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This research article presents a novel approach to Maximum Power Point Tracking (MPPT) for photovoltaic systems, employing a modified bacterial foraging algorithm with dynamically adjustable mutation rates. This method is specifically tailored to address the challenges presented by partial shading conditions, ensuring efficient and rapid tracking of the MPP while preventing local optima entrapment. To evaluate the performance of this innovative technique, a comparative analysis is conducted against the original bacterial foraging algorithm and the grey wolf optimization algorithm, both commonly employed in MPPT applications. The modified algorithm incorporates a unique strategy that dynamically adapts mutation rates based on the algorithm's convergence behavior, enhancing the tracking accuracy from 81.31% to 89.39%. To validate the effectiveness of the proposed technique, extensive simulations are carried out using MATLAB Simulink, considering various partial shading scenarios commonly encountered in practical photovoltaic applications. It's worth noting that the shading scenario data were extracted from the NASA Worldwide Prediction of Energy website, specifically from the city of Ain El Ibel Djelfa irradiance records. The simulation results unequivocally demonstrate the superiority of the modified bacterial foraging MPPT technique over both algorithms in terms of tracking efficiency (0.4s to 0.9s) and robustness under partial shading conditions. The findings of this research offer valuable insights into the potential advantages of employing a modified bacterial foraging approach for MPPT applications. This innovative techniques with its ability significantly enhance its performance in real-world scenarios involving partial shading, positioning it as a promising choice for optimizing photovoltaic system efficiency and power output. [ABSTRACT FROM AUTHOR]

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system. [ABSTRACT FROM AUTHOR]

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system.

Bio-inspired algorithms are widely and successfully used in solving complex optimization problems. However, a common challenge faced by these algorithms is getting stuck in local search spaces and failing to explore new regions to further enhance the solution. Such a limitation is often attributed to the influence of the working parameters that need to be carefully tuned so to obtain an optimal performance. Unfortunately, finding the right parameter settings is problem-specific and can vary from one instance to another. To address this issue, this research investigates the integration of machine learning techniques with bio-inspired algorithms to dynamically adjust algorithm parameters based on solution quality. Specifically, the study focuses on an immune system-inspired algorithm and introduces a new strategy to mutate individuals in order to explore the search space more efficiently. By leveraging prediction techniques such as Linear Regression, the algorithm can predict local optima and adjust its search direction accordingly. The proposed method, referred to as Dynamic-IA , is evaluated using the Network Reliability Problem as a case study. Several network topologies are considered in our experiments and the results obtained by our algorithm when compared with the standard Immune Algorithm and other metaheuristic algorithms prove our strategy. • Immunological Algorithm with dynamic mutation rate. • Combining Immune Algorithm with Machine Learning techniques. • Ordinary Least Squares Regression method to dynamically determine the number of mutations. • The Maximum Relevance Minimum Redundancy algorithm was used to detect the most important parameter of Immune Algorithm. • The Network Reliability Problem was tackled. [ABSTRACT FROM AUTHOR]

This paper presents a method on how to estimate main effects of gene representation. This estimate can be used not only to understand the domination of genes in the representation but also to design the mutation rate in genetic algorithms (GAs). A new approach of dynamic mutation rate is proposed by integrating the information of the main effects into the genes. By introducing the proposed method in GAs, both solution quality and solution stability can be improved in solving a set of parametrical test functions. The algorithm was applied to two illustrative applications to evaluate the performance of the proposed method, where the first application is on solving uncapacitated facility location problems and the next is on optimal power flow problems, which are employed. Results indicate that the proposed method yields significantly better results than the existing methods. [ABSTRACT FROM AUTHOR]