Authors: Johnson A. Adeyiga, Kehinde A. Sotonwa, Micheal T. Adenibuyan.
The search for parking space is a time consuming process which not only affects the economic activities efficiency but also the social interaction and cost. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were simulated to allocate parking space for vehicles by sending request by request handler to route generator to allocate optimal route from source to destination using Matric Laboratory (MATLAB) Software in an intelligent parking system. This was measured by some parameters such as time taken, cost and user satisfaction. Unlike GA that required some genetic operations which made it unable to handle complexity that increase search space; PSO required small number of parameters and correspondingly lower iteration which made it best alternative. Also, PSO always choose a parking space that can be reached with deadline and within the budget imposed by user, traffic and free slots which made it to achieve high user satisfaction, saves time and cost effective.
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