA) has demonstrated superior performance compared with other swarm intelligence algorithms, making it widely used and applied to practical problemsolving. In this paper, an enhanced WOA is proposed, referred to as the Improved Whale Optimization Algorithm (IWOA). The convergence factor of the traditional WOA is replaced with a random trigonometric function, which considers both the historical and current best positions of the whale population during the bubble-encircling search phase, thereby accelerating the global search speed of the whale population. In the random-encircling search phase, a combination of equiprobable random selection and roulette wheel selection is used to select random positions. Additionally, a random perturbation of individuals is introduced in the spiral search phase to prevent premature convergence to local optima. The effectiveness and robustness of the IWOA are evaluated using 4 benchmark function problems, and the results are compared with three other algorithms including particle swarm optimization (PSO). The experimental results demonstrate that the proposed IWOA outperforms other algorithms in terms of convergence speed and accuracy.

关键词： Optimization Problem swarm intelligence algorithms Benchmark Function Problems Whale Optimization Algorithm

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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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Chaotic Adaptive Fireworks Algorithm

Chaotic Adaptive Fireworks Algorithm

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7th International Conference on swarm intelligence (ICSI)

作者： Gong, Chibing Guangdong Tech Coll Water Resource & Elect Engn Dept Comp Informat Engn Guangzhou 510635 Guangdong Peoples R China

ISBN: (纸本)9783319410005;9783319409993

Fireworks algorithm (FWA) is considered a novel algorithm that reacts the fireworks explosion process. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes in regards to enhancing performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add chaos to the AFWA with the goal of boosting performance and achieving global optimization. The parameter. is tuned using ten chaotic maps, and twelve benchmark functions will be tested in regards to chaotic adaptive fireworks algorithm (CAFWA). The final results conclude the CAFWA is able to outperform the FWA, EFWA, and AFWA. Additionally, the CAFWA is compared with the bat algorithm (BA), standard particle swarm optimization 2011 (SPSO2011), harmony search (HS), and firefly algorithm (FA). The research results indicated that the highest performance presented itself when CAFWA is used with Circle maps.

关键词： Chaotic maps Fireworks algorithm swarm intelligence algorithms

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Multi-objective Synthesis of Antennas from Special and Conventional Materials

Multi-objective Synthesis of Antennas from Special and Conve...

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作者： Raida, Zbyněk Brno University of Technology

In the paper, we try to provide a comprehensive look on a multi-objective design of radiating, guiding and reflecting structures fabricated both from special materials (semiconductors, high-impedance surfaces) and conventional ones (microwave substrates, fully metallic antennas). Discussions are devoted to the proper selection of the numerical solver used for evaluating partial objectives, to the selection of the domain of analysis, to the proper formulation of the multi-objective function and to the way of computing the Pareto front of optimal solutions (here, we exploit swarm-intelligence algorithms, evolutionary methods and self-organizing migrating algorithms). The above-described approaches are applied to the design of selected types of microwave antennas, transmission lines and reflectors. Considering obtained results, the paper is concluded by generalizing remarks.

关键词： Semiconductor antennas conical antennas planar antennas fractal antennas multi-objective optimization evolutionary algorithms swarm intelligence algorithms self-organizing migrating algorithms antenna measurements

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R-GWO: Representative-based grey wolf optimizer for solving engineering problems

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APPLIED SOFT COMPUTING 2021年 106卷 107328-107328页

作者： Banaie-Dezfouli, Mahdis Nadimi-Shahraki, Mohammad H. Beheshti, Zahra Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

