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

Design of multi-objective evolutionary algorithms: application to the flow-shop scheduling problem

Multi-objective optimization using evolutionary algorithms has been extensively studied in the literature. We propose formal methods to solve problems appearing frequently in the design of such algorithms. To evaluate the effectiveness of the introduced mechanisms, we apply them to the flow-shop scheduling problem. We propose a dynamic mutation Pareto genetic algorithm (GA) in which different genetic operators are used simultaneously in an adaptive manner, taking into account the history of the search. We present a diversification mechanism which combines sharing in the objective space as well as in the decision space, in which the size of the niche is automatically calculated. We also propose a hybrid approach which combines the Pareto GA with local search. Finally, we propose two performance indicators to evaluate the effectiveness of the introduced mechanism

A fast Reoptimization approach for the dynamic technician routing and scheduling problem

The Technician Routing and Scheduling Problem (TRSP) consists in routing staff to serve requests for service, taking into account time windows, skills, tools, and spare parts. Typical applications include maintenance operations and staff routing in telecoms, public utilities, and in the health care industry. In this paper we tackle the Dynamic TRSP (D-TRSP) in which new requests appear over time. We propose a fast reoptimization approach based on a parallel Adaptive Large Neighborhood Search (RpALNS) able to achieve state-of-the-art results on the Dynamic Vehicle Routing Problem with Time Windows. In addition, we solve a set of randomly generated D-TRSP instances and discuss the potential gains with respect to a heuristic modeling a human dispatcher solution

Multi-objective optimization using metaheuristics: non-standard algorithms

In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems has become an active research area. Solving this kind of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfils the requirements of convergence to the true Pareto front and uniform diversity. Most of the studies on metaheuristics for multi-objective optimization are focused on Evolutionary Algorithms, and some of the state-of-the-art techniques belong this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi-objective optimization. In particular, we focus on non-evolutionary metaheuristics, hybrid multi-objective metaheuristics, parallel multi-objective optimization and multi-objective optimization under uncertainty. We analyze these issues and discuss open research lines

Metaheuristics for multiobjective combinatorial optimization: review and recent issues

Ce document présente certaines voies prometteuses, émergent actuellement dans le domaine de l\u27optimisation combinatoire multiobjectif. Résoudre de tels problèmes implique notamment la recherche d\u27un ensemble de solutions dites Pareto optimales\u27\u27. Ces solutions sont les meilleurs compromis réalisable pour les différents objectifs à optimiser pour le problème étudié, le but étant de découvrir un ensemble de bonne qualité en terme de convergence, mais également en terme de diversité des compromis proposés. Dans le domaine des métaheuristiques, il existe plusieurs état de l\u27art du domaine traitant principalement des algorithmes évolutionnaires. Nous nous proposons ici d\u27enrichir ces études en relevant des approches récentes qui ont fait preuve d\u27innovation mais également de bons résultats. Aprés une introduction générale et avoir proposé une classification des méthodes usuelles, nous nous proposons de discuter des orientations récentes et prometteuses de la recherche dans ce domaine. Les approches étudiées sont l\u27application des métaheuristues mono-objectif récentes au cadre multi-objectif, les métaheuristiques hybrides, les métaheuristiques multi-objectif et le parallèlisme, et enfin l\u27optimisation multi-objectif sous incertitude. Nous concluerons par une discussion et quelques questions ouvertes