Marco César Goldbarg

A Memetic Algorithm for the Heterogeneous Fleet Vehicle Routing Problem

Abstract The Heterogeneous Fleet Vehicle Routing Problem is a variant of the classical Vehicle Routing Problem in which customers are served by a heterogeneous fleet of vehicles with various capacities, fixed and variable costs. Due to its complexity, no exact algorithm is known for this problem. This paper describes a genetic algorithm hybridized with two known heuristic methods for this problem. Hybrid genetic algorithms are often referred as memetic algorithms. Computational experiments presenting the performance of the proposed algorithm concerning quality of solution and processing time are reported. On a set of benchmark instances, it produces high-quality solutions, including several new best-known solutions.

Particle swarm for the traveling salesman problem

This paper presents a competitive Particle Swarm Optimization algorithm for the Traveling Salesman Problem, where the velocity operator is based upon local search and path-relinking procedures. The paper proposes two versions of the algorithm, each of them utilizing a distinct local search method. The proposed heuristics are compared with other Particle Swarm Optimization algorithms presented previously for the same problem. The results are also compared with three effective algorithms for the TSP. A computational experiment with benchmark instances is reported. The results show that the method proposed in this paper finds high quality solutions and is comparable with the effective approaches presented for the TSP.

New VNS heuristic for total flowtime flowshop scheduling problem

This paper presents a new Variable Neighborhood Search (VNS) approach to the permutational flowshop scheduling with total flowtime criterion, which produced 29 novel solutions for benchmark instances of the investigated problem. Although many hybrid approaches that use VNS do exist in the problems literature, no experimental study was made examining distinct VNS alternatives or their calibration. In this study six different ways to combine the two most used neighborhoods in the literature of the problem, named job interchange and job insert, are examined. Computational experiments were carried on instances of a known dataset and the results indicate that one of the six tested VNS methods, named VNS4, is quite effective. It was compared to a state-of-the-art evolutionary approach and statistical tests applied on the computational results indicate that VNS4 outperforms its competitor on most benchmark instances.

Transgenetic algorithm for the Traveling Purchaser Problem

In this paper an evolutionary algorithm is presented for the Traveling Purchaser Problem, an important variation of the Traveling Salesman Problem. The evolutionary approach proposed in this paper is called transgenetic algorithm. It is inspired on two significant evolutionary driving forces: horizontal gene transfer and endosymbiosis. The performance of the algorithm proposed for the investigated problem is compared with other recent works presented in the literature. Computational experiments show that the proposed approach is very effective for the investigated problem with 17 and 9 new best solutions reported for capacitated and uncapacitated instances, respectively.

Logistic regression for parameter tuning on an evolutionary algorithm

The investigation of the parameters for which algorithms have their best performance is crucial when working with metaheuristics. This paper proposes the utilization of logistic regression, a statistical tool, for parameter tuning of an evolutionary algorithm called ProtoG. To illustrate the ideas proposed in this work, the algorithm is applied to the traveling salesman problem.

Transgenética computacional: uma aplicação ao problema quadrático de alocação

This paper introduces the Computational Transgenetic approach. The metaphor is based on the use of memetic pieces of information and on the extra and intracellular flows to design and accomplish genetic manipulation in the chromosomes of a given population of an evolutionary algorithm. The research develops two algorithmic approaches. The first algorithmic approach uses both the intra and extra-cellular flows to guide an evolutionary search process. The second one uses, uniquely, intracellular manipulation. Computational Transgenetic agents are presented. Properties resulting from chromosome x agent interactions, similar to the natural immunologic process, are examined. Finally, the paper reports the results of computational experiments of applying both techniques to the Quadratic Assignment Problem.

Memetic algorithm for the Traveling Car Renter Problem: an experimental investigation

The Traveling Car Renter Problem (CaRS) is a generalization of the Traveling Salesman Problem where the tour can be decomposed into contiguous paths that are travelled by different rented cars. When a car is rented in a city and delivered in another, the renter must pay a fee to return the car to its home city. Given a graph G and cost matrices associated to cars available for rent, the problem consists in determining the minimum cost Hamiltonian cycle in G, considering also the cost paid to deliver a car in a city different from the one it was rented. The latter cost is added to the cost of the edges in the cycle. This paper describes the general problem and some related variants. Two metaheuristic approaches are proposed to deal with CaRS: GRASP hybridized with Variable Neighborhood Descent and Memetic Algorithm. A set of benchmark instances is proposed for the new problem which is utilized on the computational experiments. The algorithms are tested on a set of 40 Euclidean and non-Euclidean instances.

On the Biobjective Adjacent Only Quadratic Spanning Tree Problem

Abstract The adjacent only quadratic minimum spanning tree problem is an NP-hard version of the minimum spanning tree where the costs of interaction effects between every pair of adjacent edges are included in the objective function. This paper addresses the biobjective version of this problem. A Pareto local search algorithm is proposed. The algorithm is applied to a set of 108 benchmark instances. The results are compared to the optimal Pareto front generated by a branch and bound algorithm, which is a multiobjective adaptation of a well known algorithm for the mono-objective case.

Hybridizing VNS and path-relinking on a particle swarm framework to minimize total flowtime

This paper presents a new hybridization of VNS and path-relinking on a particle swarm framework for the Permutational Flowshop Scheduling Problem with total flowtime criterion. The operators of the proposed particle swarm are based on path-relinking and variable neighborhood search methods. The performance of the new approach was tested on the benchmark suit of Taillard, and five novel solutions for the benchmark suit are reported. The results were compared against results obtained using methods from literature. Statistical analysis favors the new particle swarm approach over the other methods tested.

Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms

This chapter introduces a class of evolutionary algorithms whose inspiration comes from living processes where cooperation is the main evolutionary strategy. The proposed technique is called Transgenetic Algorithms and is based on two recognized driving forces of evolution: the horizontal gene transfer and the endosymbiosis. These algorithms perform a stochastic search simulating endosymbiotic interactions between a host and a population of endosymbionts. The information exchanging between the host and ensosymbionts is intermediated by agents, called transgenetic vectors, who are inspired on natural mechanisms of horizontal gene transfer. The proposed approach is described and a didactic example with the well-known Traveling Salesman Problem illustrates its basic components. Applications of the proposed technique are reported for two NP-hard combinatorial problems: the Traveling Purchaser Problem and the Bi-objective Minimum Spanning Tree Problem.