Optimizing LPB Algorithms using Simulated Annealing

TL;DR

This paper introduces LPBSA, an enhanced version of LPB that employs simulated annealing to optimize performance, outperforming other algorithms like GA and PSO in complex problem-solving.

Contribution

The paper presents a novel LPBSA algorithm that integrates simulated annealing with population-based methods, improving efficiency and solution quality over existing algorithms.

Findings

LPBSA outperforms GA, PSO, and LPB in complex problems.

The algorithm improves population quality through SA-based selection.

Enhanced efficiency and validated performance of LPBSA.

Abstract

Learner Performance-based Behavior using Simulated Annealing (LPBSA) is an improvement of the Learner Performance-based Behavior (LPB) algorithm. LPBSA, like LPB, has been proven to deal with single and complex problems. Simulated Annealing (SA) has been utilized as a powerful technique to optimize LPB. LPBSA has provided results that outperformed popular algorithms, like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and even LPB. This study outlines the improved algorithm's working procedure by providing a main population and dividing it into Good and Bad populations and then applying crossover and mutation operators. When some individuals are born in the crossover stage, they have to go through the mutation process. Between these two steps, we have applied SA using the Metropolis Acceptance Criterion (MAC) to accept only the best and most useful individuals to be used in the next iteration. Finally, the outcomes demonstrate that the population is enhanced, leading to improved efficiency and validating the performance of LPBSA.