Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems

TL;DR

This paper investigates the fundamental aspects of particle swarm optimization (PSO), demonstrating its versatility and robustness across different optimization problems, including benchmark functions, engineering challenges, and scheduling tasks.

Contribution

It provides a comprehensive study of PSO's core principles and applies a consistent algorithm to diverse problems, highlighting its adaptability without extensive parameter tuning.

Findings

PSO outperforms traditional methods in computational efficiency.

The same PSO algorithm effectively solves different types of optimization problems.

PSO demonstrates robustness and flexibility across various applications.

Abstract

The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature. While particle swarm optimizers share such advantages, they outperform evolutionary algorithms in that they require lower computational cost and easier implementation, involving no operator design and few coefficients to be tuned. However, even marginal variations in the settings of these coefficients greatly influence the dynamics of the swarm. Since this paper does not intend to study their tuning, general-purpose settings are taken from previous studies, and virtually the same algorithm is used to optimize a variety of notably different problems. Thus, following a review of the paradigm, the algorithm is tested on a set of benchmark functions and engineering problems taken from the literature. Later, complementary lines of code are incorporated to adapt the method to combinatorial optimization as it occurs in scheduling problems, and a real case is solved using the same optimizer with the same settings. The aim is to show the flexibility and robustness of the approach, which can handle a wide variety of problems.