Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm: Performance Analysis and Optimization Exploration

Comparison Of Wolf Pack And Artificial Bee Colony Algorithms: Performance Analysis And Optimization Exploration

Optimization algorithms are based on specific ideas or mechanisms and design certain rules to solve problems. Classical optimization algorithms mainly include genetic algorithms (Goldberg & Holland, 1988), ant colony algorithms (Colorni & Dorigo, 1991), particle swarm optimization (PSO) algorithms (Kennedy & Eberhart, 1995), artificial bee colony (ABC) algorithms (Karaboga, 2005), and simulated annealing algorithms (Kirkpatrick et al., 1983). These algorithms are well structured and operate on simple principles, hence the name intelligent optimization algorithms or metaheuristic algorithms (L. Wang, 2001). Intelligent optimization algorithms can be subdivided at the principle level into several broad categories such as humanoid intelligence-like algorithms (Rumelhart et al., 1986; Hart et al., 1998; Glover, 1989; Shi, 2011; Atashpaz-Gargari & Lucas, 2007), evolutionary algorithms (Wang et al., 2020c; Storn & Price, 1997; Ferreira, 2001), swarm intelligence-like algorithms, plant growth-like algorithms (Mehrabian & Lucas, 2006; Ghaemi & Feizi-Derakhshi, 2014; X. S. Yang, 2012), and nature-like algorithms (Shareef et al., 2015; Rashedi et al., 2009; Patel & Savsani, 2015; S. Li et al., 2019).

Group intelligence algorithms (Cheng et al., 2018) are inspired by group behaviors, such as foraging and reproduction of natural group-living organisms, and achieve complex functions through cooperation, competition, interaction, and learning among individuals, which show superior performance in solving complex problems and thus have been widely used in combinatorial optimization, image processing, and data mining. In 1989, G. Beni et al. proposed the concept of “swarm intelligence” (Beni et al., 2020). Ant colony algorithms (Colorni & Dorigo, 1991; Colorni et al., 1992; Colorni et al., 1994) and PSO algorithms (Kennedy & Eberhart, 1995; Smets & Kennes, 1994) are the pioneers of swarm intelligence optimization algorithms. With the development of the algorithms, many classical algorithms have emerged, including the ant colony algorithm (Colorni & Dorigo, 1991), PSO algorithm (Kennedy & Eberhart, 1995), ABC algorithm (Karaboga, 2005), and cuckoo search algorithm (X. S. Yang & Deb, 2009).