A non-revisiting artificial bee colony algorithm for phased array synthesis

Abstract

An effective non-revisiting artificial bee colony (NrABC) algorithm based on the paradigm of artificial bee colony (ABC) is developed in this paper. NrABC is applied to tackle the synthesis of phased linear arrays. Pros and cons of NrABC is provided along with a comparison to standard ABC. Binary space partitioning tree structure is used to record history evolutionary information. Non-revisiting scheme assures NrABC keeping good diversity of population. Moreover, scout bee stage is discarded in NrABC which also removes an algorithmic parameter of standard ABC. Three phased array synthesis examples are employed to study the performance of NrABC. It turns out that under the same experimental configuration, NrABC outperforms standard ABC in terms of both solution quality and reliability in repeated runs.

An improved dynamic cooperative random drift particle swarm optimization algorithm based on search history decision

“…In equation (13) s and u k i;j are two different random numbers uniformly distributed on (0,1), i.e. s; k$Uð0;1Þ.…”

“…Most importantly, when the same optimal value reappears again in the search history, it can warn that the search may fall into local optimum. Non-revisiting strategy is integrated with intelligent algorithms12–18 to avoid the re-evaluation of the evaluated solution candidates by using binary space partitioning (BSP). The non-revisiting genetic algorithm (NrGA) is proposed to prevent solution re-evaluation14,15 in the genetic algorithm by using search history and cNrGA16 is another version designed for continuous variables.…”

Thinning of antenna array via adaptive memetic particle swarm optimization

“…Stochastic algorithms are popularly used because traditional optimization methods are not suitable due to the unavailable of gradient information [11][12][13]. Stochastic algorithms also show good performance for such design problems [14,15].…”

“…Although EAs and SI are created based on different nature, their positive combinations are able to result effective algorithms [22]. In the past, GA, PSO, and ABC have been used in antenna designs [15,23,24]. Recently, covariance matrix adaptation evolutionary strategy [25] and differential evolution [26] are applied to synthesize antenna array patterns.…”