Reza Gholami

Competition over Resources: A New Optimization Algorithm Based on Animals Behavioral Ecology

In the recent years, many heuristic optimization algorithms have been developed. A majority of these heuristic algorithms have been derived from the behavior of biological or physical systems in nature. In this paper, we propose a new optimization algorithm based on competitive behavior of animal groups. In the proposed algorithm, the whole population is divided into a number of groups. In each group, the best searching agent spreads its children in its owned territory. Any group which is not able to find rich resources will be eliminated form competition. The competition gradually results in an increase in population of wealthy group which gives a fast convergence to proposed optimization algorithm. In the following, after a detailed explanation of the algorithm and pseudo code, we compare it to other existing algorithms, including genetics and particle swarm optimizations. Applying the proposed algorithm on various benchmark cost functions, shows faster and superior results compared to other optimization algorithms.

Linear antenna array design for UWB radar

This paper presents a compact microstrip-fed ultra-wideband (UWB) antenna and its four-element array design for target detection applications. A prototype was fabricated and then measured based on optimal parameters. There is good consistency between the simulated S11 and the measured one. In addition, a 1 × 4 linear array design with the size of 100 mm × 34 mm has been proposed to achieve a higher gain. Simulation shows that the array gain is increased about 6 dBi in comparison to the single element through the whole UWB frequency range of 3.1 GHz to 10.6 GHz. The proposed array has an average of -15 dB side lobe level (SLL) in the mentioned range. And also, a -23 dB SLL has been achieved by applying Dolph-Chebyshev amplitude distribution at 6 GHz. Simulation results confirm that the antenna exhibits a constant bidirectional radiation pattern with a high and flat gain in case of the array design.

Synthesis of aperiodic linear antenna arrays based on competition over resources optimization

In the recent years, many heuristic optimization algorithms derived from the behavior of biological or physical systems in nature have been developed. In this paper, we propose a new optimization algorithm based on competitive behavior of group animals. The competition gradually results in an increase in population of wealthy group which gives a fast convergence to the optimization algorithm. In the following, after a detailed explanation of the algorithm and pseudo code, we compare it with particle swarm optimizations as a famous heuristic algorithm. The proposed method is used to determine an optimal set of amplitude weights of antennas and element spacing that satisfy the optimal goal during constant first null beamwidth (FNBW) for aperiodic linear array. It is to be noted that the desired prescribed nulls depth and side lobes level is achieved simultaneously with the narrowest possible FNBW. The proposed algorithm on known array antenna synthesis, shows faster and superior results compared to other optimization algorithms.

Driving point impedance restriction in synthesis of linear antenna arrays using competition over resources optimization algorithm

In the recent years, many heuristic optimization algorithms derived from the behavior of biological or physical systems in nature have been developed. In this paper, we propose a new optimization algorithm based on competitive behavior of group animals. The competition gradually results in an increase in population of wealthy group which gives a fast convergence to the optimization algorithm. In the following, after a detailed explanation of the algorithm and pseudo code, we compare it with particle swarm optimizations as a famous heuristic algorithm. The proposed method is used to determine an optimal set of amplitude weights of antennas and element spacing that satisfy the optimal goal during constant first null beamwidth (FNBW) for aperiodic linear array. It is to be noted that the desired prescribed nulls depth and side lobes level (SLL) is achieved simultaneously with the narrowest possible FNBW. In addition, because of practical consideration in array antennas, restriction of driving-point impedance (DPI) of each element is proposed during pattern synthesis. Final array factor without and with impedance optimization are compared with uniform amplitude excitation and equal element spacing array factor as reference. The proposed algorithm on known array antenna synthesis, shows faster and superior results compared to other optimization algorithms.

Competition over resources algorithm and its application for planar array pattern synthesis

This paper will investigate a method of synthesizing planar arrays through the application of a new optimization algorithm based on competitive behavior of animal groups that will impose deeper nulls in the interfering direction with the constraint of a reduced Side Lobe Level (SLL). Simulation results for optimal patterns by position-only and both the element space and amplitude controlling with the imposed single and double nulls are given to show the effectiveness of the proposed method. In the following, after a general explanation of the algorithm, a comparison of obtained results with particle swarm optimizations, as a well-known heuristic algorithm, is presented. Furthermore, in order to set which design case could provide a better performance in imposed deeper nulls and side lobe reduction, a comparative evaluation of both proposed methods (position-only, position and amplitude) will be done. Finally, the optimized design case with a uniform planar array will be achieved. Also, the proposed algorithm on array antenna synthesis, shows faster and superior results compared to other optimization algorithms.