Clarisse Dhaenens

A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop

The resolution of workshop problems such as the Flow Shop or the Job Shop has a great importance in many industrial areas. The criteria to optimize are generally the minimization of the makespan or the tardiness. However, few are the resolution approaches that take into account those different criteria simultaneously. This paper presents an approach based on hybrid genetic algorithms adapted to the multicriteria case. Several strategies of selection and diversity maintaining are presented. Their performances are evaluated and compared using different benchmarks. A parallel model is also proposed and implemented for the hybrid metaheuristic. It allows to increase the population size and the number of generations, and then leads to better results.

Advances in Multi-Objective Nature Inspired Computing

The purpose of this book is to collect contributions that deal with the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems. Such a collection intends to provide an overview of the state-of-the-art developments in this field, with the aim of motivating more researchers in operations research, engineering, and computer science, to do research in this area. As such, this book is expected to become a valuable reference for those wishing to do research on the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems.

On the structure of multiobjective combinatorial search space: MNK-landscapes with correlated objectives

The structure of the search space explains the behavior of multiobjective search algorithms, and helps to design well-performing approaches. In this work, we analyze the properties of multiobjective combinatorial search spaces, and we pay a particular attention to the correlation between the objective functions. To do so, we extend the multiobjective NK-landscapes in order to take the objective correlation into account. We study the co-influence of the problem dimension, the degree of non-linearity, the number of objectives, and the objective correlation on the structure of the Pareto optimal set, in terms of cardinality and number of supported solutions, as well as on the number of Pareto local optima. This work concludes with guidelines for the design of multiobjective local search algorithms, based on the main fitness landscape features.

K-PPM: A new exact method to solve multi-objective combinatorial optimization problems

In this paper we propose an exact method able to solve multi-objective combinatorial optimization problems. This method is an extension, for any number of objectives, of the 2-Parallel Partitioning Method (2-PPM) we previously proposed. Like 2-PPM, this method is based on splitting of the search space into several areas, leading to elementary searches. The efficiency of the proposed method is evaluated using a multi-objective flow-shop problem.

Parallel partitioning method (PPM): A new exact method to solve bi-objective problems

In this paper, we propose a new exact method, called the parallel partitioning method (PPM), able to solve efficiently bi-objective problems. This method is based on the splitting of the search space into several areas leading to elementary exact searches. We compare this method with the well-known two-phase method (TPM). Experiments are carried out on a bi-objective permutation flowshop problem (BOFSP). During experiments the proposed PPM is compared with two versions of TPM: the basic TPM and an improved TPM dedicated to scheduling problems. Experiments show the efficiency of the new proposed method.

An exact parallel method for a bi-objective permutation flowshop problem

In this paper, we propose a parallel exact method to solve bi-objective combinatorial optimization problems. This method has been inspired by the two-phase method which is a very general scheme to optimally solve bi-objective combinatorial optimization problems. Here, we first show that applying such a method to a particular problem allows improvements. Secondly, we propose a parallel model to speed up the search. Experiments have been carried out on a bi-objective permutation flowshop problem for which we also propose a new lower bound.

Using datamining techniques to help metaheuristics: a short survey

Hybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics and datamining through a short survey that enumerates the different opportunities of such combinations based on literature examples.

Solving a dial-a-ride problem with a hybrid evolutionary multi-objective approach: Application to demand responsive transport

Demand responsive transport allows customers to be carried to their destination as with a taxi service, provided that the customers are grouped in the same vehicles in order to reduce operational costs. This kind of service is related to the dial-a-ride problem. However, in order to improve the quality of service, demand responsive transport needs more flexibility. This paper tries to address this issue by proposing an original evolutionary approach. In order to propose a set of compromise solutions to the decision-maker, this approach optimizes three objectives concurrently. Moreover, in order to intensify the search process, this multi-objective evolutionary approach is hybridized with a local search. Results obtained on random and realistic problems are detailed to compare three state-of-the-art algorithms and discussed from an operational point of view.

Clustering Nominal and Numerical Data: A New Distance Concept for a Hybrid Genetic Algorithm

As intrinsic structures, like the number of clusters, is, for real data, a major issue of the clustering problem, we propose, in this paper, CHyGA (Clustering Hybrid Genetic Algorithm) an hybrid genetic algorithm for clustering. CHyGA treats the clustering problem as an optimization problem and searches for an optimal number of clusters characterized by an optimal distribution of instances into the clusters. CHyGA introduces a new representation of solutions and uses dedicated operators, such as one iteration of K-means as a mutation operator. In order to deal with nominal data, we propose a new definition of the cluster center concept and demonstrate its properties. Experimental results on classical benchmarks are given.

A multicriteria genetic algorithm to analyze microarray data

Knowledge discovery from DNA microarray data has become an important research area for biologists. Association rules is an important task of knowledge discovery that can be applied to the analysis of gene expression in order to identify patterns of genes and regulatory network. Association rules discovery may be modeled as an optimization problem. We propose a multicriteria model for association rules problem and present a genetic algorithm designed to deal with association rules on DNA microarray data, in order to obtain associations between genes. Hence, we expose the main features of the proposed genetic algorithm. We emphasize on specificities for the association rule problem (encoding, mutation and crossover operators) and on its multicriteria aspects. Results are given for real datasets.