Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm

Abstract

Abstract:In many non-deterministic search algorithms, particularly those analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. The BA is a population-based metaheuristic search algorithm inspired by bees seeking nectar/pollen. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 well-known benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much more quickly.

Bee System-Based Self Configurable Optimized Resource Allocation Technique in Device-to-Device (D2D) Communication Networks

“…employed bees, onlooker bees, and scout bees. Here we provide an overview of how the ABC algorithm works [42], [43], [44].…”

Routing Optimization in WBAN using Bees Algorithm for Overcrowded Hajj Environment

“…The distance route is calculated from patient to medical center then the shortest node is selected based on Bayesian game formulation. According to [15], Bees Algorithm has been shown to be powerful optimization methods when compare it to other population-based methods [16]. The Bees Algorithm is proposed in this paper to find best path for data to reach destination within shortest time during overcrowded Hajj environment.…”

Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10)

“…On the other hand, stochastic methods can provide successful results in finding the global best solution without consideration of any assumption of differentiability and continuity of objective function. Until now, several stochastic methods such as genetic algorithms (GA) (Holland, 1992; Grüninger and Wallace, 1996; Ursem, 2000; Deb et al, 2002; Poli and Langdon, 2002; Dilettoso and Salerno, 2006; Krug et al, 2010), simulated annealing (SA) (Woodley et al, 1999; Abraham and Probert, 2006; Glass et al, 2006; Oganov and Glass, 2006; Trimarchi and Zunger, 2007), differential evolution (DE) (Storn, 1996; Storn and Price, 1997; Price et al, 2006; Rocca et al, 2011), harmony search (HS) (Geem, 2000, 2001, 2006; Geem et al, 2001, 2005; Diao and Shen, 2012; Gholizadeh and Barzegar, 2013; Hadwan et al, 2013; Manjarres et al, 2013; Nekooei et al, 2013; Wang and Li, 2013; Hoang et al, 2014; Fattahi et al, 2015; Weyland, 2015; Assad and Deep, 2016), ant colony optimization (ACO) (Colorni et al, 1992; Dorigo, 1992; Dorigo and Di Caro, 1999; Zlochin et al, 2004; Dorigo and Birattari, 2010; Korošec et al, 2012), cuckoo search (CS) (Payne and Sorensen, 2005; Yang and Deb, 2009; Inderscience, 2010), bat algorithm (BA) (Altringham et al, 1996; Richardson, 2008; Yang, 2010a,b), artificial bee colony optimization (ABC) (Karaboga and Basturk, 2007, 2008; Omkar et al, 2011; Fister and Žumer, 2012; Li G. et al, 2012), honey bee mating optimization (HBMO); (Pham et al, 2005; Haddad et al, 2006; Afshar et al, 2007; Jahanshahi and Haddad, 2008; Marinakis and Marinaki, 2009; Pham and Castellani, 2009, 2014, 2015; Bitam et al, 2010; Gavrilas et al, 2010; Marinaki et al, 2010; Chakaravarthy and Kalyani, 2015; Nasrinpour et al, 2017; Rajasekhar et al, …”