Path Relinking in Pareto Multi-objective Genetic Algorithms

Path relinking algorithms have proved their efficiency in single objective optimization. Here we propose to adapt this concept to Pareto optimization. We combine this original approach to a genetic algorithm. By applying this hybrid approach to a bi-objective permutation flow-shop problem, we show the interest of this approach. In this paper, we present first an Adaptive Genetic Algorithm dedicated to obtain a first well diversified approximation of the Pareto set. Then, we present an original hybridization with Path Relinking algorithm, in order to intensify the search between solutions obtained by the first approach. Results obtained are promising and show that cooperation between these optimization methods could be efficient for Pareto optimization

Solving the waste collection problem from a multiobjective perspective: New methodologies and case studies

Fecha de lectura Tesis Doctoral: 19 de marzo de 2018.Economía Aplicada ( Matemáticas) Resumen tesis: El tratamiento de residuos es un tema de estudio por parte de las administraciones locales a nivel mundial. Distintos factores han de tenerse en cuenta para realizar un servicio eficiente. En este trabajo se desarrolla una herramienta para analizar y resolver el problema de la recogida de residuos sólidos en Málaga. Tras un análisis exhaustivo de los datos, se aborda el problema real como un problema de rutas multiobjetivo con capacidad limitada. Para los problemas multiobjetivo, no suele existir una única solución óptima, sino un conjunto de soluciones eficientes de Pareto. Las características del problema hacen inviable su resolución de forma exacta, por lo que se aplican distintas estrategias metaheurísticas para obtener una buena aproximación. En particular, se combinan las técnicas de GRASP, Path Relinking y Variable Neighborhood Search, que son adaptadas a la perspectiva multicriterio. Se trata de una aproximación en dos fases: una primera aproximación de la frontera eficiente se genera mediante un GRASP multiobjetivo. Tres son los métodos propuestos para la primera aproximación, dos de ellos derivados de la publicación de Martí et al. (2015) y el último se apoya en la función escalarizada de logro de Wierzbicki (Wierzbicki, 1980) para distintas combinaciones de pesos. A continuación, esta aproximación es mejorada con una versión de Path Relinking o Variable Neighborhood Search, con un punto de referencia diseñado para problemas multiobjetivo. Una vez generada la aproximación de la frontera eficiente, el proceso de obtención de la solución que más se adecúa a las preferencias de los gestores se basa en el desarrollo de un método interactivo sin trade – off, derivado de la filosofía NAUTILUS (Miettinen et al. 2010). Para evitar gastos de cómputo extensos, esta metodología se apoya en una pre - computación de los elementos de la frontera eficiente

GIMO : A multi-objective anytime rule mining system to ease iterative feedback from domain experts

Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for classification problems: (1) Domain experts’ acceptance is of critical importance, and domain experts can provide valuable input, but it is hard to use this feedback. (2) Evaluating the quality of the model is not a matter of calculating AUC or accuracy. Instead, there are multiple objectives of varying importance with hard to quantify trade-offs. Furthermore, the performance of the model cannot be evaluated on a per-instance level in our case, because it shares aspects with the set cover problem. To overcome these problems, we take a holistic approach and develop a rule mining system that simplifies iterative feedback from domain experts and can incorporate the domain-specific evaluation needs. A central part of the system is a novel multi-objective anytime rule mining algorithm. The algorithm is based on the GRASP-PR meta-heuristic but extends it with ideas from several other approaches. We successfully applied the system in the industrial context. In the current article, we focus on the description of the algorithm and the concepts of the system. We make an implementation of the system available. © 2020 The Author

A Pareto-metaheuristic for a bi-objective winner determination problem in a combinatorial reverse auction

The bi-objective winner determination problem (2WDP-SC) of a combinatorial procurement auction for transport contracts comes up to a multi-criteria set covering problem. We are given a set B of bundle bids. A bundle bid b in B consists of a bidding carrier c_b, a bid price p_b, and a set tau_b of transport contracts which is a subset of the set T of tendered transport contracts. Additionally, the transport quality q_t,c_b is given which is expected to be realized when a transport contract t is executed by a carrier c_b. The task of the auctioneer is to find a set X of winning bids (X is subset of B), such that each transport contract is part of at least one winning bid, the total procurement costs are minimized, and the total transport quality is maximized. This article presents a metaheuristic approach for the 2WDP-SC which integrates the greedy randomized adaptive search procedure, large neighborhood search, and self-adaptive parameter setting in order to find a competitive set of non-dominated solutions. The procedure outperforms existing heuristics. Computational experiments performed on a set of benchmark instances show that, for small instances, the presented procedure is the sole approach that succeeds to find all Pareto-optimal solutions. For each of the large benchmark instances, according to common multi-criteria quality indicators of the literature, it attains new best-known solution sets.Pareto optimization; multi-criteria winner determination; combinatorial auction; GRASP; LNS

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201