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Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms
Abstract
This work presents the application of a parallel cooperative optimization approach to the broadcast operation in mobile ad-hoc
networks (manets). The optimization of the broadcast operation implies satisfying several objectives simultaneously, so a multi-objective
approach has been designed. The optimization lies on searching the best configurations of the dfcn broadcast protocol for a given manet scenario. The cooperation of a team of multi-objective evolutionary algorithms has been performed with a novel optimization
model. Such model is a hybrid parallel algorithm that combines a parallel island-based scheme with a hyperheuristic approach.
Results achieved by the algorithms in different stages of the search process are analyzed in order to grant more computational
resources to the most suitable algorithms. The obtained results for a manets scenario, representing a mall, demonstrate the validity of the new proposed approach.
... R NODE TRAVERSAL TIME 0.040 s [0.01, 15.0] R MAX RREQ TIMEOUT 10.0 s [1.0, 100.0] R NET DIAMETER 35 [3] [100] Z ALLOWED HELLO LOSS 2 [0] [20] Z REQ RETRIES 2 [0] [20] Z TTL START 1 [1] [40] Z TTL INCREMENT 2 [1] [20] Z TTL THRESHOLD 7 [1] [60] Z ...
... R NODE TRAVERSAL TIME 0.040 s [0.01, 15.0] R MAX RREQ TIMEOUT 10.0 s [1.0, 100.0] R NET DIAMETER 35 [3] [100] Z ALLOWED HELLO LOSS 2 [0] [20] Z REQ RETRIES 2 [0] [20] Z TTL START 1 [1] [40] Z TTL INCREMENT 2 [1] [20] Z TTL THRESHOLD 7 [1] [60] Z ...
... R NODE TRAVERSAL TIME 0.040 s [0.01, 15.0] R MAX RREQ TIMEOUT 10.0 s [1.0, 100.0] R NET DIAMETER 35 [3] [100] Z ALLOWED HELLO LOSS 2 [0] [20] Z REQ RETRIES 2 [0] [20] Z TTL START 1 [1] [40] Z TTL INCREMENT 2 [1] [20] Z TTL THRESHOLD 7 [1] [60] Z ...
Resumen— El encaminamiento de datos en redes vehiculares presenta un desafío debido a los frecuen-tes e inesperados cambios de topología. En este estu-dio presentamos un algoritmo de optimización multi-objetivo paralelo para tratar este tipo de problemas con el fin de maximizar la cantidad de datos que se intercambian y minimizar los tiempos de transmisión. Esta forma de abordarlo es novedosa porque en este dominio la mayoría de trabajos de optimización em-plean técnicas secuenciales y/o mono-objetivas, hecho que limita su eficacia. Los resultados experimentales muestran que el algoritmo optimizado mejora de for-ma significativa a las otras propuestas en el estado del arte. Además , la aplicación de nuestro modelo parale-lo obtiene una eficiencia computacional mayor de un 86 %.
... The model combines the operation of an island-based scheme with a hyperheuristic approach to manage the choice of which lower-level algorithm configuration is executed on each island at each optimization stage. Similar models have been applied to mono-objective problems [42] and multi-objective problems [56]. The novelty resides in the adaptation of the model to the FAP, and in the incorporation of novel hyperheuristics inside the model. ...
... In [56] it was shown that for some problems, given a set of configurations, a dynamic-mapped scheme could lead to better results than a static-mapped heterogeneous scheme and than any of the homogeneous schemes that could be constituted. It suggests that the combination of different strategies working at the same time produces additional benefits. ...
This work presents a set of approaches used to deal with the frequency assignment problem (FAP), which is one of the key issues
in the design of GSM networks. The used formulation of FAP is focused on aspects which are relevant for real-world GSM networks.
A memetic algorithm, together with the specifically designed local search and variation operators, are presented. The memetic
algorithm obtains good quality solutions but it must be adapted for each instance to be solved. A parallel hyperheuristic-based
model was used to parallelize the approach and to avoid the requirement of the adaptation step of the memetic algorithm. The
model is a hybrid algorithm which combines a parallel island-based scheme with a hyperheuristic approach. The main operation
of the island-based model is kept, but the configurations of the memetic algorithms executed on each island are dynamically
mapped. The model grants more computational resources to those configurations that show a more promising behavior. For this
purpose two different criteria have been used in order to select the configurations. The first one is based on the improvements
that each configuration is able to achieve along the executions. The second one tries to detect synergies among the configurations,
i.e., detect which configurations obtain better solutions when they are cooperating. Computational results obtained for two
different real-world instances of the FAP demonstrate the validity of the proposed model. The new designed schemes have made
possible to improve the previously known best frequency plans for a real-world network.
