Banca de DEFESA: ISLAME FELIPE DA COSTA FERNANDES

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : ISLAME FELIPE DA COSTA FERNANDES
DATE: 15/06/2022
TIME: 14:00
LOCAL: https://meet.google.com/uoj-wecr-vwf
TITLE:

Hybridizing Metaheuristics for Multi and Many-objective Problems in a Multi-agent Architecture

KEY WORDS:

Metaheuristic hybridization. Multi-objective optimization. Agent intelligence.Multi-agent paradigm. Decomposition.

PAGES: 182
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Sistemas de Computação
SPECIALTY: Arquitetura de Sistemas de Computação
SUMMARY:

Hybrid algorithms combine the best features of individual metaheuristics. They have proven to find high-quality solutions for multi-objective optimization problems. Architectures provide generic functionalities and features for implementing new hybrid algorithms to solve arbitrary optimization problems. Architectures based on agent intelligence and multi-agent concepts, such as learning and cooperation, give several benefits for hybridizing metaheuristics. Nevertheless, there is a lack of studies on architectures that fully explore these concepts for multi-objective hybridization. This thesis studies a multi-agent architecture named MO-MAHM, inspired by Particle Swarm Optimization concepts. In the MO-MAHM, particles are intelligent agents that learn from past experiences and move in the search space, looking for high-quality solutions. The main contribution of this work is to study the MO-MAHM potential to hybridize metaheuristics for solving combinatorial optimization problems with two or more objectives. We investigate the benefits of machine learning methods for agents' learning support and propose a novel velocity operator for moving the agents in the search space. The proposed velocity operator uses a path-relinking technique and decomposes the objective space without requiring aggregation functions. Another contribution of this thesis is an extensive survey of existing multi-objective path-relinking techniques. Due to a lack in the literature of effective multi- and many-objective path-relinking techniques, we present a novel decomposition-based one, referred to as MOPR/D. Experiments comprise three differently structured combinatorial optimization problems with up to five objective functions: 0/1 multidimensional knapsack, quadratic assignment, and spanning tree. We compared the MO-MAHM with existing hybrid approaches, such as memetic algorithms and hyper-heuristics. Statistical tests show that the architecture presents competitive results regarding the quality of the approximation sets and solution diversity.

BANKING MEMBERS:
Presidente - 1201268 - ELIZABETH FERREIRA GOUVEA GOLDBARG
Interno - 1149561 - MARCO CESAR GOLDBARG
Interna - 2859606 - SILVIA MARIA DINIZ MONTEIRO MAIA
Externa à Instituição - MYRIAM REGATTIERI DE BIASE DA SILVA DELGADO - UTFPR
Externa à Instituição - THATIANA CUNHA NAVARRO DE SOUZA - UFERSA