# A Quantum-Inspired Predator-Prey Algorithm for Real-Parameter Optimization
## 2 Related Work  
The field of metaheuristic optimization has grown rapidly with inspiration from various processes in nature. _Cat swarm optimization_ (CSO) is a population-based optimization technique that mimics the foraging behavior of cats as they search for prey [28]. The _tunicate swarm algorithm_ (TSA) imitates the jet propulsion and swarm behaviors of tunicates during the navigation and foraging process [29]. _Bee colony optimization_ (BCO) [30] features the actions of individuals in various decentralized systems and is applicable to complex combinatorial optimization problems. _Bacterial foraging optimization_ (BFO) mimics how bacteria forage over a landscape of nutrients to perform parallel non-gradient optimization [31]. _Artificial bee colony_ (ABC) [32] is inspired by the foraging behavior of honey bees and consists of three essential components: (1.) employed foraging bees, (2.) unemployed foraging bees, and (3.) food sources. ABC has simple implementation and efficient exploration capabilities, but can become stuck in local optima and requires fine-tuning due to its sensitivity to hyperparameters [33; 34].  
[PERSON] et al. [35] introduced the _mayfly optimization_ (MO) algorithm, where the swarm of individuals are categorized as male and female mayflies and each gender exhibits distinct swarm (population) updating behavior. In the MO algorithm, individuals situated far from the best candidate or historical best trajectories move toward the optimal position at a reduced speed. On the contrary, when individuals are close to the global best candidate or historical trajectories, they move at a faster pace, which slows down the convergence rate [36]. The _firefly algorithm_ (FA) [37] mimics the flashing behavior of fireflies to solve complex optimization problems. It suffers from some drawbacks, such as a slow convergence rate, becoming stuck in local optima, and sensitivity to the choice of hyperparameters. The _dragonfly algorithm_ (DA) [38] originates from the static and dynamic swarming behaviors of dragonflies in nature. It can solve complex and multi-objective optimization problems and can handle constraints, while searching for diverse solutions.  
The _cuckoo search_ (CS) [39] optimization is inspired by the brood parasitism of cuckoo species with the behavior of laying eggs in the nests of other bird species. The _grasshopper optimization algorithm_ (GOA) [40] is a multi-objective algorithm inspired by the navigation of grasshopper swarms that features the interaction of individuals in the swarm, including the attraction force, repulsion force, and comfort zone. The _whale optimization algorithm_ (WOA) [41] mimics the social behavior of humpback whales, inspired by the bubble-net hunting strategy. The _grey wolf optimizer_ (GWO) [42] mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. _Jaya_[43] is a simple and parameter-less optimization algorithm designed for both constrained and unconstrained optimization. It does not require the tuning of hyperparameters, making it user-friendly. The _sine cosine algorithm_ (SCA) [44] balances exploration and exploitation, with promising results across various domains.  
In general, most of these metaheuristic optimization algorithms have the advantage of being easy to implement and providing gradient-free optimization and are suitable in problems where gradient information is not theoretically available or is computationally difficult to obtain. These are population-based algorithms that have a pool of individuals that compete and collaborate to form new solutions over time. However, they have some drawbacks, such as slow convergence, low precision in solving problems, hyperparameter tuning, and a limited ability to perform local searches. The algorithms presented earlier focus on continuous parameter optimization and can be extended to apply to discrete-parameter and constraint-parameter optimization problems.