Heuristic optimization algorithms which are inspired by nature have become very popular for solving real world problems recently. The use of these algorithms increases day by day in the literature because of their flexible structures and non-containing confusing mathematical terms. One of these algorithms is Bat Algorithm (BA). BA is a heuristic algorithm based on echolocation characteristic of bats and developed by the mimics of bats' foraging behaviour. In this study exploration mechanism of the algorithm is improved by modifying the equation of pulse emission rate and loudness of bats. The performance of Modified Bat Algorithm (MBA) is verified by 15 benchmark functions and the results were exhibited as comparative. The results of MBA are superior in terms of solution quality on optimization problems compared to BA.

Antenna array design techniques are focused on two main classes: uniformly spaced antenna arrays and the non-uniform spacing case. These include techniques based on mathematical programming, such as constrained programming and non-linear programming. More recently, meta-heuristics approaches have been successful at designing antenna arrays [5]. In this work, this paper presents efficient methods of genetic algorithm (GA), memetic algorithm (MA) and tabu search algorithm (TSA) for the synthesis of linear antenna design. We present three examples of antenna array design to compare the efficiency of the algorithms through simple design to complex design. The GA, TSA and MA has been used to optimize the spacings between the elements of the linear array to produce a radiation pattern with minimum SLL and null placement control.

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In this work, the signal and noise behaviors of a microwave transistor within its operation domain (CT,V DS , IDS , f ) are modeled by the Artificial Neural Network (ANN) and Fuzzy Logic System (FLS) without using any information on the microwave circuit theory . A worked example is presented where the same data is employed for both models selected from the manufacturer's data sheets. Performances of the FLS and ANN models are compared and conclusions are drawn.

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