Papers by Leonardo Bezerra
Automatic Component-Wise Design of Multi-Objective Evolutionary Algorithms
IEEE Transactions on Evolutionary Computation, 2015
To DE or Not to DE? Multi-objective Differential Evolution Revisited from a Component-Wise Perspective
Lecture Notes in Computer Science, 2015
Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms
Lecture Notes in Computer Science, 2015
Lecture Notes in Computer Science, 2014
Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research e↵ort o... more Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research e↵ort 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 components from di↵erent MOEAs. The number of available algorithmic components is large, though, and an algorithm designer working on a specific application cannot analyze all possible combinations. In this paper, we investigate the automatic design of MOEAs, extending previous work on other multi-objective metaheuristics. We conduct our tests on four variants of the permutation flowshop problem that di↵er on the number and nature of the objectives they consider. Moreover, given the di↵erent characteristics of the variants, we also investigate the performance of an automatic MOEA designed for the multi-objective PFSP in general. Our results show that the automatically designed MOEAs are able to outperform six traditional MOEAs, confirming the importance and e ciency of this design methodology.
An analysis of local search for the bi-objective bidimensional knapsack problem
Many studies in the literature have applied multi-objective evolutionary algorithms (MOEAs) to mu... 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 maintaining the population. In this paper, we compare seven MOEAs from the literature on three bi-objective and one tri-objective variants of the permutation flowshop problem. The overall best and worst performing MOEAs are then used for an iterative analysis, where each of the main components of these algorithms is analyzed to determine their contribution to the algorithms' performance. Results confirm some previous knowledge on MOEAs, but also provide new insights. Concretely, some components only work well when simultaneously used. Furthermore, a new best-performing algorithm was discovered for one of the problem variants by replacing the diversity component of the best performing algorithm (NSGA-II) with the diversity component from PAES.
FAITH: A Desktop Virtual Reality System for Fingerspelling
GRACE: A Generational Randomized ACO for the Multi-objective Shortest Path Problem
The Multi-objective Shortest Path Problem (MSP) is a widely studied NP-Hard problem. A few exact ... 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, since no comparisons among them are presented in the literature. In this paper an Ant Colony Optimization (ACO) algorithm, called GRACE, is proposed for the MSP. The proposed approach is compared to the well-known evolutionary algorithm NSGA-II. Furthermore, GRACE is compared to another ACO algorithm proposed previously for the MSP. Results of a computational experiment with eighteen instances, with three objectives each, show that the proposed approach is able to produce high quality results for the tested instances.
GRACE: A Generational Randomized ACO for the Multi-objective Shortest Path Problem
Lecture Notes in Computer Science, 2011
Abstract. The Multi-objective Shortest Path Problem (MSP) is a widely studied NP-Hard problem. A ... more Abstract. 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 ...
Analyzing the impact of MOACO components: An algorithmic study on the multi-objective shortest path problem
Expert Systems with Applications, 2013
ABSTRACT Multi-objective Ant Colony Optimization (MOACO) algorithms have been successfully applie... more ABSTRACT Multi-objective Ant Colony Optimization (MOACO) algorithms have been successfully applied to several multi-objective combinatorial optimization problems (MCOP) over the past decade. Recently, we proposed a MOACO algorithm named GRACE for the multi-objective shortest path (MSP) problem, confirming the efficiency of such metaheuristic for this MCOP. In this paper, we investigate several extensions of GRACE, proposing several single and multi-colony variants of the original algorithm. All variants are compared on the original set of instances used for proposing GRACE. The best-performing variants are also assessed using a new benchmark containing 300 larger instances with three different underlying graph structures. Experimental evaluation shows one of the variants to produce better results than the others, including the original GRACE, thus improving the state-of-the-art of MSP.
Analyzing the impact of MOACO components: An algorithmic study on the multi-objective shortest path problem
Multi-objective Ant Colony Optimization (MOACO) algorithms have been successfully applied to seve... more Multi-objective Ant Colony Optimization (MOACO) algorithms have been successfully applied to several multi-objective combinatorial optimization problems (MCOP) over the past decade. Recently, we proposed a MOACO algorithm named GRACE for the multi-objective shortest path (MSP) problem, confirming the efficiency of such metaheuristic for this MCOP. In this paper, we investigate several extensions of GRACE, proposing several single and multi-colony variants of the original algorithm. All variants are compared on the original set of instances used for proposing GRACE. The best-performing variants are also assessed using a new benchmark containing 300 larger instances with three different underlying graph structures. Experimental evaluation shows one of the variants to produce better results than the others, including the original GRACE, thus improving the state-of-the-art of MSP.► A MOACO algorithm from the state-of-the-art of the MSP is extended. ► Seven novel variants of the original algorithm are proposed. ► Several MOACO components from the literature are tested on the MSP. ► A new benchmark is created and filtered with the help of an exact algorithm. ► One of the novel variants outperforms the original algorithm on both benchmarks.
Multi-objective ant colony optimization (MOACO) algorithms have shown promising results for vario... 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 new state-of-the-art MOACO algorithms for the bi-objective traveling salesman problem. In this paper, we apply this approach to the bi-objective bidimensional knapsack problem (bBKP) to prove its generality and power. As a first step, we tune and improve the performance of four MOACO algorithms that have been earlier proposed for the bBKP. In a second step, we configure the full MOACO framework and show that the automatically configured MOACO framework outperforms all previous MOACO algorithms for the bBKP as well as their improved variants.
optimization-online.org
The use of metaheuristics in Multi-objective Combinatorial Optimization, particularly Ant Colony ... more The use of metaheuristics in Multi-objective Combinatorial Optimization, particularly Ant Colony Optimization (ACO), has grown recently. This paper proposes an approach where multi-species ants compete for food resources. Each species has its own search strategy and do not access pheromone information of other species. As in nature, successful ant populations are allowed to grow, whereas the others shrink. This approach is applied to the Multi-objective Shortest Path Problem and shows to inherit the behavior of succesful strategies from different types of problems. It is also compared to an existing ACO and to NSGA-II. Results show that the proposed approach is able to produce significantly better approximation sets than other methods.
GRACE: a generational randomized ACO for the multi-objective shortest path problem
Evolutionary Multi-Criterion …, Jan 1, 2011
Abstract. The Multi-objective Shortest Path Problem (MSP) is a widely studied NP-Hard problem. A ... more Abstract. 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 ...
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Papers by Leonardo Bezerra