The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.

User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) are a transportation layer routing protocols which are considered of the core protocols of the internet protocol suite. The behaviour of these routing protocols with different network metrics and scenarios is still not very clear. Therefore, this paper presents a comparison of the performance of both TCP and UDP to precisely determine which of these protocols is better. Network Simulator version 2.35 (NS2) is utilized to analyse and evaluate the performance for both TCP and UDP protocols varying in the packet size and the bandwidth. In this study, we have used two scenarios, in the first scenario the bandwidth has been changed with fixed packet size and in the second scenario the packet size has been changed with fixed bandwidth to precisely verify the performance of these protocols. These protocols were examined in terms of the rate end-to-end delay, rate throughput, packet delivery ratio, and packet loss ratio Â

Performance Evaluation of LAR and OLSR Routing Protocols in Forest Fire Detection using Mobile Ad-Hoc Network