Optimization Methods Through Selecting Suitable Search Techniques in Adaptive Hypercubes
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Abstract
This paper proposes two methods for unconstrained single objective optimization by creating a search space called a hypercube, where the position and boundaries vary according to the objective function values. The search within the hypercube utilizes either an exploratory search algorithm or an Adaptive Coefficient Particle Swarm Optimizer (ACPSO) based on the improvement of previous search results. The performance of these search methods is tested using seven benchmark functions with dimensions of 10, 15, and 20. The proposed methods successfully find solutions for up to five functions. They can obtain search values close to the solution of the Rosenbrock function, whereas most search methods tend to get trapped in the search process. The proposed search methods maintain their good performance even as the number of dimensions of the functions increases.
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