Leonardo C. T. Bezerra

Learn More
Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research effort over the past two decades. Traditionally, these MOEAs have been seen as monolithic units, and their study was focused on comparing them as blackboxes. More recently, a component-wise view of MOEAs has emerged, with flexible frameworks combining algorithmic(More)
This supplementary material provides all the data required for the analysis of the main paper that had been omitted for brevity. Tables I and II list the numerical parameters used by the traditional MOEAs when tuned for 10 000 function evaluations or 1-minute runtime, respectively. Figures 2–12 show the performances of all algorithms in all scenarios when(More)
Multi-objective ant colony optimization (MOACO) algorithms have shown promising results for various multi-objective problems, but they also offer a large number of possible design choices. Often, exploring all possible configurations is practically infeasible. Recently, the automatic configuration of a MOACO framework was explored and was shown to result in(More)
Many studies in the literature have applied multi-objective evolutionary algorithms (MOEAs) to multi-objective combinatorial optimization problems. Few of them analyze the actual contribution of the basic algorithmic components of MOEAs. These components include the underlying EA structure, the fitness and diversity operators, and their policy for(More)
The Multi-objective Shortest Path Problem (MSP) is a widely studied NP-Hard problem. A few exact algorithms were already proposed to solve this problem, however none is able to solve large instances with three or more objectives. Recently, some metaheuristics have been proposed for the MSP, but little can be said about their efficiency regarding each other,(More)
Differential evolution (DE) research for multi-objective optimization can be divided into proposals that either consider DE as a stand-alone algorithm, or see DE as an algorithmic component that can be coupled with other algorithm components from the general evolutionary multiobjective optimization (EMO) literature. Contributions of the latter type have(More)
A main focus of current research on evolutionary multiobjective optimization (EMO) is the study of the effectiveness of EMO algorithms for problems with many objectives. Among the several techniques that have led to the development of more effective algorithms, decomposition and component-wise design have presented particularly good results. But how do they(More)
The inverted generational distance (IGD) is a metric for assessing the quality of approximations to the Pareto front obtained by multi-objective optimization algorithms. The IGD has become the most commonly used metric in the context of many-objective problems, i.e., those with more than three objectives. The averaged Hausdorff distance and IGD are variants(More)
Local search techniques are increasingly often used in multiobjective combinatorial optimization due to their ability to improve the performance of metaheuristics. The efficiency of multi-objective local search techniques heavily depends on factors such as (i) neighborhood operators, (ii) pivoting rules and (iii) bias towards good regions of the objective(More)
Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a large number of algorithms and a rich literature on performance assessment tools to evaluate and compare them. Yet, newly proposed MOEAs are typically compared against very few, often a decade older MOEAs. One reason for this apparent contradiction is the lack(More)