The grey wolf optimizer (GWO) is a well-known nature-inspired algorithm, which shows a sufficient performance for solving various optimization problems. However, it suffers from low exploration and population diversity because its optimization process is only based on the best three wolves greedily, and the information of other wolves does not consider. In this paper, a representative-based grey wolf optimizer (R-GWO) is proposed to tackle with these weaknesses of the GWO. The R-GWO introduces a search strategy named representative-based hunting (RH) a combination of three effective trial vectors inspired by alpha wolves' behaviors to improve the exploration and diversity of the population. The RH search strategy utilizes a representative archive to reduces the greediness and enhance the diversity of solutions, and it can also strike balance between the exploration and exploitation using a nonlinear control parameter. The performance and applicability of the proposed R-GWO were evaluated on CEC 2018 benchmark functions and six engineering design problems. The results were compared by eight state-of-the-art metaheuristic algorithms: PSO, KH, GWO, WOA, EEGWO, BOA, HHO, and HGSO. Moreover, the results were statistically analyzed by three test Wilcoxon rank-sum, Friedman and mean absolute error (MAE). The performance results show that on all 29 functions with dimensions 30, 50, and 100, the R-GWO is superior to the competitor algorithms except on function 27 on all dimensions and function 22 on dimension 30. The proposed R-GWO is the most effective algorithm compared with competitor algorithms, with an overall effectiveness of 95.4%. The experimental and statistical results show that the R-GWO is competitive and superior to compared algorithms and can solve engineering design problems better than competitor algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Optimization algorithms Metaheuristic algorithms swarm intelligence algorithms Grey wolf optimizer Engineering design problems

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Hierarchical structure-based joint operations algorithm for global optimization

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swarm AND EVOLUTIONARY COMPUTATION 2023年 79卷

作者： Sun, Gaoji Han, Rongqing Deng, Libao Li, Chunlei Yang, Guoqing Zhejiang Normal Univ Coll Econ & Management Jinhua Peoples R China Harbin Inst Technol Sch Informat Sci & Engn Weihai Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China Hebei Univ Sch Management Baoding Peoples R China

Joint operations algorithm (JOA) is a metaheuristic algorithm based on joint operations strategy in military theory, which incorporates three core operations - offensive, defensive and regroup - and has excellent performance in global optimization problems. To enhance the optimization performance of the original JOA, we re-examine the positioning of the three core operations in balancing global exploration and local exploitation, and propose a hierarchical structure-based JOA variant (abbreviated as HSJOA) by adjusting their execution mechanism. In addition, we redesign the three core operations to give full play to their synergy effect. In the new offensive operations, we simplify its specific execution strategy, but introduce an adjustment parameter to retain valuable position information. In the modified defensive operations, we integrate the influence of officer and elite comrades on soldiers to design a Gaussian distribution-based conservative defensive strategy and one Cauchy distribution-based aggressive defensive strategy. In the novel regroup operations, we replace the original random division strategy with a sorting-based division scheme. To evaluate the optimization performance of HSJOA, we conduct comparison experiments using nine excellent algorithms to deal with four real-life optimization problems and thirty test functions from IEEE CEC 2014 testbed. The comparison results show that the overall optimization performance of HSJOA is significantly better than the original JOA, the outstanding variants (TAPSO and GODE) of two well-known algorithms and four recently proposed algorithms (EO, SOA, MPA and WOA), while inferior to two winners of IEEE CEC competition (L-SHADE and EBOwithCMAR), but HSJOA clearly outperforms L-SHADE and EBOwithCMAR in terms of the runtime consumption.

关键词： Joint operations algorithm Metaheuristic algorithms swarm intelligence algorithms Evolutionary algorithms Global optimization

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Chameleon swarm Algorithm: A bio-inspired optimizer for solving engineering design problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 174卷 114685-114685页

作者： Braik, Malik Shehadeh Al Balqa Appl Univ Dept Comp Sci Al Salt Jordan

This paper presents a novel meta-heuristic algorithm named Chameleon swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360 degrees scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixtyseven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.

关键词： Chameleon swarm Algorithm Optimization techniques Meta-heuristics Nature-inspired algorithms Evolutionary algorithms swarm intelligence algorithms

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Computational Models & Methods in Systems Biology & Medicine

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IET SYSTEMS BIOLOGY 2015年第6期9卷 217-217页

作者： Ruskin, Heather J. Roznovat, Irina A.

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