KeywordsFrequency assignment problem–Memetic algorithms–Hyperheuristics–Parallel Island-based models–Cooperative strategies
... This study applied a specialized cellular MO Genetic Algorithm (GA) to optimize the coverage, the network use, and the broadcasting time. Later, other authors proposed other soft computing methods to deal with the same problem (Durillo et al. 2008;Segura et al. 2009). ...
This article analyzes the use of two parallel multi-objective soft computing algorithms to automatically search for high-quality settings of the Ad hoc On Demand Vector routing protocol for vehicular networks. These methods are based on an evolutionary algorithm and on a swarm intelligence approach. The experimental analysis demonstrates that the configurations computed by our optimization algorithms outperform other state-of-the-art optimized ones. In turn, the computational efficiency achieved by all the parallel versions is greater than 87 %. Therefore, the line of work presented in this article represents an efficient framework to improve vehicular communications.
... Due to the large number of possible combinations present in this NP-complete problem, we opted for the use of evolutionary algorithms to solve it. Recent trends in evolutionary techniques are focused on the use of parallelism to combine the properties of different algorithms [2]. If there is much difference among the algorithms, the greater the overall diversity Solving RC problem by using a parallel team of EAs 723 of the parallel team, encouraging the discovery of higher quality solutions. ...
In this work, we present a new approach to solve the location management problem by using the reporting cells strategy. Location management is a very important and complex problem in mobile computing which aims to minimize the costs involved. In the reporting cells location management scheme, some cells in the network are designated as reporting cells (RCs). The choice of these cells is not trivial because they affect directly to the cost of the mobile network. This article is focused on the use of high performance computing to execute a parallel heuristic that places optimally the RCs in a mobile network, minimizing its total cost. The main goal of this work is to demonstrate that the collaborative work of different evolutionary algorithms can obtain very good results. For this reason, we have implemented a parallel heuristic and six evolutionary algorithms that works in a parallel way on a cluster to solve the RCs problem.
... • Telecommunications, a field that has grown at a fast pace in recent years, posing difficult challenges to the research community due to the large size of the infrastructures, the need for real-time results, etc. Parallel metaheuristics have shown a great impact on facing these challenges, providing accurate and efficient solutions to the related optimization problems in network design [4,206,217,228], network routing [77,158,239,294], and network planning, especially in modern networks technologies such as cellular [4,249], mobile ad-hoc networks [158], vehicular networks, sensor networks, and peer-to-peer [169,207]. ...
The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.
Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed.
The use of evolutionary algorithms (EA
s) for solving multiobjective optimization problems has been very active in the last few years. The main reasons for this popularity are their ease of use with respect to classical mathematical programming techniques, their scalability, and their suitability for finding trade-off solutions in a single run. However, these algorithms may be computationally expensive because (1) many real-world optimization problems typically involve tasks demanding high computational resources and (2) they are aimed at finding a whole front of optimal solutions instead of searching for a single optimum. Parallelizing EAs emerges as a possible way of reducing the CPU
time down to affordable values, but it also allows researchers to use an advanced search engine – the parallel model – that provides the algorithms with an improved population diversity and enable them to cooperate with other (eventually nonevolutionary) techniques. The goal of this chapter is to provide the reader with an up-to-date review of the recent literature on parallel EAs for multiobjective optimization.
Multiobjectivisation transforms a mono-objective problem into a multiobjective one. The main aim of multiobjectivisation is to avoid stagnation in local optima by changing the landscape of the original fitness function. In this contribution, an analysis of different multiobjectivisation approaches has been performed. It has been carried out with a set of scalable mono-objective benchmark problems. The experimental evaluation has demonstrated the advantages of multiobjectivisation, both in terms of quality and saved resources. However, it has been revealed that it produces a negative effect in some cases. Some multiobjectivisation schemes require the specification of additional parameters which must be adapted for dealing with different problems. Multiobjectivisation with parameters has been proposed as a method to improve the performance of the whole optimisation scheme. Nevertheless, the parameter setting of an optimisation scheme which considers multiobjectivisation with parameters is usually more complex. In this work, a new model based on the usage of hyperheuristics to facilitate the application of multiobjectivisation with parameters is proposed. Experimental evaluation shows that this model has increased the robustness of the whole optimisation scheme.
