From d3671e4df2fd418c158173b818b353b948cdd2a2 Mon Sep 17 00:00:00 2001 From: canape Date: Wed, 12 Mar 2008 14:50:46 +0000 Subject: [PATCH] delete git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1131 331e1502-861f-0410-8da2-ba01fb791d7f --- tags/paradiseo-1.1/paradiseo-peo/src/peo.h | 351 --------------------- 1 file changed, 351 deletions(-) delete mode 100644 tags/paradiseo-1.1/paradiseo-peo/src/peo.h diff --git a/tags/paradiseo-1.1/paradiseo-peo/src/peo.h b/tags/paradiseo-1.1/paradiseo-peo/src/peo.h deleted file mode 100644 index 9ef703a01..000000000 --- a/tags/paradiseo-1.1/paradiseo-peo/src/peo.h +++ /dev/null @@ -1,351 +0,0 @@ -/* -* -* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2008 -* (C) OPAC Team, LIFL, 2002-2008 -* -* Sebastien Cahon, Alexandru-Adrian Tantar, Clive Canape -* -* This software is governed by the CeCILL license under French law and -* abiding by the rules of distribution of free software. You can use, -* modify and/ or redistribute the software under the terms of the CeCILL -* license as circulated by CEA, CNRS and INRIA at the following URL -* "http://www.cecill.info". -* -* As a counterpart to the access to the source code and rights to copy, -* modify and redistribute granted by the license, users are provided only -* with a limited warranty and the software's author, the holder of the -* economic rights, and the successive licensors have only limited liability. -* -* In this respect, the user's attention is drawn to the risks associated -* with loading, using, modifying and/or developing or reproducing the -* software by the user in light of its specific status of free software, -* that may mean that it is complicated to manipulate, and that also -* therefore means that it is reserved for developers and experienced -* professionals having in-depth computer knowledge. Users are therefore -* encouraged to load and test the software's suitability as regards their -* requirements in conditions enabling the security of their systems and/or -* data to be ensured and, more generally, to use and operate it in the -* same conditions as regards security. -* The fact that you are presently reading this means that you have had -* knowledge of the CeCILL license and that you accept its terms. -* -* ParadisEO WebSite : http://paradiseo.gforge.inria.fr -* Contact: paradiseo-help@lists.gforge.inria.fr -* -*/ - -#ifndef __peo_h_ -#define __peo_h_ - -#include -#include -#include - - -//! \mainpage The ParadisEO-PEO Framework -//! -//! \section intro Introduction -//! -//! ParadisEO is a white-box object-oriented framework dedicated to the reusable design -//! of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary -//! algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization -//! mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a -//! clear conceptual separation of the solution methods from the problems they are intended to solve. This separation -//! confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes -//! provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is one of the rare -//! frameworks that provide the most common parallel and distributed models. Their implementation is portable on -//! distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as -//! MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated -//! provided classes. Their experimentation on the radio network design real-world application demonstrate their -//! efficiency. -//! -//! In practice, combinatorial optimization problems are often NP-hard, CPU time-consuming, -//! and evolve over time. Unlike exact methods, metaheuristics allow to tackle large-size problems -//! instances by delivering satisfactory solutions in a reasonable time. Metaheuristics are -//! general-purpose heuristics that split in two categories: evolutionary algorithms (EA) and local -//! search methods (LS). These two families have complementary characteristics: EA allow -//! a better exploration of the search space, while LS have the power to intensify the search in -//! promising regions. Their hybridization allows to deliver robust and better solutions -//! -//! Although serial metaheuristics have a polynomial temporal complexity, they remain -//! unsatisfactory for industrial problems. Parallel and distributed computing is a powerful way -//! to deal with the performance issue of these problems. Numerous parallel and distributed -//! metaheuristics (PDM) and their implementations have been proposed, and are available on -//! theWeb. They can be reused and adapted to his/her own problems. However, the user has to -//! deeply examine the code and rewrite its problem-specific sections. The task is tedious, errorprone, -//! takes along time and makes harder the produced code maintenance. A better way to -//! reuse the code of existing PDM is the reuse through libraries. These are often -//! more reliable as they are more tested and documented. They allow a better maintainability -//! and efficiency. However, libraries do not allow the reuse of design. -//! -//! \section parallel_metaheuristics Parallel and distributed metaheuristics -//! -//! \subsection parallel_distributed Parallel distributed evolutionary algorithms -//! -//! Evolutionary Algorithms (EA) are based on the iterative improvement of a -//! population of solutions. At each step, individuals are selected, paired and recombined in order -//! to generate new solutions that replace other ones, and so on. As the algorithm converges, -//! the population is mainly composed of individuals well adapted to the "environment", for -//! instance the problem. The main features that characterize EA are the way the population is -//! initialized, the selection strategy (deterministic/stochastic) by fostering "good" solutions, -//! the replacement strategy that discards individuals, and the continuation/stopping criterion -//! to decide whether the evolution should go on or not. -//! -//! Basically, three major parallel and distributed models for EA can been distinguished: -//! the island (a)synchronous cooperative model, the parallel evaluation of the -//! population, and the distributed evaluation of a single solution. -//!
