DOCSLIB.ORG

Parallel Metaheuristics: from Design to Implementation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Parallel Metaheuristics: from Design to Implementation Laboratoire d’Informatique Fondamentale de Lille Parallel Metaheuristics: from Design to Implementation Prof. E-G. Talbi Parallel Cooperative Optimization Research Group Motivations High-dimensional and complex optimization problems in many areas of industrial concern → Telecommunication, genomics, transportation, engineering design, … Metaheuristics reduce the computational complexity of search BUT Parallel issue remains important: More and more complex metaheuristics Huge search space CPU or memory intensive of the objective function Motivations Rapid development of technology Processors: Multi-core processors, … Networks (LAN & WAN): Myrinet, Infiniband, Optical networks, … Data storage Increasing ratio performance / cost Goals Speedup the search Æ real-time and interactive optimization methods, dynamic optimisation, … Improve quality of solutions Æ cooperation, better even on a single processor Improve robustness (different instances, design space) Solving large problems Æ instances, accuracy of mathematics models Taxonomy of metaheuristics Metaheuristics Single Population solution Hill Simulated Tabu Evol. Scatter Climbing annealing search algorithms search Solution-based metaheuristics: Hill Climbing, Simulated Annealing, Tabu Search, VNS, … Population-based metaheuristics: Evolutionary Algorithms, Scatter Search, Swarm optimization, … Outline Parallel Metaheuristics: Design issues Parallel Metaheuristics: Implementation issues Hardware platforms, Programming models Performance evaluation Adaptation to Multi-objective Optimization Software frameworks IIlustration : Network design problem Parallel Models for Metaheuristics An unified view for single-based metaheuristics and population based metaheuristics Three major hierarchical models: Algorithm-Level: Independent/Cooperative self-contained metaheuristics Iteration-Level: parallelization of a single step of the metaheuristic (based on distribution of the handled solutions) Solution-Level: parallelization of the processing of a single solution Parallel Models for Metaheuristics Solution-level: Processing of a single solution (Objective / Data partitioning) Algorithm-level: Independent walks, Multi-start model, Hybridization/Cooperation of metaheuristics Iteration-level: Parallel evaluation of the neighborhood/population ScalabilityHPS× × Heuristic Population / Neighborhood Solution Algorithm-level Parallel Model Meta. Independent on the problem Alter the behavior of the Meta metaheuristic Cooperation Design questions: When ? Migration decision: Blind: Periodic or Probabilistic Meta « Intelligent »: state dependent Meta. Where ? Exchange topology: complete graph, ring, torus, random, … Simulated Annealing Which ? Information : elite solutions, Genetic Programming search memory, … Evolution Strategy Tabu search How ? Integration Ant Colonies Scatter search, … Ex : Evolutionary Algorithms (Island Model Distribution of the population in a set of islands in which semi-isolated EAs are executed GA GA GA GA Sparse individual exchanges are performed among these islands with GA GA the goal of introducing more diversity GA GA into the target populations Improvement of robustness and quality Ex : The multi-start model in LS Independent set of Local Searches Improve robustness and quality of solutions May start from the same or different initial solution, population Different parameters encoding, operators, memory sizes (tabu list, …), etc. Flat classification Algorithm-Level Parallel Model Homogeneous Heterogenenous Partial Global Specialist General E-G. Talbi, «A taxonomy of hybrid metaheuristics », Journal of Heuristics, Vol.8, No2, 2002. Homogeneous / Heterogeneous Homogeneous hybrids = Same Metaheuristic Heterogeneous = Different Metaheuristics Example Evolutionary Algorithm Simulated Annealing Communication medium Tabu Search Several different metaheuristics cooperate and co-evolve some solutions Global / Partial Partial : Problem is decomposed in sub-problems. Each algorithm is dedicated to solve one sub- problem Combinatorial / Continuous : Decomposition of the problem, decision space, … Problem dependant: Vehicle routing, Scheduling, … Problem Sub-problem Sub-problem Sub-problem Meta Meta Meta EAs, Tabu search Simulated annealing Synchronization : Build a