This work presents a set of improvements and a performance analysis for a previously designed multi-objective optimisation
parallel model. The model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach
in order to grant more computational resources to those schemes that show a more promising behaviour. The main aim is to raise
the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be
tackled since the strengths of one algorithm can compensate for the weaknesses of another. A contribution-based hyperheuristic
previously presented in the literature is compared with a novel hypervolume-based hyperheuristic. The computational results
obtained for some tests available in the literature demonstrate the superiority of the hypervolume-based hyperheuristic when
compared to the contribution-based hyperheuristic and to other standard parallel models.
The Location Management of a mobile network is a major problem nowadays. One of the most popular strategies used to solve
this problem is the Reporting Cells. To configure a mobile network is necessary to indicate what cells of the network are
going to operate as Reporting Cells (RC). The choice of these cells is not trivial because they affect directly to the cost
of the mobile network. Hereby we present a parallel cooperative strategy of evolutionary algorithms to solve the RC problem.
This method tries to solve the Location Management, placing optimally the RC in a mobile network, minimizing its cost. Due
to the large amount of solutions that we can find, this problem is suitable for being solved with evolutionary strategies.
Our work consists in the implementation of some evolutionary algorithms that obtain very good results working in a parallel
way on a cluster.
This work presents several approaches used to deal with the 2D packing problem proposed in the GECCO 2008 contest session. A memetic algorithm, together with the specifically designed local search and variation operators, are presented. A novel parallel model was used to parallelize the approach. The model is a hybrid algorithm which combines a parallel island-based scheme with a hyperheuristic approach. An adaptive behavior is added to the island-based model by applying the hyperheuristic procedure. The main operation of the island-based model is kept, but the configurations of the memetic algorithms executed on each island are dynamically mapped. The model grants more computational resources to those configurations that show a more promising behavior. For this purpose a specific criterion was designed in order to select the configurations with better success expectations. Computational results obtained for the contest problem demonstrate the validity of the proposed model. The best reported solutions for the problem contest instance have been achieved by using the here presented approaches.
Mobile Ad Hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, broadcasting becomes an operation of capital importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multi-objective problem targeting three goals: reaching as many devices as possible, minimizing the network utilization, and reducing the duration time of the broadcasting process. In this paper, we study the fine-tuning of broadcasting strategies by using a cellular multi-objective genetic algorithm (cMOGA) which computes a Pareto front of solutions to empower a human designer with the ability of choosing the preferred configuration for the network. We define two formulations of the problem, one with three objectives and another one with two objectives plus a constraint. For our tests, a benchmark of three realistic environments for metropolitan MANETs has been defined. Our experiments using a complex and realistic MANET simulator reveal that cMOGA is a promising approach to solve the optimum broadcast problem.
This paper introduces a new cellular genetic algorithm for solving multiobjective contin-uous optimization problems. Our approach is characterized by using an external archive to store non-dominated solutions and a feedback mechanism in which solutions from this archive randomly replaces existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been evaluated with both constrained and unconstrained problems and compared against NSGA-II and SPEA2, two state-of-the-art evolutionary multiob-jective optimizers. For the used benchmark, preliminary experiments indicate that MOCell obtains competitive results in terms of convergence, and it clearly outperforms the other two compared al-gorithms concerning the diversity of solutions along the Pareto front.
A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means of an extensivetestbed it is demonstrated that the new methodconverges faster and with more certainty than manyother acclaimed global optimization methods. The newmethod requires few control variables, is robust, easyto use, and lends itself very well to parallelcomputation.
Mobile Ad hoc NETworks (MANETs) are dynamic networks populated by mobile stations. Stations in MANETs are usually laptops, PDAs or mobile phones. These devices feature Bluetooth and/or IEEE 802.11 (WiFi) network interfaces and communicate in a decentralized manner. Mobility is a key feature of MANETs. Because of their high cost and their lack of flexibility of such networks, experimentation is mostly achievable through simulation. Numerous tools exist for MANETs simulation, including ns-2 and GloMoSim which are the two most popular ones. This paper provides a State of the Art of MANETs simulators and associated simulation techniques. First it gives an overview of the domain. Then it provides a map of the main characteristics that MANETs simulation tools should feature and the current support of these. Finally, a description for each simulator is provided, including an explanation of what make them appealing solutions.
