The Design of a Uniplanar Printed Triple Band-Rejected UWB Antenna using Particle Swarm Optimization and the Firefly Algorithm

YesA compact planar monopole antenna is proposed for ultra-wideband applications. The antenna has a microstrip line feed and band-rejected characteristics and consists of a ring patch and partial ground plane with a defective ground structure of rectangular shape. An annular strip is etched above the radiating element and two slots, one C-shaped and one arc-shaped, are embedded in the radiating patch. The proposed antenna has been optimized using bio-inspired algorithms, namely Particle Swarm Optimization and the Firefly Algorithm, based on a new software algorithm (Antenna Optimizer). Multi-objective optimization achieves rejection bands at 3.3 to 3.7 GHz for WiMAX, 5.15 to 5.825 GHz for the 802.11a WLAN system or HIPERLAN/2, and 7.25 to 7.745 GHz for C-band satellite communication systems. Validated results show wideband performance from 2.7 to 10.6 GHz with S11 ˂ -10 dB. The antenna has compact dimensions of 28 × 30 mm2. The radiation pattern is comparatively stable across the operating band with a relatively stable gain except in the notched bands.This work was supported in part by the United Kingdom Engineering and Physical Science Research Council (EPSRC) under Grant EP/E022936A, TSB UK under grant application KTP008734 and the Iraqi Ministry of Higher Education and Scientific Research

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Hybridizing Invasive Weed Optimization with Firefly Algorithm for Unconstrained and Constrained Optimization Problems

This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm for global optimization problems. Unconstrained and constrained optimization problems with continuous design variables are used to illustrate the effectiveness and robustness of the proposed algorithm. The firefly algorithm (FA is effective in local search, but can easily get trapped in local optima. The invasive weed optimization (IWO) algorithm, on the other hand, is effective in accurate global search, but not in local search. Therefore, the idea of hybridization between IWO and FA is to achieve a more robust optimization technique, especially to compensate for the deficiencies of the individual algorithms. In the proposed algorithm, the firefly method is embedded into IWO to enhance the local search capability of IWO algorithm that already has very good exploration capability. The performance of the proposed method is assessed with four well-known unconstrained problems and four practical constrained problems. Comparative assessments of performance of the proposed algorithm with the original FA and IWO are carried out on the unconstrained problems and with several other hybrid methods reported in the literature on the practical constrained problems, to illustrate its effectiveness. Simulation results show that the proposed HIWFO algorithm h as superior searching quality and robustness than the approaches considered

Adaptive bio-inspired firefly and invasive weed algorithms for global optimisation with application to engineering problems

The focus of the research is to investigate and develop enhanced version of swarm intelligence firefly algorithm and ecology-based invasive weed algorithm to solve global optimisation problems and apply to practical engineering problems. The work presents two adaptive variants of firefly algorithm by introducing spread factor mechanism that exploits the fitness intensity during the search process. The spread factor mechanism is proposed to enhance the adaptive parameter terms of the firefly algorithm. The adaptive algorithms are formulated to avoid premature convergence and better optimum solution value. Two new adaptive variants of invasive weed algorithm are also developed seed spread factor mechanism introduced in the dispersal process of the algorithm. The working principles and structure of the adaptive firefly and invasive weed algorithms are described and discussed. Hybrid invasive weed-firefly algorithm and hybrid invasive weed-firefly algorithm with spread factor mechanism are also proposed. The new hybridization algorithms are developed by retaining their individual advantages to help overcome the shortcomings of the original algorithms. The performances of the proposed algorithms are investigated and assessed in single-objective, constrained and multi-objective optimisation problems. Well known benchmark functions as well as current CEC 2006 and CEC 2014 test functions are used in this research. A selection of performance measurement tools is also used to evaluate performances of the algorithms. The algorithms are further tested with practical engineering design problems and in modelling and control of dynamic systems. The systems considered comprise a twin rotor system, a single-link flexible manipulator system and assistive exoskeletons for upper and lower extremities. The performance results are evaluated in comparison to the original firefly and invasive weed algorithms. It is demonstrated that the proposed approaches are superior over the individual algorithms in terms of efficiency, convergence speed and quality of the optimal solution achieved