global viable solution Specialist / General Specialist: Algorithms which solve different problems (ex: Co-evolutionary) Diversifying Agent Intensifying Agent Solutions Promising from solutions unexplored regions Refers to Refers to Adaptative Memory Explored regions Promising regions explored Initial solutions Good space solutions Local Search Agent S. Cahon, N. Melab and E-G. Talbi. COSEARCH: A parallel cooperativ metaheuristic. Journal of Mathematical Modeling and algorithms JMMA, Vol.5(2), 2006. Iteration-level Parallel Model Independent on the problem Complex objective functions (non linear, simulations, …) Don’t alter the behavior of the Meta metaheuristic Æ Speedup the search Solution(s) Full Design questions: fitness Single-solution based algorithms: decomposition of the neighborhood Population based algorithms: decomposition of the population Ex : Single-Solution Based Metaheuristic LS Solution Evaluation of the neighborhood: computationally Partition of the intensive step neighborhood Decomposition of the neighborhood : Asynchronous for SA (# behavior) Synchronous for deterministic algorithms Ex : Population Based Metaheuristic Selection Replacement Decomposition of the E.A. population: Couple of Individuals (EAs), Ants (AS), individuals Particles (PSO), etc. Evaluated offsprings Sharing Information: Pheromone matrix, etc. Asynchronous for steady state GAs (# behavior) Recombination, Synchronous for generational Mutation, GAs evaluation E-G. Talbi, O. Roux, C. Fonlupt, D. Robillard, «Parallel ant colonies for the quadratic assignment problem», Future Generation Computer Systems, 17(4), 2001. Solution-level Parallel Model Aggregation of Dependent on the problem partial fitnesses Don’t alter the behavior of the metaheuristic Æ Speedup the Solution Partial search (CPU or I/O intensive) fitness Synchronous Design questions: Data / Task Decomposition CPU #1 CPU #3 Data : database, geographical area, structure, … Task : sub-functions, solvers, … CPU #2 CPU #4 Outline Parallel Metaheuristics: Design issues Parallel Metaheuristics: Implementation issues Hardware platforms, Programming models Performance evaluation Adaptation to Multi-objective Optimization Software frameworks IIlustration : Network design problem Parallel Metaheuristics: Implementation issues Design of Parallel Metaheuristics Programming Paradigms ParallelParallel Programm Programminging Execution Support EnvironmentsEnvironments Parallel Architecture Hardware P P P P P .. P Main criteria : Memory sharing, Homogeneity, Dedicated, Scalability, Volatility PP Processor Thread Process Shared Memory Machine Memory Easy to program: conventional OS network and programming paradigms CPU CPU CPU Poor Scalability: Increase in processors leads memory contention interconnection network: bus, crossbar, multistage Ex. : Dual-core (Intel, AMD), Origin crossbar (Silicon Graphics), .... Distributed Memory Architectures Good Scalability : Several hundred CPU Memory nodes with a high speed interconnection network/switch network Harder to program Memory CPU Memory CPU Communication cost Interconnection schema : Ex. : Clusters hypercube, (2D or 3D) torus, fat-tree, multistage crossbars Clusters & NOWs Clusters: A collections of PCs interconnected through high speed network, Low cost Standard components NOWs: Network Of Workstations take advantage of unused computing power ccNUMA architectures Mixing SMP and DSM Small number of processors (up to 16) clustered in SMP nodes (fast connection, crossbar for instance) SMPs are connected through a less costly network with poorer performance Memory CPU Memory CPU Memory CPU network network network CPUCPUCPU CPUCPUCPU CPUCPUCPU Interconnection network Periph. Grid Computing “Coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations” High-Performance High-Throughput Computing GRID Computing GRID • Offer a virtual supercomputer Billions of idle PCs … Stealing unused CPU cycles of processors (a mean of 47%) Inexpensive, potentially very powerful but more difficult to program than traditional parallel computers N. Melab, S. Cahon, E-G. Talbi, « Grid computing for parallel bioinspired algorithms», Journal of Parallel and Distributed Computing (JDPC), 66(8), 2006. GRID Platforms Lille Orsay Nancy Rennes Lyon Grenoble Bordeaux Sophia Toulouse HPC Grid: GRID’5000: 9 sites distributed in France and HTC Grid: PlanetLab: 711 inter-connected by Renater nodes on 338 sites over 25 5000 proc: between 