A Relative Assessment of Genetic Algorithm and Binary Particle Swarm Optimization Algorithm for Maximizing the Annual Profit of an Indian Offshore Wind Farm

Since climate change is prompting cataclysmic consequences across the world, renewable power generation technologies like wind power recommend fitting substitutes to fossil fuels for dwindling greenhouse gas production. For facilitating the escalating energy requisite of its rising economic structure, India must track down newer cost-effective wind power generation prospects. The current study intends to maximize the annual profit of an offshore wind power generating site in the Gulf of Khambhat exercising artificial intelligence. Genetic algorithm and binary particle swarm optimization algorithm have been applied at the same time to weigh their relative proficiency. The conclusions of the evaluation verify the enhanced competence of genetic algorithm over binary particle swarm optimization in optimizing the deemed purpose.

Keywords:

Offshore Wind Power, Genetic Algorithm, Binary Particle Swarm Optimization, Annual Profit Gulf of Khambhat,

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