Chaotic Adaptive Butterfly Mating Optimization and Its Applications in Synthesis and Structure Optimization of Antenna Arrays

Abstract

A novel chaotic adaptive butterfly mating optimization (CABMO) is proposed to be used in synthesizing the beam pattern. In order to improve the optimization accuracy and avoid trapping in the local optimum, the homogeneous chaotic system and adaptive movement mechanism are combined into the proposed algorithm, where the initialization and redistribution of butterflies are chaotically dispersed with an adaptive movement closely related to the ultraviolet changes. After validating the performance of CABMO through several benchmark functions with different dimensions, the improved algorithm outperforms when compared to other state-of-the-art nature-inspired metaheuristic algorithms. The proposed algorithm is then used to understand any linear array problems in terms of the sidelobe reduction. Finally, a CABMO strategy is utilized to optimize the mutual coupling model of the closely spaced VLF umbrella arrays. Results show that the optimized structure has comfortably outperformed the original structure. Full scanning of wave positions is realized from 15 to 30 kHz. The synthesis patterns are close to the theoretical optimum. The optimized results of the radiation performance and synthesized patterns demonstrate that the pattern synthesis and antenna structure optimization based on the CABMO algorithm provides a novel idea for antenna array optimization.

Synthesis for sidelobe suppression of linear array based on improved grasshopper optimization algorithm with adaptive chaotic strategy

“…After 20 independent runs, the maximum SLL convergence characteristics of the 16‐element linear array optimized by GOA and ACGOA are shown in Figure 23, compared with GOA, ACGOA can find the best global optimum more quickly, the convergence speed is faster and the trend is more stable, and the optimization value of SLL is smaller. The array factor optimized by FA, 18 CS, 20 IWO, 24 CABMO, 25 GOA, and ACGOA are shown in Figure 24. The maximum SLL of the 16‐element linear array optimized by each algorithm and the excitation amplitude of each element are listed in Tables 11 and 12, respectively.…”

“…For antenna optimization in electromagnetic field, although many classical methods (convex optimization, 3,4 FFT, 5,6 and alternative projection 7,8 based methods) have been applied to array antenna sidelobe suppression, the most popular stochastic optimization method is the population algorithm inspired by nature. In the aspect of linear array pattern synthesis, there are many successful swarm intelligence optimization algorithms, such as genetic algorithm (GA), 9,10 particle swarm optimization (PSO), 11,12 cuckoo optimization algorithm (COA), 13 cat swarm optimization (CSO), 14 spider monkey optimization (SMO), 15 whale optimization algorithm (WOA), 16 firefly algorithm (FA), [17][18][19] cuckoo search (CS), 20,21 butterfly mating optimization (BMO), 22,23 chaos adaptive butterfly mating optimization (CABMO), 24 invasive weed optimization (IWO), 25 improved harmony search algorithm (IHSA), 26 etc. Because of their good robustness, the optimization ability of these algorithms has been verified to a certain extent, but there are still some limitations for specific antenna problems, such as convergence, optimization speed and accuracy, local or global optimization ability, etc.…”

Optimal Pattern Synthesis of Linear Array and Broadband Design of Whip Antenna Using Grasshopper Optimization Algorithm

“…CSO [13] SMO [14] GOA Figure 10, the distribution of maximum SLL for 20 runs of GOA for 16-element linear array is shown in Figure 11, and the best array factor is obtained as shown in Figure 12, which is compared with FA [13,[23][24][25], CS [13,26,27], IWO [13], and CABMO [28] optimized arrays. e maximum SLL and the excitation magnitude of each element of the linear array optimized by each algorithm are shown in Table 6.…”

“…In terms of electromagnetic field problems and antenna optimization, population-based algorithms inspired by nature are the most popular in stochastic optimization methods [4,5]. Many swarm intelligent algorithms have been successfully applied to antenna array pattern synthesis or antenna broadband optimization, such as genetic algorithm (GA) [6,7], ant colony optimization (ACO) [8,9], particle swarm optimization (PSO) [10][11][12], invasive weed optimization (IWO) [13], cat swarm optimization (CSO) [14], spider monkey optimization (SMO) [15], butterfly mating optimization (BMO) [16,17], social group optimization (SGO) [18], grey wolf optimization (GWO) [19], quadratic programming method (QPM) [20], flower pollination algorithm (FPA) [21], ant lion optimization (ALO) [22], firefly algorithm (FA) [23][24][25], cuckoo search (CS) [26,27], chaotic adaptive butterfly mating optimization (CABMO) [28], modified spider monkey optimization (MSMO) [29], enhanced firefly algorithm (EFA) [30], bat flower pollination (BFP) algorithm [31], gravitational search algorithm (GSA) [32], and so on. For antenna broadband optimization, there are also GA [33,34], evolutionary algorithm (EA) [35], real frequency technology [36], IWO [37][38][39], etc.…”

A Novel Modified Sparrow Search Algorithm with Application in Side Lobe Level Reduction of Linear Antenna Array

“…With the development of computer technologies and computational electromagnetics, some intelligent optimization algorithms that simulate the behavior mechanism of biological groups or the laws of natural phenomena have begun to appear in the vision of many scholars. With its unique advantages in solving large-scale, nonlinear, and other complex optimization problems, the design and optimization technology of antenna arrays based on an intelligent optimization algorithm has always been a research hotspot in the field of EM optimization [4].…”