    -//!
  • Island (a)synchronous cooperative model. Different EA are simultaneously deployed to -//! cooperate for computing better and robust solutions. They exchange in an asynchronous -//! way genetic stuff to diversify the search. The objective is to allow to delay the global -//! convergence, especially when theEAare heterogeneous regarding the variation operators. -//! The migration of individuals follows a policy defined by few parameters: the migration -//! decision criterion, the exchange topology, the number of emigrants, the emigrants selection -//! policy, and the replacement/integration policy.
    • -//! -//!
  • Parallel evaluation of the population. It is required as it is in general the most timeconsuming. -//! The parallel evaluation follows the centralized model. The farmer applies -//! the following operations: selection, transformation and replacement as they require a -//! global management of the population. At each generation, it distributes the set of new -//! solutions between differentworkers. These evaluate and return back the solutions and their -//! quality values. An efficient execution is often obtained particularly when the evaluation -//! of each solution is costly. The two main advantages of an asynchronous model over -//! the synchronous model are: (1) the fault tolerance of the asynchronous model; (2) the -//! robustness in case the fitness computation can take very different computation times (e.g. -//! for nonlinear numerical optimization). Whereas some time-out detection can be used to -//! address the former issue, the latter one can be partially overcome if the grain is set to very -//! small values, as individuals will be sent out for evaluations upon request of the workers.
    • -//! -//!
  • Distributed evaluation of a single solution. The quality of each solution is evaluated in -//! a parallel centralized way. That model is particularly interesting when the evaluation -//! function can be itself parallelized as it is CPU time-consuming and/or IO intensive. In -//! that case, the function can be viewed as an aggregation of a certain number of partial -//! functions. The partial functions could also be identical if for example the problem to deal -//! with is a data mining one. The evaluation is thus data parallel and the accesses to data -//! base are performed in parallel. Furthermore, a reduction operation is performed on the -//! results returned by the partial functions. As a summary, for this model the user has to -//! indicate a set of partial functions and an aggregation operator of these.
    • -//!
-//! -//! \subsection parallel_ls Parallel distributed local searches -//! -//! \subsubsection local_searches Local searches -//! -//! All metaheuristics dedicated to the improvement of a single solution -//! are based on the concept of neighborhood. They start from a solution randomly generated or -//! obtained from another optimization algorithm, and update it, step by step, by replacing the -//! current solution by one of its neighboring candidates. Some criterion have been identified to -//! differentiate such searches: the heuristic internal memory, the choice of the initial solution, -//! the candidate solutions generator, and the selection strategy of candidate moves. Three main -//! algorithms of local search stand out: Hill Climbing (HC), Simulated -//! Annealing (SA) and Tabu Search (TS). -//! -//! \subsubsection parallel_local_searches Parallel local searches -//! -//! Two parallel distributed models are commonly used in the literature: the parallel distributed -//! exploration of neighboring candidate solutions model, and the multi-start model. -//!
    -//!
  • Parallel exploration of neighboring candidates. It is a low-level Farmer-Worker model -//! that does not alter the behavior of the heuristic. A sequential search computes the same -//! results slower.At the beginning of each iteration, the farmer duplicates the current solution -//! between distributed nodes. Each one manages some candidates and the results are returned to the farmer. -//! The model is efficient if the evaluation of a each solution is time-consuming and/or there are a great -//! deal of candidate neighbors to evaluate. This is obviously not applicable to SA since only one candidate -//! is evaluated at each iteration. Likewise, the efficiency of the model for HC is not always guaranteed as -//! the number of neighboring solutions to process before finding one that improves the current objective function may -//! be highly variable.
    • -//! -//!
  • Multi-start model. It consists in simultaneously launching several local searches. They -//! may be heterogeneous, but no information is exchanged between them. The resultswould -//! be identical as if the algorithms were sequentially run.Very often deterministic algorithms -//! differ by the supplied initial solution and/or some other parameters. This trivial model is -//! convenient for low-speed networks of workstations.
