# CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments
## 2 Related Work
This is a widely used optimization algorithm inspired by fireflies' behavior. The algorithm partitions the population into different species based on their similar solutions. Fireflies within the same species closely cooperate and share information, while fireflies belonging to different species compete for resources. This division allows for both exploration and exploitation of the search space, improving the algorithm's ability to adapt to changing environments. The authors in [34] focused on studying the effectiveness of a multi-population heuristic approach to solving non-stationary optimization tasks. The authors emphasize that real-world optimization problems often have non-stationary characteristics, meaning that the problem landscape changes over time. Thus, they introduced a multi-population heuristic approach, which involves multiple populations working in parallel. Each population adapts and evolves independently, making the approach suitable for solving non-stationary problems where the landscape changes unpredictably. An appropriate candidate for a nature-inspired algorithm dealing with the changing environment components is an artificial immune system (AIS). An AIS [35] is an adaptive system inspired by vertebrate immune processes developed by researchers to solve complex real-world problems [36]. In this regard, some achievements have been made in AISs dealing with DOPs. [PERSON] et al. have proposed modifications to the artificial immune network (AIN) algorithm for dynamic environments [37]. They utilize particular sub-populations for memory, linear search for parameter control, and novel operators for mutation in AIN. The multi-population strategy of the artificial immune algorithm in a dynamic environment has been suggested by [PERSON] et al., which obtained relatively successful results [38]. In [39], the authors focused on the improvement of adaptation in optimization problems subject to time-dependent changes. The authors propose a hybrid approach that combines genetic algorithms (GAs) and artificial immune systems (AIS) to enhance optimization. The proposed hybrid approach incorporates a multi-objective optimization framework, which combines exploration and exploitation objectives with AIS components. Specifically, the authors introduce an immune-inspired strategy for maintaining a diverse population of solutions and adaptively reacting to changes in the optimization landscape. The AIS components act as an additional mechanism for preserving diversity, increasing adaptability, and improving the algorithm's convergence rate. [PERSON] et al. [40] proposed a novel optimization technique called Immune-inspired Somatic Contiguous Hypermutation (ISCH). This technique is inspired by the immune system's somatic hypermutation process, which generates diverse antibodies to combat various pathogens. ISCH involves the creation of a population of candidate solutions, represented as individuals or antibodies. Each candidate solution corresponds to a specific state within the search space. The somatic contiguous hypermutation operator is then applied to these individuals to generate mutated offspring. Unlike traditional mutation operators, the contiguous hypermutation in ISCH selectively mutates contiguous regions within the candidate solutions. This approach allows for more focused exploration of the search space, potentially yielding better solutions in less time. In [41], [PERSON] et al. discussed the clonal selection algorithm (CSA) and its various applications in engineering. CSA is a computational optimization technique inspired by the immune system's clonal selection process. This algorithm mimics the immune system's ability to generate antibodies to combat infections, and it has been successfully applied to a wide range of engineering problems. A comprehensive review and performance evaluation of some different mutation behaviors for the clonal selection algorithm, artificial immune network, and B-cell algorithm is reported in dynamic environments [42] by [PERSON] et al.