The increasing population and uncontrolled urbanization have led to increased greenhouse gas emissions, compromising the regularity of climate. This has made the utilization of water and water-based renewable energy systems fragile and uncertain. To reduce risk and increase energy resource utilization, various decision-making methods and optimization techniques have been successfully applied. However, few studies have focused on the impact of climate change on maximum utilization of water or water-based renewable energy sources under minimum cost constraints.

Opportunities have been solved using traditional optimization techniques like linear programming, non-linear programming, and dynamic programming. However, their drawbacks have led to the demand for heuristic optimization approaches like simulated annealing, tabu search, and evolutionary algorithms.

Bio-inspired algorithms are considered more optimal than conventional optimization techniques like linear or integer-based algorithms. Multi-criteria decision-making methods, such as weighted averages, priority setting, outranking, fuzzy principles, and their combinations, are used for sustainable energy planning decisions. Analytical Hierarchy Process (AHP) is the most popular technique, followed by outranking techniques like PROMETHEE and ELECTRE.

The role of cognitive nature-inspired optimization algorithms and multi-criteria hierarchical decision-making methods has shown solutions to many water and energy-related problems. This internship will allow the participants to explore the potential of metaheuristics hybridized with MCDM techniques in optimal utilization water or water based renewable energy potentials.

Anyone interested ? Please participate in the discussions.