    • -//!
-//! -//! \section hybridization Hybridization -//! -//! Recently, hybrid metaheuristics have gained a considerable interest. For many -//! practical or academic optimization problems, the best found solutions are obtained by -//! hybrid algorithms. Combinations of different metaheuristics have provided very powerful -//! search methods. Two levels and two modes -//! of hybridization have been distinguished: Low and High levels, and Relay and Cooperative modes. -//! The low-level hybridization addresses the functional composition of a single optimization -//! method. A function of a given metaheuristic is replaced by another metaheuristic. On the -//! contrary, for high-level hybrid algorithms the different metaheuristics are self-containing, -//! meaning no direct relationship to their internal working is considered. On the other hand, -//! relay hybridization means a set of metaheuristics is applied in a pipeline way. The output -//! of a metaheuristic (except the last) is the input of the following one (except the first). -//! Conversely, co-evolutionist hybridization is a cooperative optimization model. Each metaheuristic -//! performs a search in a solution space, and exchange solutions with others. -//! -//! \section paradiseo_goals Paradiseo goals and architecture -//! -//! The "EO" part of ParadisEO means Evolving Objects. EO is a C++ LGPL open source -//! framework and includes a paradigm-free Evolutionary Computation library (EOlib) -//! dedicated to the flexible design of EA through evolving objects superseding the most common -//! dialects (Genetic Algorithms, Evolution Strategies, Evolutionary Programming and -//! Genetic Programming). Furthermore, EO integrates several services including visualization -//! facilities, on-line definition of parameters, application check-pointing, etc. ParadisEO is an -//! extended version of the EO framework. The extensions include local search methods, hybridization -//! mechanisms, parallelism and distribution mechanisms, and other features that -//! are not addressed in this paper such as multi-objective optimization and grid computing. In -//! the next sections, we present the motivations and goals of ParadisEO, its architecture and -//! some of its main implementation details and issues. -//! -//! \subsection motivation Motivations and goals -//! -//! A framework is normally intended to be exploited by as many users as possible. Therefore, -//! its exploitation could be successful only if some important user criteria are satisfied. The -//! following criteria are the major of them and constitute the main objectives of the ParadisEO -//! framework: -//! -//!
    -//!
  • Maximum design and code reuse. The framework must provide for the user a whole -//! architecture design of his/her solution method. Moreover, the programmer may redo as -//! little code as possible. This objective requires a clear and maximal conceptual separation -//! between the solution methods and the problems to be solved, and thus a deep domain -//! analysis. The user might therefore develop only the minimal problem-specific code.
    • -//! -//!
  • Flexibility and adaptability. It must be possible for the user to easily add new features/ -//! metaheuristics or change existing ones without implicating other components. Furthermore, -//! as in practice existing problems evolve and new others arise these have to be -//! tackled by specializing/adapting the framework components.
    • -//! -//!
  • Utility. The framework must allow the user to cover a broad range of metaheuristics, -//! problems, parallel distributed models, hybridization mechanisms, etc.
    • -//! -//!
  • Transparent and easy access to performance and robustness. As the optimization applications -//! are often time-consuming the performance issue is crucial. Parallelism and -//! distribution are two important ways to achieve high performance execution. In order to -//! facilitate its use it is implemented so that the user can deploy his/her parallel algorithms in -//! a transparent manner. Moreover, the execution of the algorithms must be robust to guarantee -//! the reliability and the quality of the results. The hybridization mechanism allows -//! to obtain robust and better solutions.
    • -//! -//!
  • Portability. In order to satisfy a large number of users the framework must support -//! different material architectures and their associated operating systems.
    • -//!
-//! -//! \subsection architecture ParadisEO architecture -//! -//! The architecture of ParadisEO is multi-layer and modular allowing to achieve the objectives -//! quoted above. This allows particularly a high flexibility and adaptability, an -//! easier hybridization, and more code and design reuse. The architecture has three layers -//! identifying three major categories of classes: Solvers, Runners and Helpers. -//!
    -//!
  • Helpers. Helpers are low-level classes that perform specific actions related to the evolution -//! or search process. They are split in two categories: Evolutionary helpers (EH) -//! and Local search helpers (LSH). EH include mainly the transformation, selection and -//! replacement operations, the evaluation function and the stopping criterion. LSH can be -//! generic such as the neighborhood explorer class, or specific to the local search metaheuristic -//! like the tabu list manager class in the Tabu Search solution method. On the -//! other hand, there are some special helpers dedicated to the management of parallel and -//! distributed models 2 and 3, such as the communicators that embody the communication -//! services. -//! -//! Helpers cooperate between them and interact with the components of the upper layer -//! i.e. the runners. The runners invoke the helpers through function parameters. Indeed, -//! helpers have not their own data, but they work on the internal data of the runners.