The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the duration of the operation itself. This research, which has been developed within the OPLINK project (http://oplink.lcc.uma.es), faces a wide study about this problem in metropolitan MANETs with up to seven different advanced multiobjective metaheuristics. They all compute Pareto fronts of solutions which empower a human designer with the ability of choosing the preferred configuration for the network. The quality of these fronts is evaluated by using the hypervolume metric. The obtained results show that the SPEA2 algorithm is the most accurate metaheuristic for solving the broadcasting problem.
In this paper we study a number of issues related to the design of a cellular genetic algorithm (cGA) for multiobjective optimiza- tion. We take as an starting point an algorithm following the canonical cGA model, i.e., each individual interacts with those ones belonging to its neighborhood, so that a new individual is obtained using the typical se- lection, crossover, and mutation operators within this neighborhood. An external archive is used to store the non-dominated solutions found dur- ing the evolution process. With this basic model in mind, there are many difierent design issues that can be faced. Among them, we focus here on the synchronous/asynchronous feature of the cGA, the feedback of the search experience contained in the archive into the algorithm, and two difierent replacement strategies. We evaluate the resulting algorithms us- ing a benchmark of problems and compare the best of them against two state-of-the-art genetic algorithms for multiobjective optimization. The obtained results indicate that the cGA model is a promising approach to solve this kind of problem.
This paper presents a new broadcasting protocol called Delayed Flooding with Cumulative Neighborhood (DFCN) designed for wireless ad hoc networks. DFCN enables bandwidth-efficient broadcasting in wide area network composed of large number of mobile devices. The protocol was validated trough simulation which proved its efficiency and cost-effectiveness. Comparison with other well known protocols has shown that the proposed protocol outperforms them in such terms as a number of emissions and redundant receptions.
This paper demonstrates that the self-adaptive technique of Dieren tial Evolution (DE) can be simply used for solving a multi- objective optimization problem where parameters are interdependent. The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Dieren tial Evolution scheme, and results are reported on a rotated problem which has presented diculties using existing Multi-objective Genetic Algorithms. The Dieren tial Evolution variant of the NSGA-II has demonstrated rotational invariance and su- perior performance over the NSGA-II on this problem.
This work presents a new parallel model for the solution of multi-objective optimization problems. The model combines a parallel island-based scheme with a hyperheuristic approach in order to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. Computational results demonstrate that the model grants more computational resources to those algorithms that show a more promising behaviour.
Engineering of mobile telecommunication networks endures two major
problems: the design of the network and the frequency assignment. We
address the first problem in this paper, which has been formulated as a
multiobjective constrained combinatorial optimisation problem. We
propose a genetic algorithm (GA) that aims to approximate the Pareto
frontier of the problem. Advanced techniques have been used, such as
Pareto ranking, sharing and elitism. The GA has been implemented in
parallel on a network of workstations to speed up the search. To
evaluate the performance of the GA, we have introduced two new
quantitative indicators: the entropy and the contribution. Encouraging
results are obtained on real-life problems
This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the major difference to Genetic Algorithms, since it allows for an on-line adaptation of strategy parameters without exogenous control. 1. Introduction Within nearly 20 years of research it has been demonstrated that the search process of natural evolution can yield very robust search algorithms on a computer, although the imitations used are crude simplifications of the natural model. Several of these Evolutionary Algorithms are nowadays broadly accepted, the most important ones being the Genetic Algorithm (GA) and Classifier System (CFS) by Holland 7; 8 , the Evolution Strategy (ES) by Rechenberg and Schwefel 11; 12 , and the G...
We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (1 + 1) evolution strategy, employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. (1+1)-PAES is intended as a good baseline approach, against which more involved methods may be compared, and may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We also introduce (1 + ) and ( + ) variants of PAES as good extensions to the basic algorithm. Six variants of PAES are compared with variants of the Niched Pareto GA and the Nondominated Sorting GA over a divers...
Stochastic local search (SLS) algorithms for the propositional satisfiability problem (SAT) have been successfully applied to solve suitably encoded search problems from various domains. One drawback of these algorithms is that they are usually incomplete. We refine the notion of incompleteness for stochastic decision algorithms by introducing the notion of "probabilistic asymptotic completeness" (PAC) and prove for a number of well-known SLS algorithms whether or not they have this property. We also give evidence for the practical impact of the PAC property and show how to achieve the PAC property and significantly improved performance in practice for some of the most powerful SLS algorithms for SAT, using a simple and general technique called "random walk extension".