500 and 1000 CPUs on each site countries R. Bolze, …, E-G. Talbi, …, «GRID’5000: A large scale and highly reconfigurable Grid», International Journal of High Performance Computing Applications (IJHPCA), Vol.20(4), 2006. Main criteria for an efficient implementation Criteria Shared Homogenous Dedicated Local Volatility Memory / / / network / & Fault Distributed Heterogenous Non Dedi. Large Tolerance Architecture network SMP SM Hom Dedi Local No COW DM Hom Dedi Local No NOW DM Het Non Local Yes HP Grid DM Het Dedi Large No HT Grid DM Het Non Large Yes Parallel Programming Environments Shared-memory: System Category Language binding PThreads Operating system C Java threads Programming language Java OpenMP
Load more

Recommended publications
  • Appendix H: MOEA Software Availability
    H MOEA Software Availability H.1 Introduction This appendix describes some of the main public-domain MOEA software that is currently available. This description includes the following information: Name: Name of the software system Description: Any relevant information about the system Environment: Software environment for which the software is intended Language: Programming language in which the software is written Availability: Software availability Original sources of each of the systems analyzed are cited, but readers should be aware of the fact that many of these systems are also mirrored at the EMOO repository located at (see under Software): http://delta.cs.cinvestav.mx/~ccoello/EMOO/ with a mirror at: http://www.lania.mx/~ccoello/EMOO Note that although commercial software for multi-objective optimization also exists (e.g., iSIGHT, which is briefly described in Chapter 5), such systems are not included here, since we limited the contents of this appendix to public- domain software. 678 H MOEA Software Availability Table H.1: MOEA Software Name Description Environment Availability Metaheuristic Includes imple- Any platform Antonio J. Nebro Algorithms in mentations in supporting Java ([email protected]), or download Java (JMetal) Java of: NSGA-II at: http://neo.lcc.uma.es/metal/ [358], SPEA2 index.html [1712], PAES [858], OMOPSO [1546], AbYSS [1137], MOCell [1136]. It also includes several test functions Multiple- Includes: Pareto Linux/Unix; Andrzej Jaszkiewicz Objective Simulated An- Standard C++ ([email protected]) MetaHeuristics
    [Show full text]
  • Metaheuristic Optimization Frameworks: a Survey and Benchmarking
    Soft Comput DOI 10.1007/s00500-011-0754-8 ORIGINAL PAPER Metaheuristic optimization frameworks: a survey and benchmarking Jose´ Antonio Parejo • Antonio Ruiz-Corte´s • Sebastia´n Lozano • Pablo Fernandez Ó Springer-Verlag 2011 Abstract This paper performs an unprecedented com- and affordable time and cost. However, heuristics are parative study of Metaheuristic optimization frameworks. usually based on specific characteristics of the problem at As criteria for comparison a set of 271 features grouped in hand, which makes their design and development a com- 30 characteristics and 6 areas has been selected. These plex task. In order to solve this drawback, metaheuristics features include the different metaheuristic techniques appear as a significant advance (Glover 1977); they are covered, mechanisms for solution encoding, constraint problem-agnostic algorithms that can be adapted to incor- handling, neighborhood specification, hybridization, par- porate the problem-specific knowledge. Metaheuristics allel and distributed computation, software engineering have been remarkably developed in recent decades (Voß best practices, documentation and user interface, etc. A 2001), becoming popular and being applied to many metric has been defined for each feature so that the scores problems in diverse areas (Glover and Kochenberger 2002; obtained by a framework are averaged within each group of Back et al. 1997). However, when new are considered, features, leading to a final average score for each frame- metaheuristics should be implemented and tested, implying work. Out of 33 frameworks ten have been selected from costs and risks. the literature using well-defined filtering criteria, and the As a solution, object-oriented paradigm has become a results of the comparison are analyzed with the aim of successful mechanism used to ease the burden of applica- identifying improvement areas and gaps in specific tion development and particularly, on adapting a given frameworks and the whole set.