    • -//! -//!
  • Runners. The Runners layer contains a set of classes that implement the metaheuristics -//! themselves. They perform the run of the metaheuristics from the initial state or -//! population to the final one. One can distinguish the Evolutionary runners (ER) such as -//! genetic algorithms, evolution strategies, etc., and Local search runners (LSR) like tabu -//! search, simulated annealing and hill climbing. Runners invoke the helpers to perform -//! specific actions on their data. For instance, an ER may ask the fitness function evaluation -//! helper to evaluate its population. An LSR asks the movement helper to perform -//! a given movement on the current state. Furthermore, runners can be serial or parallel -//! distributed.
    • -//! -//!
  • Solvers. Solvers are devoted to control the evolution process and/or the search. They -//! generate the initial state (solution or population) and define the strategy for combining -//! and sequencing different metaheuristics. Two types of solvers can be distinguished. -//! Single metaheuristic solvers (SMS) and Multiple metaheuristics solvers (MMS). SMSs -//! are dedicated to the execution of only one metaheuristic.MMS are more complex as they -//! control and sequence several metaheuristics that can be heterogeneous. Solvers interact with -//! the user by getting the input data and delivering the output (best solution, statistics, -//! etc).
    • -//!
-//! -//! According to the generality of their embedded features, the classes of the architecture split -//! in two major categories: Provided classes and Required classes. Provided classes embody -//! the factored out part of the metaheuristics. They are generic, implemented in the framework, -//! and ensure the control at run time. Required classes are those that must be supplied by the -//! user. They encapsulate the problem-specific aspects of the application. These classes are -//! fixed but not implemented in ParadisEO. The programmer has the burden to develop them -//! using the OO specialization mechanism. -//! -//! \section tutorials ParadisEO-PEO Tutorials -//! -//! The basisc of the ParadisEO framework philosophy are exposed in a few simple tutorials: -//! -//! All the presented examples have as case study the traveling salesman problem (TSP). Different operators and auxiliary objects were designed, -//! standing as a common shared source code base. While not being -//! part of the ParadisEO-PEO framework, it may represent a startpoint for a better understanding of the presented tutorials. -//! -//! \section LICENCE -//! -//! -//!This software is governed by the CeCILL license under French law and -//!abiding by the rules of distribution of free software. You can use, -//!modify and/ or redistribute the software under the terms of the CeCILL -//!license as circulated by CEA, CNRS and INRIA at the following URL -//!"http://www.cecill.info". -//! -//!As a counterpart to the access to the source code and rights to copy, -//!modify and redistribute granted by the license, users are provided only -//!with a limited warranty and the software's author, the holder of the -//!economic rights, and the successive licensors have only limited liability. -//! -//!In this respect, the user's attention is drawn to the risks associated -//!with loading, using, modifying and/or developing or reproducing the -//!software by the user in light of its specific status of free software, -//!that may mean that it is complicated to manipulate, and that also -//!therefore means that it is reserved for developers and experienced -//!professionals having in-depth computer knowledge. Users are therefore -//!encouraged to load and test the software's suitability as regards their -//!requirements in conditions enabling the security of their systems and/or -//!data to be ensured and, more generally, to use and operate it in the -//!same conditions as regards security. -//!The fact that you are presently reading this means that you have had -//!knowledge of the CeCILL license and that you accept its terms. -//! -//!ParadisEO WebSite : http://paradiseo.gforge.inria.fr -//!Contact: paradiseo-help@lists.gforge.inria.fr - -#include "core/peo_init.h" -#include "core/peo_run.h" -#include "core/peo_fin.h" - -#include "core/messaging.h" -#include "core/eoPop_mesg.h" -#include "core/eoVector_mesg.h" - -#include "peoWrapper.h" - -/* <------- components for parallel algorithms -------> */ -#include "peoTransform.h" -#include "peoEvalFunc.h" -#include "peoPopEval.h" - -/* Cooperative island model */ -#include "core/ring_topo.h" -#include "core/star_topo.h" -#include "core/random_topo.h" -#include "core/complete_topo.h" -#include "peoData.h" -#include "peoSyncIslandMig.h" -#include "peoAsyncIslandMig.h" - -/* Synchronous multi-start model */ -#include "peoMultiStart.h" -/* <------- components for parallel algorithms -------> */ - -/* Parallel PSO */ -#include "peoPSO.h" - -#endif