Modified Fruit Fly Optimization Algorithm for Analysis of Large Antenna Array

Abstract

This research paper deals with the optimization of a large antenna array for maximum directivity using a modified fruit fly optimization algorithm (MFOA) with random search of two groups of swarm and adaptive fruit fly swarm population size. The MFOA is utilized to determine three nonlinear mathematical test functions, analysis of the optimal number of elements and optimal element spacing of the large antenna array, and analysis of nonuniform amplitude of antenna array. The numerical results demonstrate that the MFOA is effective in solving all test function and electromagnetic problems. The advantages of the proposed algorithm are ease of implementation, large search range, less processing time, and reduced memory requirement.

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

“…Several methods are proposed for the formation of user pairs such as round robin [28]. As these approaches tend to require significantly higher computational loads when the number of users increases, new computationally lower schemes have been proposed in literature to determine the user pairs such as the heuristic models which are inspired by artificial intelligence approaches [29][30][31][32][33][34] which include drosophila optimization algorithm [35], particle swarm optimization algorithm [36][37][38], firefly optimization algorithm [39], dolphin echolocation algorithm [40], genetic algorithm [20,41] and ant-colony optimization algorithm [19,42]. These models have the ability to solve problems in varying fields such as, but not limited to, transportation, signal processing, image processing and biomedical engineering.…”

MFOA-integrated modified inverted F-based adaptive array Antenna for 2.4 GHz band applications

“…It can be searched in the larger space than the FOA and does not converge to the local optimum solution. Figures a and b compare the food‐seeking iterative processes of a fruit fly swarm of the conventional and modified algorithmic schemes .…”

“…Typically, EM problems are characterized by large search space, complexity and discontinuous behavior, the characteristics which contribute to the premature convergence (i.e., suboptimal solution). To tackle the suboptimization issue, this current research has adopted the MFOA , in which the population is equally divided into two groups and assigned two separate tasks. In the optimization, the first group initially carries out the search in a large search area for specific locations with possible optimal solutions and then the second group is deployed in those specific locations.…”

“…In Refs. , the conventional fruit fly optimization algorithm (FOA) was applied to electromagnetic (EM) problems. In addition, several other optimization algorithms were proposed for EM applications , but none have thus far been implemented on the AAA.…”

Antenna Design by Means of the Fruit Fly Optimization Algorithm

“…This algorithm was created to study financial distress models [15] and it has also been successfully applied to other problems [16,17]. Nevertheless, the application of this algorithm to antenna design optimization has been very limited; few examples exist in the literature [18,19] and all of them are dedicated to the synthesis of array factor analytical functions.…”