On the Study and Development of Multi-method Multi-objective Algorithms

• Grant number: 23/03726-4
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: May 01, 2023
• End date: April 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: João Paulo Papa
• Grantee: Douglas Rodrigues
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Metaheuristic optimization algorithms have been used to address several problems in engineering, biology, and physics, among others, given that many of these problems can be modeled as optimization tasks. Such metaheuristic methods are inspired by natural processes, such as the social dynamics of some animals in the search for food and survival or even physical phenomena. Although these techniques are commonly applied to solve single-objective problems, they can also be used to solve multiobjective tasks, where the Pareto-optimal frontier concept replaces the idea of a unique global optimal solution. However, numerous multiobjective optimization algorithms have been proposed over the years, which are designed to cover a large spectrum of problems within a specific domain. However, it is known that no single algorithm can solve all problems, and choosing the most suitable one for a given task, i.e., that maximizes the results, is a duty that requires the knowledge of an expert. A way around this situation is to use multimethod algorithms, i.e., the optimization task is divided between a set of metaheuristic algorithms that seek to solve the problem simultaneously to maximize the quality of the solution. The advantage and benefit of this approach are exploring the strengths of each algorithm used as a component to suppress its weaknesses. This project aims to develop multimethod and multiobjective algorithms for optimization problems and test them regarding the quality of the solutions found and performance analysis in different domains.

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

LUCAS, THIAGO JOSE; PASSOS JUNIOR PASSOS, LEANDRO APARECIDO; RODRIGUES, DOUGLAS; JODAS, DANILO; PAPA, JOAO PAULO; PONTARA DA COSTA, KELTON AUGUSTO; SCHERER, RAFAL. Ensemble Diversity Pruning on Cybersecurity: Optimizing Intrusion Detection Systems. 2024 31ST INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING, IWSSIP 2024, v. N/A, p. 6-pg., 2024-01-01. (23/03726-4, 23/14427-8)

JODAS, DANILO SAMUEL; GARCIA, GABRIEL LINO; PAIOLA, PEDRO HENRIQUE; RIBEIRO MANESCO, JOAO RENATO; PAPA, JOAO PAULO. Impact of Quantization on Large Language Models for Portuguese Classification Tasks. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2024, PT I, v. 15368, p. 15-pg., 2025-01-01. (13/07375-0, 23/14427-8, 19/07665-4, 23/01374-3, 23/03726-4, 23/10823-6, 24/00789-8)

LAMKOWSKI, PEDRO; RODRIGUES, DOUGLAS; PASSOS, LEANDRO A.; PAPA, JOAO P.; ALMEIDA, JURANDY. Neural Architecture Search for Enhancing Action Video Recognition in Compressed Domains. 2024 31ST INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING, IWSSIP 2024, v. N/A, p. 7-pg., 2024-01-01. (23/03726-4, 19/07665-4, 13/07375-0, 23/14427-8)

CRIVELARO, MATHEUS GOES; RODRIGUES, DOUGLAS; GIFALLI, ANDRE; PAPA, JOAO PAULO; GONZALES, CARLOS GUILHERME; DE SOUZA, ANDRE NUNES; DA SILVA, GUSTAVO VINICIUS; SILVEIRA NETO, ERASMO. Denoising Autoencoder for Partial Discharge Identification in Instrument Transformers. 2024 31ST INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING, IWSSIP 2024, v. N/A, p. 7-pg., 2024-01-01. (23/03726-4, 19/07665-4, 13/07375-0, 23/14427-8)