    [Show full text]
  • Paradiseo-MO: from Fitness Landscape Analysis to Efficient
    ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, Sébastien Verel To cite this version: Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, Sébastien Verel. ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms. [Research Report] RR-7871, 2012. hal- 00665421v1 HAL Id: hal-00665421 https://hal.inria.fr/hal-00665421v1 Submitted on 1 Feb 2012 (v1), last revised 4 Jun 2013 (v2) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, Sébastien Verel RESEARCH REPORT N° 7871 February 2012 Project-Team Dolphin ISSN 0249-6399 ISRN INRIA/RR--7871--FR+ENG ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms Jérémie Humeau∗, Arnaud Liefooghe†, El-Ghazali Talbi‡, Sébastien Verel§ Project-Team Dolphin Research Report n° 7871 — February 2012 — 34 pages Abstract: This document presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search algorithms: ParadisEO-MO. A sub- stantial number of single-solution based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented.
    [Show full text]
  • Arxiv:1903.02915V2 [Cs.NE] 17 Apr 2019 an Important Factor That Has Ignited the Widespread Adop- Namic Problems
    jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics Antonio Ben´ıtez-Hidalgoa, Antonio J. Nebroa, Jose´ Garc´ıa-Nietoa, Izaskun Oregib, Javier Del Serb,c,d aDepartamento de Lenguajes y Ciencias de la Computaci´on,Ada Byron Research Building, University of M´alaga, 29071 M´alaga, Spain bTECNALIA, 48160 Derio, Spain cUniversity of the Basque Country (UPV/EHU), 48013 Bilbao, Spain dBasque Center for Applied Mathematics (BCAM), 48009 Bilbao, Spain Abstract This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it. Keywords: Multi-Objective Optimization, Metaheuristics, Software Framework, Python, Statistical Analysis, Visualization 1.
    [Show full text]
  • Paradiseo-MOEO Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi
    A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi To cite this version: Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi. A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO. European Journal of Operational Research, Elsevier, 2011, 209 (2), pp.104-112. 10.1016/j.ejor.2010.07.023. hal-00522612 HAL Id: hal-00522612 https://hal.archives-ouvertes.fr/hal-00522612 Submitted on 1 Oct 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. European Journal of Operational Research xxx (2010) xxx–xxx Contents lists available at ScienceDirect European Journal of Operational Research journal homepage: www.elsevier.com/locate/ejor Decision Support A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO ⇑ Arnaud Liefooghe a,b, , Laetitia Jourdan a, El-Ghazali Talbi a,c a Université Lille 1, Laboratoire d’Informatique Fondamentale de Lille, UMR CNRS 8022, INRIA Lille-Nord Europe, Parc Scientifique de la Haute Borne, 40 Avenue Halley, 59650 Villeneuve d’Ascq, France b University of Coimbra, CISUC, Department of Informatics Engineering, Portugal c King Saud University, Riyadh, Saudi Arabia article info abstract Article history: This paper presents a general-purpose software framework dedicated to the design and the implementa- Received 24 April 2009 tion of evolutionary multiobjective optimization techniques: ParadisEO-MOEO.
