Design of small broadband, multiband and high-directivity microstrip patch antennas (MPAs) is a challenging task for the antenna research community since the classical MPAs do not perform well enough to be used in the real world applications. In this sense, various performance improvement techniques such as stacked patches, air gaps, compact meandering geometries, fractal-shapes and shorting pins are applied to design improved MPAs. Use of bio-inspired algorithms along with these techniques is trending due to their capability of manipulating antenna parameters to achieve optimized performance. Literature presents use of bio-inspired algorithms such as Genetic algorithms (GA), Particle swarm optimization (PSO), Differential evolution (DE), Invasive weed optimization (IWO), Wind driven optimization (WDO)  and Ant colony optimization (ACO) on MPAs for performance enhancement. This paper begins with an introduction to MPAs followed by an analysis of the performance improvement techniques. Evolution of bio-inspired algorithms and their applications in the field of MPAs are also presented. Based on the compilation of studies, importance of applying multi-objective bio-inspired algorithms for simultaneous optimization of multiple antenna parameters is emphasized. Further, research voids in the field are revealed and direction is shown to design compact multifunctional MPAs.

Bio-Inspired Algorithms Applied on Microstrip Patch Antennas: a Review

The ultrasonic machining (USM) process has been analyzed in the present study to obtain the desired process responses by optimizing machining parameters using cuckoo search (CS) and chicken swarm optimization (CSO), two powerful nature-inspired, population and swarm-intelligence-based metaheuristic algorithms. The CS and CSO algorithms have been compared with other non-conventional optimization techniques in terms of optimal results, convergence, accuracy, and computational time. It is found that CS and CSO algorithms predict superior single and multi-objective optimization results than gravitational search algorithms (GSAs), genetic algorithms (GAs), particle swarm optimization (PSO) algorithms, ant colony optimization (ACO) algorithms and artificial bee colony (ABC) algorithms, and gives exactly the same results as predicted by the fireworks algorithm (FWA). The CS algorithm outperforms all other algorithms namely CSO, FWA, GSA, GA, PSO, ACO, and ABC algorithms in terms of mean computational time, whereas, the CSO algorithm outperforms all other algorithms except for the CS and GSA algorithms.

Ultrasonic Machining Process Optimization by Cuckoo Search and Chicken Swarm Optimization Algorithms

A comparative study of the use of bio-inspired optimization technologies including the Cuckoo Search (CS) algorithm, the Differential Evolution (DE) algorithm, and Quantum-behaved Particle Swarm Optimization (QPSO) in the design of microstrip patch antennas for use in RF energy harvesting systems is presented. Radio frequency (RF) energy harvesting is considered as an eco-friendly energy source and has become a focus of intense research especially for use in distributed sensor networks. In a RF energy harvesting system, the antenna is responsible for capturing RF signals over a certain frequency band, and it is a vital element in determining the performance of the RF energy harvester. In this paper, a new mathematical weighted evaluation model involving antenna efficiency, center frequency, and bandwidth is proposed to evaluate the performance of a rectangular microstrip patch antenna (RMPA) for a RF harvesting system based on both the transmission-line model and the cavity model. With the evaluation model as the objective function, bio-inspired optimization approaches are utilized to determine the geometrical parameters of the optimal antenna based on given constraints. Moreover, the optimised designs of an antenna for harvesting energy from the Global System for Mobile Communications (GSM) frequency band are proposed via the mathematical model and bio-inspired optimization approaches using simulations. Furthermore, a comparative study of the DE, CS, and QPSO techniques is conducted via the evaluation of the properties of the antenna designs.

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Bio-inspired optimization algorithms applied to rectenna design

In this article we present a general review of nature-inspired metaheuristic algorithms with implementations in patch antenna designs. The primary focus is on Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO). New algorithms recently been introduced also are analyzed, such as: Firefly algorithm (FA), Cuckoo Search (CS), Bat algorithm (BA), Social Spider Optimization (SSO) and Spider Monkey Optimization (SMO). Of each algorithm, a summary of significant examples and a flowchart for a quick and easy interpretation are presented. Finally, the common characteristics of algorithms are compared, concluding in a hierarchical classification according to the efficiency of each one to solve the patch antenna problems.

Optimization designs in patch antennas using nature-inspired metaheuristic algorithms: A review

The wire-length of vertically stacked ICs plays a vital role. The wire-length is minimized by using differential evolutionary algorithms withIBM Benchmark inputs. Moreover this wire length is minimized with the respect to the length of the Through Silicon via (TSVs). As a result, the wire-length has been minimized using this algorithm with various parameters. Experimental result shows that the total wirelength can be reduced.

Minimizationof wirelength in 3d IC routing by using differential evolution algorithm

Cuckoo search is one of the recent metaheuristic optimization algorithm which was first developed by Xin-She Yang and Saush Deb in 2009. This paper is based on Cuckoo Search or Cuckoo Algorithm- an algorithm based on the destructive reproductive strategy of cuckoos. We can use this algorithm to solve various optimization problems in various domains like design of springs and welded beam structures. This is aimed to understand the breeding behavior of cuckoo. The solutions that we got by CS are far better than the solutions we obtained from swarm optimization.

http://ieeexplore.ieee.org/iel7/7332173/7344420/07344522.pdf

Cuckoo search algorithm for speech recognition

Microstrip line feed Microstrip Antenna (MSA) geometrical design parameters are Patch Width, Inset Feed Position and Patch length. Cuckoo Search algorithm (CS) is a recent optimization algorithm creating its application in the electromagnetic community for evolving the design parameters of microstrip antenna. Cuckoo Search algorithm (CS) has been employed in this paper to calculate the design parameters of the microstrip line feed microstrip antenna for a reaspective target frequencies and return loss. Same investigation is made for different microwave frequencies (1–18 GHz) and at dielectric constant 2.4. Simulations have been done using IE3D software. The results suggests Cuckoo search algorithm outperforms Differential Evolution (DE)in terms of quality of solution and convergence speed.

Comparative study of Cuckoo search optimization and differential evolution algorithm for design of microstrip fed microstrip patch antenna

Global routing in VLSI (very large scale integration) design is one of the most challenging discrete optimization problems in computational theory. In this review paper of ours, we have presented a polynomial time algorithm for the global routing problem based on integer programming formulation with a theoretical approximation bound. The algorithm ensures that all routing demands are satisfied concurrently, and the overall cost is approximately minimized. Both serial and parallel implementation has been provided to develop several heuristics used to improve the quality of the solution and reduce running time. We provide computational results on two sets of well-known benchmarks and show that, with a certain set of heuristics, our new algorithms perform extremely well compared with other integer-programming models.

Ayoshna Saha

Papers

Cuckoo search algorithm for speech recognition

Comparative study of Cuckoo search optimization and differential evolution algorithm for design of microstrip fed microstrip patch antenna

An overview of global routing