# Chaotic Quantum Behaved Particle Swarm Optimization for Multiobjective Optimization in Habitability Studies

## TL;DR

This paper enhances the Quantum-behaved Particle Swarm Optimization algorithm to better solve multiobjective habitability problems, demonstrating improved convergence and optimization performance in economic-based models.

## Contribution

The paper introduces modifications to the Quantum-behaved Particle Swarm Optimization algorithm to address premature convergence in multiobjective habitability optimization.

## Key findings

- Proposed algorithm modifications improve convergence behavior.
- Enhanced optimization results on Cobb Douglas habitability function.
- Demonstrated effectiveness in multiobjective economic models.

## Abstract

In this paper, based on the Quantum-behaved Particle Swarm Optimization algorithm, we evolve the algorithm to optimize a multiobjective optimization problem, namely the Cobb Douglas Habitability function which is based on CES production functions in Economics. We also propose some changes to the Quantum-behaved Particle Swarm Optimization algorithm to mitigate the problem of the algorithm prematurely converging and show the results of the proposed changes to the Quantum-behaved Particle Swarm Optimization.