Saúl Zapotecas-Martínez

Injecting CMA-ES into MOEA/D

MOEA/D is an aggregation-based evolutionary algorithm which has been proved extremely efficient and effective for solving multi-objective optimization problems. It is based on the idea of decomposing the original multi-objective problem into several single-objective subproblems by means of well-defined scalarizing functions. Those single-objective subproblems are solved in a cooperative manner by defining a neighborhood relation between them. This makes MOEA/D particularly interesting when attempting to plug and to leverage single-objective optimizers in a multi-objective setting. In this context, we investigate the benefits that MOEA/D can achieve when coupled with CMA-ES, which is believed to be a powerful single-objective optimizer. We rely on the ability of CMA-ES to deal with injected solutions in order to update different covariance matrices with respect to each subproblem defined in MOEA/D. We show that by cooperatively evolving neighboring CMA-ES components, we are able to obtain competitive results for different multi-objective benchmark functions.

Traffic Signal Optimization: Minimizing Travel Time and Fuel Consumption

This work integrates a multi-objective evolutionary algorithm with the multi-agent transport simulator MATSim and the comprehensive modal emission model simulator CMEM to analyze the evolutionary optimization of traffic signals minimizing travel time and fuel consumption on a real-world large scenario. We simulate the movement of 20.000 vehicles on the transport network of a 5

MONSS: A multi-objective nonlinear simplex search approach

\times

Feature Selection in Gait Classification Using Geometric PSO Assisted by SVM

8 Km

Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition

^2

A refinement mechanism to improve particle swarm optimization

area of Quito including 70 signal lights. Our aim is to clarify the nature and the extent of the conflict between these objectives. We also compare with a single-objective optimization algorithm where only travel time is optimized and evaluate the impact of the signals settings on gas emissions.