Using multi-agent systems and social choice theory to design hyper-heuristics for multi-objective optimization problems.

• Ano de defesa: 2022
• Autor(a) principal: Carvalho, Vinicius Renan de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Agent-based voting; Algoritmos; Borda count method; Copeland method; Heurística; Hyper-heuristics; Kemeny-young method; Multi-agent systems; Multi-objective optimization; Sistemas multiagentes; Social choice theory; Teoria da escolha social; Votação baseada em agentes
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-16032022-105222/

The majority of the most effective and efficient algorithms for multi-objective optimization are based on Evolutionary Computation. However, choosing the most appropriate algorithm to solve a certain problem is not trivial and often requires a time-consuming trial process. As an emerging area of research, hyper-heuristics investigates various techniques to detect the best low-level heuristic while the optimization problem is being solved. On the other hand, agents are autonomous component responsible for watching an environment and perform some actions according to their perceptions. In this context, agent-based techniques seem suitable for the design of hyper-heuristics. There are several hyper-heuristics proposed for controlling lowlevel heuristics, but only a few of them are focused on selecting multi-objective optimization algorithms (MOEA). This work presents an agent-based hyper-heuristic for choosing the best multi-objective evolutionary algorithm. Based on Social Choice Theory, the proposed framework performs a cooperative voting procedure, considering a set of quality indicator voters, to define which algorithm should generate more offspring along to the execution. Comparative performance analysis was performed across several benchmark functions and real-world problems. Results showed the proposed approach was very competitive both against the best MOEA for each given problem and against state-of-art hyper-heuristics.