Hidden Markov Model in Hill Climbing Algorithm with Randomized Search Algorithm

Author Reg. ID: TIJER_103934

Published Paper Id: TIJER2403062

Published In: Volume 11 Issue 3, March-2024

Abstract: Numerical Analysis, Hill Climbing is a mathematical optimization technique that belongs to the local search family. It is an iterative algorithm that starts with an arbitrary solution to a problem and then tries to find a better solution by making an incremental change. If the change produces a better solution, another incremental change is made to the new solution, until no further improvements can be found. Hill-climbing algorithm is a local search algorithm that continuously moves upward (increasing) until the best solution is reached. This algorithm terminates when the peak is reached. The state space diagram visually represents the states and the optimization function. When the objective function is the y-axis, we try to find the local maximum and the global maximum. Hill climbing is useful for the effective operation of robotics. It enhances the coordination of different systems and components in robots.

Keywords: Hill climbing, Local Maximum, Global Maximum, Ridges, Robotics, Job planning.

Downloads: 00035

Page No: a467-a470

Country: Srikakulam, Andhra Pradesh, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403062

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403062

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