Over the last few years, personal communication devices have invaded most developed countries and today, the majority of the population owns a mobile phone and most people use personal digital assistants, mobile computers, etc. This tendency is reinforced and occurs at the same time with a new trend: most of these devices get equipped with one or several wireless networking interfaces. Practically, Wi-Fi or/and Bluetooth-enabled devices become of frequent use. More than allowing the connection to some access point (which can be found in airports, train stations, city-centers, restaurants, etc), these interfaces also permit to interconnect directly with one another in a decentralized way and hence to self-organize into “ad hoc networks”.A mobile ad hoc network is a set of mobile nodes able to communicate with other nodes in their surroundings. These wireless communications happen in a peer-to-peer manner, without relying on any predefined infrastructure. Today, mobile ad hoc networks are mainly used for sensing, gaming and military purposes. But the steadily wider adoption of wireless technologies in daily life let one foresee the next generation of mobile ad hoc network applications: environmental and medical monitoring, groupware, customer-to- customer applications, risk management, entertainment, advertising, etc. In this document we will consider a subclass of mobile ad hoc network called “delay tolerant networks”. In such network, the mobility is hardly predictable and the disruption of connection is a common and normal phenomenon.In order to enable the development and spreading of these applications, a number of issues have to be solved. First, in such networks, end-to-end connectivity cannot be guaranteed. Indeed mobile ad hoc networks may be partitioned and nodes may be spo- radically present in the network. As such, mobile ad hoc networks can be considered as Delay Tolerant Networks (DTN). Second, the topology of the network changes over time because of the mobility of the stations. Then, the way the communication primitives were implemented in the context of wired networks is no longer applicable. It is hence necessary to propose new algorithms to take those primitives into account, like broadcasting that serves as a basic pattern for the design of many mobile ad hoc network applications.The design and implementation of such communication schemes, and more generally of mobile ad hoc network applications, can be achieved using two different ways: either by building a real network, or by resorting to modelling and simulation. Theoretical approaches failed at modeling the inherent complexity of mobile ad hoc networks in a comprehensive manner. Indeed, ad hoc networks are decentralised systems which are ruled by a variety of elements including radio signal, network traffic, mobility, collaborative behavior, etc. If theoretical approaches certainly are useful for represent one or the other elements, they do not suit the modelling of a system as a whole. In the context of this work, where city-scale environment were considered, simulation was hence unavoidable.The development of such simulators took place at the crossroad of some projects in relation to complex system modelling, optimization and middleware design for mobile ad hoc networks, and conducted in several European countries.This diversity led to the design of a custom simulator called Madhoc. Indeed the mobile ad hoc simulators which are already available on the market most often are designed in such a way they allow the simulation of specific applications. They also generally suffer from a complex architecture (often poorly documented) that make them hardly adaptable to other applications. Consequently, none of them apparently turn out to be flexible enough so as it would have met our diverse needs. Madhoc captures the major characteristics of DTNs, by providing an extendable set of mobility models as well as a framework for the definition of new applications.By looking at the State of Art protocols for mobile ad hoc networks and delay tolerant networks, it appeared that the broadcast issue had not yet been solved in a satisfactory manner. Indeed none of the protocols proposed so far proceed nicely in specific conditions of delay tolerant networking. Madhoc was then primarily used for the investigation of the broadcasting issue. In this specific context, networks composed of thousands devices using a variety of wireless technologies were considered. These networks are partitioned and ex- hibit heterogeneous densities. This led to the design of a bandwidth-efficient broadcasting protocol called DFCN.
IntroductionIn multiobjective optimisation the aim is to find solutions that represent a compromise betweenthe various (sometimes conflicting) criteria used to evaluate the quality of solutions. A solutionx is said to be non-dominated with respect to a set of solutions S if there is no other solution in Sthat is, as good as x in all the criteria and better than x in at least one of the criteria. In Paretooptimisation the goal is to find a set of solutions that is representative of the...
Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables.The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization. A companion CD includes DE-based optimization software in several programming languages.
