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Research Topic in Pelican Optimization Algorithm

Research Topic in Pelican Optimization Algorithm

Masters and PhD Research Topics in Pelican Optimization Algorithm

The Pelican Optimization Algorithm (POA) is a meta-heuristic algorithm inspired by the natural behavior of pelicans hunting for their prey found in water and living mainly in rivers, coasts, lakes and swamps. Pelican food consists mainly of fish and rarely of frogs, turtles and crustaceans. It has the advantages of adjustment parameters such as fast convergence speed and simple calculations. POA has better competitive performance via striking a proportional balance between exploration and exploitation to provide optimal solutions.

The behavior and strategy of pelican hunting is a sharp and intelligent process that made the birds skilled hunters. POA simulates the strategy of pelicans while attacking and hunting its prey to update candidate solutions, widely employed for dimensionality reduction.

The POA starts by randomly initializing a population of pelicans, each representing a potential solution to the optimization problem. Each pelican then evaluates its fitness based on the objective function of the problem, and the fittest pelicans are selected to form the "elite" group. The elite pelicans then fly to search for food, representing the search for better solutions.

The POA also incorporates a mechanism for exploring new areas in the search space. This is achieved by randomly selecting a subset of non-elite pelicans, which are then used to generate new candidate solutions through a random walk process. These new solutions are then evaluated and may potentially join the elite group. The POA continues to iterate through the search process until a stopping criterion is met, such as a maximum number of iterations or a desired level of solution quality is achieved.

Overall, the POA is a promising optimization algorithm that has shown good performance in many optimization problems, especially in high-dimensional and multimodal problems. However, like any other metaheuristic algorithm, its performance depends heavily on the problem-s characteristics and the chosen parameter settings.