Banca de QUALIFICAÇÃO: ISLAME FELIPE DA COSTA FERNANDES

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : ISLAME FELIPE DA COSTA FERNANDES
DATE: 30/07/2021
TIME: 08:00
LOCAL: https://meet.google.com/ohf-pcda-oww
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 haveproven to be effective in finding high-quality solutions for multi-objective combinatorialoptimization problems. Architectures provide generic functionalities and features forimplementing new hybrid algorithms capable of solving arbitrary optimization problems.Architectures based on agent intelligence and multi-agent concepts, such as learning andcooperation, give several benefits for hybridizing metaheuristics. Nevertheless, there isa lack of studies on architectures that fully explore these concepts for multi-objectivehybridization. This thesis studies a multi-agent architecture namedMO-MAHM, inspiredby Particle Swarm Optimization concepts. In theMO-MAHMarchitecture, particles areintelligent agents that learn from past experiences and move in the search space, lookingfor high-quality solutions. The main contribution of this work is to study theMO-MAHMpotential to hybridize metaheuristics for solving problems with two or more objectives.We investigate the benefits of machine learning methods for agents’ learning supportand propose a novel velocity operator for moving the agents in the search space. Theproposed velocity operator uses a path-relinking technique and decomposes the objectivespace without requiring aggregation functions. Another contribution of this thesis isan extensive survey of existing multi-objective path-relinking techniques. Due to a lackin the literature of effective multi- and many-objective path-relinking techniques, wepresent a novel decomposition-based one, referred to asMOPR/D. Experiments includethree differently structured combinatorial optimization problems with up to five objectivefunctions: 0/1 multidimensional knapsack, quadratic assignment, and spanning tree. Wecompared theMO-MAHMwith existing hybrid approaches, such as memetic algorithmsand hyper-heuristics. Statistical tests show that the architecture presents competitiveresults 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
Notícia cadastrada em: 16/06/2021 08:55
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