Enhancing Adaptive Particle Swarm Optimization Based on Human Social Learning with Human Learning Strategies for the Traveling Salesman Problem

Yusti Qomah; Bib Paruhum Silalahi; Toni Bakhtiar

doi:10.31764/jtam.v9i4.31466

Enhancing Adaptive Particle Swarm Optimization Based on Human Social Learning with Human Learning Strategies for the Traveling Salesman Problem

Yusti Qomah, Bib Paruhum Silalahi, Toni Bakhtiar

Abstract

Particle Swarm Optimization (PSO) is a widely used metaheuristic approach for solving optimization problems. Recent developments in this field involve the adaptation of human learning behaviors to enhance algorithmic performance. One such adaptation is the Adaptive Particle Swarm Optimization based on Human Social Learning (APSO-HSL), a variant of PSO that incorporates human-inspired learning strategies. This study aims to enhance the performance of APSO-HSL on the Traveling Salesman Problem (TSP) by incorporating additional human learning strategies. The proposed algorithm, named Modified Adaptive Particle Swarm Optimization–Human Learning Strategies (MAPSO-HLS), integrates learning mechanisms from Human Learning Optimization (HLO), including individual, random, and social learning. This research is classified as applied research and algorithmic experimentation, focusing on the development and modification of a metaheuristic algorithm to solve a well-known combinatorial optimization problem. Benchmark datasets from the Traveling Salesman Problem Library (TSPLIB) are used for evaluation, and all computations and experiments are implemented in Python. The performance of MAPSO-HLS is compared with the original APSO-HSL in terms of shortest distance, convergence rate, and population diversity. A comparison of the shortest distances was conducted using exact solutions and evaluated through percentage deviation. The results show that MAPSO-HLS produces more accurate solutions than APSO-HSL. Convergence analysis reveals that MAPSO-HLS converges faster toward lower objective values. Its advantage is further supported by the diversity analysis, where the diversity curves indicate a better balance between exploration and exploitation.

Keywords

Particle Swarm Optimization; Human Learning Optimization; Traveling Salesman Problem; Operations Research.

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DOI: https://doi.org/10.31764/jtam.v9i4.31466