This work presents a parallel framework for the solution of multi-objective optimization problems. The framework implements
some of the best known multi-objective evolutionary algorithms. The framework architecture makes usage of configuration files
to provide a more extensive and simple customization environment than other similar tools. A wide variety of configuration
options can be specified to adapt the software behaviour to many different parallel models, including a new adaptive model
which dynamically grants more computational resourcesto the most promising algorithms. The plugin-based architecture of the
framework minimizes the final user effort required to incorporate their own problems and evolutionary algorithms, and facilitates
the tool maintenance. The flexibility of the approach has been tested by configuring a standard homogeneous island-based model
and a self-adaptive model. The computational results obtained for problems with different granularity demonstrate the efficiency
of the provided parallel implementation.
While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.
. Since 1985 various evolutionary approaches to multiobjectiveoptimization have been developed, capable of searching for multiplesolutions concurrently in a single run. But the few comparative studies ofdifferent methods available to date are mostly qualitative and restrictedto two approaches. In this paper an extensive, quantitative comparisonis presented, applying four multiobjective evolutionary algorithms to anextended 0/1 knapsack problem.1 IntroductionMany real-world...
This paper discusses how preference information of the deci- sion maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary per- formance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary in- dicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popu- lar algorithms, namely NSGA-II and SPEA2, with respect to different performance measures.
The Strength Pareto Evolutionary Algorithm (SPEA) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies ,2 SPEA has shown very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference in various recent investigations? Furthermore, it has been used in different applications. 4 In this paper, an improved version, namely SPEA2, is...
The success of the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer (mainly when dealing with continuous search spaces) has motivated researchers to extend the use of this bio-inspired technique to other areas. One of them is multi-objective optimization. Despite the fact that the first proposal of a Multi-Objective Particle Swarm Optimizer (MOPSO) is over six years old, a considerable number of other algorithms have been proposed since then. This paper presents a comprehensive review of the various MOPSOs reported in the specialized literature. As part of this review, we include a classification of the approaches, and we identify the main features of each proposal. In the last part of the paper, we list some of the topics within this field that we consider as promising areas of future research.
Developing multiobjective evolutionary algorithms (MOEAs) involves thoroughly addressing the issues of efficiency and effectiveness. Once convinced of an MOEA's effectiveness the researcher often desires to reduce execution time and/or resource expenditure, which naturally leads to considering the MOEA's parallelization. Parallel MOEAs (pMOEAs) or distributed MOEAs are relatively new developments with few associated publications. pMOEA creation is not a simple task, involving analyzing various parallel paradigms and associated parameters. Thus, a thorough discussion of the major parallelized MOEA paradigms is included in this paper and succinct observations are made regarding an analysis of the current literature. Specifically, a previous MOEA notation is extended into the pMOEA domain to enable precise description and identification of various sets of interest. Innovative concepts for pMOEA migration, replacement and niching schemes are discussed, as well as presenting the first known generic pMOEA formulation. Taken together, this paper's analyses in conjunction with an original pMOEA design serve as a pedagogical framework and example of the necessary process to implement an efficient and effective pMOEA.
An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of approximation sets. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. Sometimes, pairs of approximation sets are also considered. In this study, we provide a rigorous analysis of the limitations underlying this type of quality assessment. To this end, a mathematical framework is developed which allows one to classify and discuss existing techniques.
Multi-objective evolutionary algorithms (MOEAs) that use
non-dominated sorting and sharing have been criticized mainly for: (1)
their O(MN3) computational complexity (where M is the number
of objectives and N is the population size); (2) their non-elitism
approach; and (3) the need to specify a sharing parameter. In this
paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II
(Non-dominated Sorting Genetic Algorithm II), which alleviates all of
the above three difficulties. Specifically, a fast non-dominated sorting
approach with O(MN2) computational complexity is presented.
Also, a selection operator is presented that creates a mating pool by
combining the parent and offspring populations and selecting the best N
solutions (with respect to fitness and spread). Simulation results on
difficult test problems show that NSGA-II is able, for most problems, to
find a much better spread of solutions and better convergence near the
true Pareto-optimal front compared to the Pareto-archived evolution
strategy and the strength-Pareto evolutionary algorithm - two other
elitist MOEAs that pay special attention to creating a diverse
Pareto-optimal front. Moreover, we modify the definition of dominance in
order to solve constrained multi-objective problems efficiently.
Simulation results of the constrained NSGA-II on a number of test
problems, including a five-objective, seven-constraint nonlinear
problem, are compared with another constrained multi-objective
optimizer, and the much better performance of NSGA-II is observed
A simple optimization procedure for constraint based problems which works without an objective function is described. The absence of an objective function makes the problem formulation particularly simple. The new method lends itself to parallel computation and is well suited for tasks where a family of solutions is required, trade-off situations have to be dealt with or the design center has to be found. ________________________________________ 1) International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn -Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email: rainer.storn@zfe.siemens.de. 2 1. Introduction The design of a technical system is usually associated with the process of properly choosing some system parameters such that the technical system meets its specifications. The parameter choosing process can also be regarded as an o...
