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Difference Between Hill Climbing and Simulated Annealing Algorithm

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Pre-requisites: Hill Climbing

Hill Climbing is a heuristic optimization process that iteratively advances towards a better solution at each step in order to find the best solution in a given search space. It is a straightforward and quick technique that iteratively improves the initial solution by making little changes to it. Hill Climbing only accepts solutions that are better than the current solution and employs a greedy technique to iteratively move towards the best solution at each stage.

Hill Climbing may not locate the global optimum because it is susceptible to becoming caught in local optima. Because of this, it is inappropriate for complex issues with numerous local optima. Hill Climbing is simple to create and has no tweaking requirements.

In order to discover the best solution in a given search space, the probabilistic optimization algorithm Simulated Annealing simulates the annealing process used in metalworking. The algorithm begins with a randomly generated initial solution and incrementally improves it by accepting less desirable solutions with a certain probability. The probability of accepting a worse solution decreases as the algorithm progresses, which enables it to escape local optima and find the global optimum.

Simulated annealing explores the search space and avoids local optimum by employing a probabilistic method to accept a worse solution with a given probability. The initial temperature, cooling schedule, and acceptance probability function are just a few of the tuning parameters. Hill Climbing is faster, but Simulated Annealing is better at locating the global optimum, particularly for complex issues with numerous local optima.

Several fields, including logistics, scheduling, and circuit design, use simulated annealing. The approach is especially helpful for optimization issues when the objective function is challenging to evaluate or where the search space is intricate and high-dimensional.

Parameters Hill Climbing  Simulated Annealing
 

Introduction

Hill Climbing is a heuristic optimization process that iteratively advances towards a better solution at each step in order to find the best solution in a given search space.

Simulated Annealing is a probabilistic optimization algorithm that simulates the metallurgical annealing process in order to discover the best solution in a given search area by accepting less-than-ideal solutions with a predetermined probability.

Objective

By iteratively progressing towards a better solution at each stage, Hill Climbing seeks to locate the ideal solution within a predetermined search space.

Simulated annealing seeks the global optimum in a given search space by accepting poorer answers with a predetermined probability. This allows it to bypass local optimum conditions.

Strategy

In order to iteratively move towards the best answer at each stage, Hill Climbing employs a greedy method. It only accepts solutions that are superior to the ones already in place.

Simulated annealing explores the search space and avoids local optimum by employing a probabilistic method to accept a worse solution with a given probability. As the algorithm advances, the likelihood of accepting an inferior answer diminishes.

Local vs. Global Optima 

Hill Climbing may not locate the global optimum because it is susceptible to becoming caught in local optima.

Simulated annealing has a chance of escaping the local optimum and locating the global optimum.

Stopping Criteria 

Hill Climbing comes to an end after a certain number of iterations or when it achieves a local optimum.

When the temperature hits a predetermined level or the maximum number of repetitions, simulated annealing comes to an end.

Performance

Hill climbing is quick and easy, but it has the potential to become locked in local optima and miss the overall best solution.

Simulated annealing is more efficient at locating the global optimum than Hill Climbing, particularly for complicated situations with numerous local optima. Simulated annealing is slower than Hill Climbing.

Tuning Parameters 

Hill Climbing has no tuning parameters. 

The beginning temperature, cooling schedule, and acceptance probability function are only a few of the tuning factors for Simulated Annealing.

Applications

Many different applications, including image processing, machine learning, and gaming, use hill climbing.

Several fields, including logistics, scheduling, and circuit design, use simulated annealing.


Last Updated : 06 Mar, 2023
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