# A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm
## 1 Introduction  
Many methods have been proposed to solve varied optimization problems. Exact optimization approaches (i.e., approaches guaranteeing the convergence to an optimal solution) have been proved to be valid and useful, but many still experience considerable difficulties when dealing with complex and large-scale optimization problems. Therefore, numerous meta-heuristics have been proposed to deal with these problems. Among meta-heuristics, hybrid meta-heuristics combining exact and heuristic approaches have been successfully developed and applied to many optimization problems such as [PERSON]. [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al, and [PERSON] et al. [1; 2; 3; 4; 5; 6]. As a result of their superior optimization performance and simplicity, meta-heuristic optimization algorithms have recently become very popular and are currently applied in a variety of fields. Meta-heuristic algorithms predominantly benefit from stochastic operators which can avoid local optima as opposed to traditional deterministic approaches [7; 8; 9]. Among them, nature-inspired meta-heuristic optimization algorithms are widely used in this field because of their flexibility, simplicity, good performance, and robustness. Nature-inspired meta-heuristic algorithms are predominantly used to solve optimization problems by mimicking not only physical phenomena but also biological or social behaviors. They can be classified into three categories: evolution-based, physics-based, and swarm-based approaches. Evolution-based algorithms are inspired by evolutionary processes found in nature. Genetic algorithm (GA) [10], biogeography-based Optimization (BBO) [11] and differential evolution (DE) [12] are the most popular evolution-based algorithms. In addition, a new evolution-based polar bearoptimization algorithm (PBO) [13] has been proposed which imitates the survival and hunting behaviors of polar bears and presents a novel birth and death mechanism to control the population. Physics-based algorithms are inspired by natural physical phenomena. The most popular algorithms are simulated annealing (SA) [14] and gravitational search algorithm (GSA) [15]. Moreover, some new physics-based algorithms, such as the black hole algorithm (BH) [16] and ray optimization (RO) [17], have recently been proposed. Swarm-based algorithms which mimic the biological behaviors of animals are very popular. The most well-known swarm-based algorithm is the particle swarm optimization (PSO) [18] which was inspired by the social behavior of birds. Two additional classic swarm-based algorithms are the ant colony optimization (ACO) [19] and the artificial bee colony algorithm (ABC) [20] which imitate the behaviors of ant and bee colonies, respectively. Recently, many novel swarm-based algorithms imitating different population behaviors have been proposed, including the firefly algorithm (FA) [21], the bat algorithm (BA) [22], the cuckoo search (CS) [23], the social spider optimization algorithm (SSO) [24], the grey wolf optimizer (GWO) [25], the dragonfly algorithm (DA) [26], the ant lion optimizer (ALO) [27], the moth-flame optimization algorithm (MFO) [28] and the whale optimization algorithm (WOA) [29]. Swarm-based optimization algorithms have been widely used in engineering, industry and other fields as a result of their excellent optimization performance and simplicity. Proposed by [PERSON] in 2014, the Grey Wolf Optimizer algorithm (GWO) is a swarm-based meta-heuristic optimization algorithm and was inspired by the hunting and search behaviors of grey wolves [25]. Thanks to its easy implementation, minor parameters, and good optimization performance, the GWO has been applied to various optimization problems and many engineering projects, such as power systems, photovoltaic systems, automated offshore crane design, feature selection in neural networks, etc. [30; 31; 32; 33; 34; 35]. Moreover, many researchers have attempted different strategies to enhance the performance of GWO [36; 37; 38; 39; 40; 41; 42; 43].