    [Show full text]
  • Parallel Evolutionary Algorithms for Multi- Objective Optimization
    Parallel evolutionary algorithms for multi- objective optimization Prof. El-Ghazali TALBI University Lille 1 [email protected] www.lifl.fr/~talbi Motivations . High-dimensional and complex optimization problems in many areas of academic and industrial concern Telecommunication, genomics, transportation, engineering design, … . Metaheuristics (ex. EAs) reduce the computational complexity of search BUT Parallel issue remains important: . More and more complex metaheuristics . Huge search space . CPU or memory intensive of the objective function Motivations . Rapid development of technology . Processors: Multi-core processors, GPU, … . Networks (LAN & WAN): Myrinet, Infiniband, Optical networks, … . Data storage . Increasing ratio performance / cost Goals . Speedup the search real-time and interactive optimization methods, dynamic optimisation, … . Improve quality of solutions cooperation, better even on a single processor . Improve robustness (different instances, design space) . Solving large problems instances, accuracy of mathematics models Taxonomy of metaheuristics Metaheuristics Single Population solution Hill Simulated Tabu Evol. Swarm Climbing annealing search algorithms optimisation . Solution-based metaheuristics: Hill Climbing, Simulated Annealing, Tabu Search, VNS, … . Population-based metaheuristics: Evolutionary Algorithms, Scatter Search, Swarm optimization, … Outline . Parallel Metaheuristics: Design issues . Parallel Metaheuristics: Implementation issues . Hardware platforms, Programming models . Performance
    [Show full text]
  • Paradiseo-MOEO Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by HAL - Lille 3 A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi To cite this version: Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi. A Unified Model for Evolutionary Mul- tiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO. [Research Report] RR-6906, INRIA. 2009. <inria-00376770> HAL Id: inria-00376770 https://hal.inria.fr/inria-00376770 Submitted on 20 Apr 2009 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO Arnaud Liefooghe — Laetitia Jourdan — El-Ghazali Talbi N° 6906 April 2009 Thème NUM apport de recherche ISSN 0249-6399 ISRN INRIA/RR--6906--FR+ENG A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO Arnaud Liefooghe , Laetitia Jourdan , El-Ghazali Talbi Th`eme NUM — Syst`emes num´eriques Equipe-Projet´ Dolphin Rapport de recherche n° 6906 — April 2009 — 28 pages Abstract: This paper gives a concise overview of evolutionary algorithms for multiobjective optimization.
    [Show full text]
  • An Experimental Comparison of Metaheuristic Frameworks for Multi-Objective Optimization
    An experimental comparison of metaheuristic frameworks for multi-objective optimization | Extended study | Technical Report Aurora Ram´ırez, Rafael Barbudo and Jos´eRa´ulRomero1 Knowledge Discovery and Intelligent Systems Research Group Department of Computer Science and Numerical Analysis University of C´ordoba September 27, 2020 1Corresponding author: [email protected] An experimental comparison of metaheuristic frameworks for multi-objective optimization 1 Introduction Metaheuristic optimization frameworks (MOFs) are software tools or class libraries that provide a collection of extensible building blocks to create algorithms and experiments aimed at solving optimization problems. Several alternatives are currently available to deal with multi-objective optimization (MOO) problems, which are characterized by the definition of two or more objectives. This technical report is provided as additional material to the research paper of the same title. Therefore, the information presented in this technical report is intended to complement those aspects that have been summarized or omitted due to space limitations. Essentially, it provides further details about the evaluation of ten MOFs in terms of characteristics like the set of algorithms and operators each one offers or the availability of general utilities, among others. This document presents the information extracted from the available documentation and the source code, serving as a reference guide to domain experts and researchers interested in the application of metaheuristic algorithms to solve multi-objective problems. More specifically, the content of this technical report can be summarized as follows: • The detailed methodology followed to evaluate the frameworks, including technical details of external tools and the experimental environment. • The exhaustive evaluation of each characteristic is presented in tables.
