# GBUO: \"The Good, the Bad, and the Ugly\" Optimizer
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Ref.** & **Algorithm** & **Main Idea (Inspiration Source)** & **Year** \\ \hline \hline \multirow{8}{*}{SB} & [17] & Particle swarm optimization & Social behavior of birds & 1995 \\ \cline{2-4}  & [24] & Cuckoo search & Behavior of cuckoo & 2009 \\ \cline{2-4}  & [25] & Lion optimization algorithm & Behavior of Lion & 2016 \\ \cline{2-4}  & [26] & Grasshopper optimization algorithm & Grasshopper behavior & 2017 \\ \cline{2-4}  & [27] & Emperor penguin optimizer & The behavior of Emperor Penguin & 2018 \\ \cline{2-4}  & [28] & Pity beetle algorithm & Aggregation behavior, searching for nest and food & 2018 \\ \cline{2-4}  & [29] & Mouth brooding fish & The behavior of mouthbrooding Fish & 2018 \\ \cline{2-4}  & [30] & Sailfish Optimizer & Group of hunting sailfish & 2019 \\ \cline{2-4}  & [31] & Following Optimization Algorithm & Relationships between members and the leader of a community & 2020 \\ \cline{2-4}  & [32] & Multi-Leader Optimizer & The presence of several leaders simultaneously for the population members & 2020 \\ \hline \multirow{8}{*}{EB} & [33] & Genetic algorithm & Darwinian evolution theory & 1992 \\ \cline{2-4}  & [34] & Differential evolution & the natural phenomenon of evolution & 1997 \\ \cline{2-4}  & [35] & Genetic program & The biological model of evolution & 1998 \\ \cline{2-4}  & [36] & Evolution strategy & Darwinian evolution theory & 2002 \\ \cline{2-4}  & [37] & Biogeography-based optimizer & Biogeographic concepts & 2008 \\ \cline{2-4}  & [38] & Artificial infectious disease & SEIQR epidemic model & 2016 \\ \cline{2-4}  & [39] & Rooted tree optimization & Plant roots movement looking for water & 2016 \\ \cline{2-4}  & [40] & Weighted superposition attraction & Weighted superposition of active fields & 2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Well-known meta-heuristic algorithms (MHAs) are proposed in the literature. Each of the above-mentioned algorithms has its specific advantages and disadvantages. For instance, in thermal process which are sufficiently slow to allow time for simulation, simulated annealing guarantees that the obtained solution is optimal. Nevertheless, fine-tuning of parameters affects the convergence of the optimization problem. In the development of MHAs, their mathematical analysis includes some open issues that require close attention. These problems are mainly of different components in MHAs that are stochastic, complex, and extremely nonlinear. Various swarm intelligence (SI) algorithms have recently been reported. The particle swarm optimization (PSO) algorithm [17] is inspired by fishes or birds' social behavior. The artificial bee colony algorithm (ABC) [59] and the ant colony optimization (ACO) algorithm [15] are inspired by the foraging behavior of honeybees and the ants' behavior when finding the optimal path in the ant colony foraging process, respectively. The ant colony's pheromone matrix continuously evolves within the candidate solution's iteration leading to an optimal solution. This could be useful in solving path planning problems [60]. The cuckoo search algorithm (CS) [24] is a simulation of the obligate brood parasitic behavior of a certain kind of cuckoo [61]. These types of algorithms are not popular due to their high complexity. In 2011, a simulation of the cooperative foraging fruit flies' behavior was presented, resulting in the fruit fly optimization algorithm (FOA) [62]. Other examples of recently introduced SI algorithms include grey, firefly algorithm (FF) [63], wolf optimization algorithm (GWO) [64], \"doctor and patient\" optimization (DPO) [65], donkey theorem optimization (DTO) [66], group optimization (GO) [67], squirrel search algorithm (SSA) [68; 69], dragonfly algorithm (DA) [70] among others.