Analysis for optimal pattern synthesis of time modulated concentric circular antenna array using memetic firefly algorithm

The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.

Optimum PID controller design using real coded genetic algorithm (RGA) and particle swarm optimization (PSO) is addressed in this paper. The lower and upper bound of the design variables (KP, TI and TD) are selected in an intelligent manner around the values obtained from Ziegler-Nichols method. PID controller was designed by minimizing various performance measures such as ISE, IAE and ITAE for two different linear systems. The performance of RGA and PSO is compared with respect to time-response specifications, computation time and statistical performance in 20 independent trials. The simulation reveals that the performance of PSO and RGA with simulated binary crossover (SBX) produced almost same time-domain performances and take same computation time. Also, it is found that PSO converges with less number of functional evaluations, consistent for simple systems and in general ITAE is preferable for quick settling time.

In this paper, a system for feature selection based on firefly algorithm (FFA) optimization is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might reduce the classification accuracy because of the large search space. The main goal of attribute reduction is to choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy from using all attributes. A system for feature selection is proposed in this paper using a modified version of the firefly algorithm (FFA) optimization. The modified FFA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. FFA is a new evolutionary computation technique, inspired by the flash lighting process of fireflies. The FFA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on eighteen data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

Flexible Alternating Current Transmission Systems (FACTS) got in the recent years a well known term for higher controllability in power systems by means of power electronic devices. FACTS devices can effectively control the load flow distribution, improve the usage of existing system installations by increasing transmission capability, compensate reactive power, improve power quality, and improve stabilities of the power network. However, the location and settings of these devices in the system plays a significant role to achieve such benefits. This work presents the application of Real Coded Genetic Algorithm (RGA) and Particle Swarm Optimisation (PSO) for finding out the optimal locations, and the optimal parameter settings of multi type FACTS devices to achieve maximum system loadability (MSL) in the power system. The FACTS devices used are Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC). The reactance model of TCSC and the decoupled model of UPFC are considered in this work. The thermal limits of the line and voltage limits of the buses are taken as constraints during the optimization. Simulated Binary Crossover (SBX) and Non-uniform polynomial mutation are employed to improve the performance of the Genetic Algorithm used. Simulations are performed on IEEE 6 bus and 30 bus power systems. The obtained results are encouraging and show the effectiveness of RGA over PSO algorithm

Evolutionary algorithms based synthesis of low sidelobe hexagonal arrays

In this paper, various evolutionary optimization based algorithms like real coded genetic algorithm (RGA), conventional particle swarm optimization (PSO), a proposed craziness particle swarm optimization (CRPSO) have been applied for the optimal design of hyper beamforming of linear antenna array. Hyper beam is derived from sum and difference beam patterns each raised to the power of the hyper beam exponent parameter for the array. CRPSO uses new definition for the velocity vector. The simulation results show CRPSO outperforms RGA and PSO in the optimal hyper beamforming by achieving much greater reduction in sidelobe level (SLL) and much more improved first null beam width (FNBW) keeping the same value of hyper beam exponent. The optimized hyper beam is achieved by optimization of current excitation weights and uniform inter-element spacing. The approach is illustrated through 10-, 14-, and 20-element linear antenna arrays.

In this paper the maximum sidelobe level (SLL) reductions without and with central element feeding in various designs of three-ring concentric circular antenna arrays (CCAA) are examined using a novel particle swarm optimization algorithm (NPSO) to finally determine the global optimal CCAA design. Real coded Genetic Algorithm (RGA) is also employed for comparative optimization but it proves to be suboptimal. The present paper assumes non-uniform excitations and uniform spacing of excitation elements in each three-ring CCAA design. Among the various CCAA designs, the three-ring CCAA containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design with global minimum SLL (-19.69dB) determined by NPSO.

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorders affected to female in their reproductive cycle. PCO (Polycystic Ovaries) describes ovaries that contain many small cysts/follicles. This paper proposes an image clustering approach for follicles segmentation using Particle Swarm Optimization (PSO) with a new modified non-parametric fitness function. The new modified fitness function use Mean Structural Similarity Index (MSSIM) and Normalized Mean Square Error (NMSE) to produce more compact and convergent cluster. The proposed fitness function is compared to a non-parametric fitness function proposed by previous research. Experimental results show that the proposed PSO fitness function produce more convergent solution than previous fitness function especially on ultrasound images. This paper also investigates the influence of contrast enhancement to the performance of PSO image clustering and the extracted follicular size. The experimental result shows that PSO image clustering which preceded by contrast enhancement produce larger intra-cluster distance, intra-cluster distance and quantization error than PSO image clustering which not preceded by contrast enhancement. PSO with contrast enhancement produce closer Region of Interest (ROI) toward to the reference ROI which manually identified by doctor.

In this paper, a steerable isotropic circular array antenna is designed to reduce the side lobe level (SLL), using an evolutionary optimization technique. Particle swarm optimization and the firefly algorithm are used to reduce the SLL, as well as to steer the main beam in a specific direction. In this steerable circular array design, the amplitude excitations are optimized. The results show that the maximum SLL peak of the resultant patterns is as per requirements. Good performance is achieved in the array factor response and suppression of SLL for different numbers of array elements with different main beam steering angles.

Hybrid firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block

Optimizing integrated energy systems using a hybrid approach blending grey wolf optimization with local search heuristics

This paper investigates the use of a Hybrid Energy Storage System (HESS) that contains a battery and fuel cell system with a microgrid (MG) to perform multiple functions such as supply-demand matching and energy arbitrage. The main objective of the proposed study is to obtain the optimal size of the MG system which minimizes the total costs and the total emissions. The problem is formulated as a nonlinear optimization problem with different constraints and solved using Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm. To validate the effectiveness of the HFPSO, the HFPSO is compared with the Particle Swarm Optimization (PSO) algorithm and the Firefly algorithm (FA). The result of the comparison proves the effectiveness of the HFPSO, and this is considered the main reason for using the HFPSO to solve the proposed difficult optimization problem. The problem is implemented using MATLAB software. The policy of the MG system is illustrated, and the results and the curves are discussed to validate this policy.

In this study, solar cell parameters, the most basic material of solar energy that has become increasingly widespread in recent years, are determined by Particle Swarm Optimization and Firefly and algorithms. The previously proposed single-diode solar cell model was used as a base for solar cells used in series connection and then in parallel connection with each other. Integral Absolute Error (IAE) was chosen as error criterion in heuristic algorithm for the parameters of a single diode solar cell. With this work, the parameters of the solar cell are brought closer to the reality, which is important because it will change the maximum power and efficiency to be drawn from the panes by designing a more accurate controller. In this study, it has been seen that the firefly algorithm gives more successful results when firefly and particle swarm optimization algorithms compared to the previous studies.

Camera calibration is an essential issue in many computer vision tasks in which quantitative information of a scene is to be derived from its images. It is concerned with the determination of a set of parameters from the given images. In literature, it has been modeled as a nonlinear global optimization problem and has been solved using various optimization techniques. In this article, a recently developed variant of a very popular global optimization technique—the particle swarm optimization (PSO) algorithm—has been used for solving this problem for a stereo camera system modeled by pin-hole camera model. Extensive experiments have been performed on synthetic data to test the applicability of the technique to this problem. The simulation results, which have been compared with those obtained by a real coded genetic algorithm (RCGA) in literature, show that the proposed PSO performs a bit better than RCGA in terms of computational effort.