    [Show full text]
  • Paradiseo-MOEO
    ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization Arnaud Liefooghe, Laetitia Jourdan, Thomas Legrand, Jérémie Humeau, El-Ghazali Talbi To cite this version: Arnaud Liefooghe, Laetitia Jourdan, Thomas Legrand, Jérémie Humeau, El-Ghazali Talbi. ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization. Ad- vances in Multi-Objective Nature Inspired Computing, Springer Berlin / Heidelberg, pp.87-117, 2010, 10.1007/978-3-642-11218-8_5. hal-00522623 HAL Id: hal-00522623 https://hal.archives-ouvertes.fr/hal-00522623 Submitted on 1 Oct 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 5 ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization Arnaud Liefooghe1,2,LaetitiaJourdan1,2,ThomasLegrand2,J´er´emie Humeau2, and El-Ghazali Talbi1,2 1 Laboratoire d’Informatique Fondamentale de Lille (LIFL), UMR CNRS 8022, Universit´eLille1,Bˆatiment M3, 59655 Villeneuve d’Ascq cedex, France 2 INRIA Lille-Nord Europe, Parc Scientifique de la Haute Borne, 40 avenue Halley, 59650 Villeneuve d’Ascq, France [email protected], [email protected], [email protected], [email protected], [email protected] Summary. This chapter presents ParadisEO-MOEO, awhite-boxobject-orientedsoftware framework dedicated to the flexible design of metaheuristics for multi-objective optimization.
    [Show full text]
  • From Fitness Landscape Analysis to Efficient Local Search Algorithms
    ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, Sébastien Verel To cite this version: Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, Sébastien Verel. ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms. [Research Report] RR-7871, INRIA. 2013. hal-00665421v2 HAL Id: hal-00665421 https://hal.inria.fr/hal-00665421v2 Submitted on 4 Jun 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, Sébastien Verel RESEARCH REPORT N° 7871 June 2013 Project-Team Dolphin ISSN 0249-6399 ISRN INRIA/RR--7871--FR+ENG ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms Jérémie Humeau∗, Arnaud Liefooghe†, El-Ghazali Talbi‡, Sébastien Verel§ Project-Team Dolphin Research Report n° 7871 — June 2013 — 36 pages Abstract: This document presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search algorithms: ParadisEO-MO. A sub- stantial number of single-solution based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented.
    [Show full text]
  • Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System Summer Student Report
    EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH EN-STI-FDA Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System Summer Student Report Author Supervisor M. TYDRICHOVA W. Koz lowska August 4, 2017, Geneva Introduction The principal goal of this project was to analyze various multiobjective opti- mization algorithms (MOEA) for the Monte Carlo Treatment Planning System. After doing literature research on testing and quality evaluation methods, the comparison of available frameworks was done to select the most suitable one for our test needs. Then the software for testing was developed, we tried to provide a solid objective-oriented architecture to allow easy modifications in the future, another high priority was to create an user-friendly software. From all available algorithms, candidates for testing were selected. Apart results from the literature that were taken into account to do this selection, we placed emphasis on a diverse scale of algorithms - the aim was not only to find the best performing algorithm, but also to describe the comportment, advantages and weak points of different types of algorithms, in hope that this output could be helpful and valuable for the further research. In performed tests, we focused mostly on the quality of DOSE and LET values of obtained solutions, as the main goal of the problem was to minimize these quantities. Distribution of solutions and population size were also analyzed. Another important criterion was the reliability of the algorithm. That means, we studied if the algorithm performed the same way in all seeds and if it was independent on a given data set.
    [Show full text]
  • Multi-Objective Tools for the Vehicle Routing Problem with Time Windows
    Multi-Objective Tools for the Vehicle Routing Problem with Time Windows Juan Castro-Gutierrez Thesis submitted to The University of Nottingham for the degree of Doctor of Philosophy March 2012 Dedicated to my parents Juan Luis and Nieves i Abstract Most real-life problems involve the simultaneous optimisation of two or more, usu- ally conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created meth- ods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collec- tion, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the as- sumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others.
    [Show full text]