Optimizing LPB Algorithms using Simulated Annealing

2024-12-22

AI Summary

To address the slow convergence and susceptibility to local optima inherent in the Learning-based Population-Based (LPB) algorithm for learner behavior optimization, this paper proposes LPBSA—a novel hybrid framework integrating Simulated Annealing (SA). Our approach innovatively embeds the Metropolis acceptance criterion into the LPB population evolution process, enabling dynamic selection of high-quality individuals between crossover and mutation operations. Additionally, we introduce a population stratification mechanism—partitioning individuals into “Good” and “Bad” subpopulations—to jointly enhance population diversity and global search capability. Extensive experiments on diverse benchmark functions demonstrate that LPBSA significantly outperforms canonical Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the original LPB: it achieves an average 23% acceleration in convergence speed and a 17% improvement in solution accuracy. These results validate LPBSA’s effectiveness and robustness in tackling complex, high-dimensional optimization problems.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• LPB Algorithm
• Performance Improvement
• Efficiency Enhancement

Innovation

• LPBSA Algorithm
• Simulated Annealing
• Enhanced Learning Behavior