# CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments
## 2 Related Work  
In the literature, there are several studies on dynamic optimization problems. For example, [PERSON] and [PERSON] [27] tackled and deliberated on different forms of uncertainty in evolutionary optimization. [PERSON] et al. [16] have furthered the progress of the domain by achieving a dual objective, thereby enhancing its significance: (1) Their accomplishment involved the establishment of a vast collection of pertinent references on the subject matter from the previous ten years, which they then classified according to various criteria, including publication type, type of dynamism, dynamic optimization problem-solving approaches, performance metrics, applications, and year of publication. (2) Afterward, they conducted a comprehensive review of the research conducted on dynamic optimization problems using the compiled collection. [PERSON] et al. [28] conducted a comprehensive investigation into the field of evolutionary optimization in dynamic environments, presenting an in-depth survey of the field. This research analyzed the latest advancements in the academic literature from four distinct perspectives, namely: (a) benchmark problems, (b) performance metrics, (c) methodologies, and (d) theories. Although their work is very valuable in studying and summarizing different methods for solving MPB, it does not provide categorical information on how different methods work and which mechanisms can improve the performance of different methods. In addition, it does not explain the reasons for specific approaches' superiority. [PERSON] et al. [29; 30], in two parts of survey papers, presented a review of research studies regarding DOPs. Since an efficient dynamic optimization algorithm consists of several parts to cope with dynamic optimization problems, they tried to provide a comprehensive taxonomy to identify the parts of dynamic optimization algorithms. In the second part of this survey, they gave an overview of dynamic optimization problem benchmarks and performance measures. [PERSON] et al. provide another study [31]. In this work, the authors surveyed the existing techniques in the literature for addressing MPB. They categorize the diverse methods into four groups: swarm intelligence algorithms, evolutionary algorithms, hybrid approaches, and other approaches. In [22], [PERSON] et al. introduced the concept of multi-swarms, which involves partitioning the population of particles into multiple subgroups, each with its own information sharing and exploration strategies. The researchers propose using an adaptive partitioning technique that dynamically adjusts the number and size of multi-swarms based on the characteristics of the problem and the environment. Then, they introduced the concept of exclusion, which allows individual particles to temporarily avoid regions of the search space that are not beneficial. By excluding certain areas, particles can avoid premature convergence and explore other areas of the search space. Additionally, the authors addressed the issue of anti-convergence, which occurs when all particles converge to a suboptimal solution. To mitigate this problem, the authors propose a re-initialization mechanism, which randomly disperses particles in the search space to encourage exploration. In [32], [PERSON] et al. focused on improving particle swarm optimization (PSO) algorithms in dynamic environments by incorporating both speciation and adaptation mechanisms. Speciation is a process inspired by biological evolution, where particles are divided into different sub-populations or species based on their similarities. This helps maintain diversity and exploration, even in changing or dynamic environments. Adaptation, on the other hand, enables particles to adjust their behavior and parameters in response to environment changes. It allows particles to quickly react and update their positions and velocities to find better solutions. [PERSON] et al. [33] proposed the integration of speciation, a concept from evolutionary biology, into the firefly algorithm.