## 3 Related Works of Enhancements to SLAM
### Evolutionary Algorithm-Based Improvements to SLAM  
Evolutionary algorithms represent a class of computational models inspired by biological evolution, which are particularly adept at handling complex optimization problems. In the context of FastSLAM, these algorithms offer a promising avenue for improvement by leveraging their robust search capabilities to enhance the exploration and exploitation of the solution space. This section delves into the integration of evolutionary algorithms with FastSLAM, highlighting how such an integration leads to significant enhancements in the algorithm's performance. We will review the key contributions of incorporating evolutionary strategies into FastSLAM, setting the stage for a discussion of our proposed improvements in subsequent literature references. [PERSON] et al. [13] proposed two sample-based evolutionary methods for global localization of mobile robots: differential evolution (DE) and PSO. These methods were designed to address the limitations of traditional probabilistic approaches, such as the Kalman filter, by accommodating non-linear and non-Gaussian noise distributions. The DE and PSO algorithms demonstrated superior performance over the standard Monte Carlo localization algorithm in terms of convergence rate, accuracy, and computational cost. The experimental results indicated that these evolutionary methods required fewer individuals or particles for convergence, highlighting their potential for efficient global localization in mobile robotics applications. [PERSON] et al. [14] introduced an evolutionary localization algorithm known as the Evolutive Localization Filter (ELF). This algorithm is founded on evolutionary computation principles and addresses the global localization challenge by stochastically searching for the optimal robot pose estimate within the state space. The ELF leverages observation and motion errors, derived from probabilistic perception and motion models, to evolve a population of pose solutions. The algorithm was successfully integrated into a mobile robot equipped with a laser range finder, demonstrating effectiveness, robustness, and computational efficiency in experimental evaluations. [PERSON] et al. [15] presented a hybrid localization approach that integrates the particle filter with PSO. This method, termed the Particle Swarm Optimization Filter (POF), focuses on accurate and robust localization in environments with a priori known maps. The POF algorithm excels in symmetrical environments, a common challenge for traditional localization techniques. The experimental results showcased that the proposed method achieved robust and precise positioning outcomes in indoor settings, utilizing fewer particles compared to benchmark algorithms, thereby reducing computational costs and enhancing navigation efficiency. [PERSON] et al. [16] developed a novel global localization filter that amalgamates DE with the Markov Chain Monte Carlo (MCMC) approach. This filter, termed the Differential Evolution Markov Chain Filter, represents the robot's pose as a set of potential location estimates weighted by a fitness function. The integration of evolutionary optimization with the statistical robustness of MCMC resulted in a significant reduction in population requirements compared to previous versions. The filter was tested in both simulated and real-world maps, yielding promising results in terms of localization accuracy and computational efficiency. Despite the numerous evolutionary approaches integrated to address SLAM challenges, the shortcomings of traditional single-objective optimization methods have become increasingly evident. These single-objective algorithms, which tend to concentrate on optimizing only one aspect of the SLAM process, often fall short in tackling the intricate and multifaceted issues associated with particle degeneration. In response, the integration of multi-objective optimization algorithms presents a promising avenue.