This article provides a snapshot of work underway within the Mobile Ad hoc Networks (manet) Working Group (WG) [1] of the Internet Engineering Task Force (IETF). The article summarizes the proceedings of the last manet WG meeting and presents a snapshot of the protocols currently under consideration within the WG
Stochastic local search (SLS) algorithms have been successfully applied to hard combinatorial problems from different domains. One important feature of SLS algorithms is the fact that their run-time behavior is characterized by a random variable. Consequently, the detailed knowledge of the run-time distribution provides important information for the analysis of SLS algorithms. In this paper we investigate the empirical run-time distributions for several state-of-the-art stochastic local search algorithms for SAT and CSP. Using statistical analysis techniques, we show that on a variety of problems from both randomized distributions and encodings of the blocks world planning and graph coloring domains, the observed run-time behavior can be characterized by exponential distributions. As a first direct consequence of this result, we establish that these algorithms can be easily parallelized with optimal speedup. Introduction Recent successes in using stochastic local search (SLS) algorit...
The Strength Pareto Evolutionary Algorithm (SPEA) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiob- jective optimization problems. In different studies ,2 SPEA has shown very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference in various recent investigations? Furthermore, it has been used in different applications. 4 In this paper, an improved version, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. The comparison of SPEA2 with SPEA and two other modern elitist methods, PESA and NSGA-II, on different test problems yields promising results.
Network wide broadcasting in Mobile Ad Hoc Networks provides important control and route establishment functionality for a number of unicast and multicast protocols. Considering its wide use as a building block for other network layer protocols, the MANET community needs to standardize a single methodology that efficiently delivers a packet from one node to all other network nodes. Despite a considerable number of proposed broadcasting schemes, no comprehensive comparative analysis has been previously done. This paper provides such analysis by classifying existing broadcasting schemes into categories and simulating a subset of each category, thus supplying a condensed but comprehensive side by side comparison. The simulations are designed to pinpoint, in each category, specific failures to network conditions that are relevant to MANETs, e.g., bandwidth congestion and dynamic topologies. In addition, protocol extensions using adaptive responses to network conditions are proposed, implemented and analyzed for one broadcasting scheme that performs well in the comparative study.
The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference in various recent investigations, e.g., (Corne, Knowles, and Oates 2000). Furthermore, it has been used in different applications, e.g., (Lahanas, Milickovic, Baltas, and Zamboglou 2001). In this paper, an improved version, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. The comparison of SPEA2 with SPEA and two other modern elitist methods, PESA and NSGA-II, on different test problems yields promising results. 1
In this paper, a flexible yet efficient algorithm for solving engineering design optimization problems is presented. The algorithm is developed based on both binary-coded and realcoded genetic algorithms (GAs). Since both GAs are used, the variables involving discrete, continuous, and zero-one variables are handled quite efficiently. The algorithm restricts its search only to the permissible values of the variables, thereby reducing the search effort in converging to the optimum solution. The efficiency and ease of application of the proposed method is demonstrated by solving three different mechanical component design problems borrowed from the optimization literature. The proposed technique is compared with binarycoded genetic algorithms, Augmented Lagrange multiplier method, Branch and Bound method and Hooke and Jeeves pattern search method. In all cases, the solutions obtained using the proposed technique are superior than those obtained with other methods. These results ...
The success of binary-coded genetic algorithms (GAs) in problems having discrete search space largely depends on the coding used to represent the problem variables and on the crossover operator that propagates building-blocks from parent strings to children strings. In solving optimization problems having continuous search space, binary-coded GAs discretize the search space by using a coding of the problem variables in binary strings. However, the coding of real-valued variables in finite-length strings causes a number of difficulties---inability to achieve arbitrary precision in the obtained solution, fixed mapping of problem variables, inherent Hamming cliff problem associated with the binary coding, and processing of Holland's schemata in continuous search space. Although, a number of real-coded GAs are developed to solve optimization problems having a continuous search space, the search powers of these crossover operators are not adequate. In this paper, the search power...
Indicator-Based Selection in Multiobjective Search
- E Zitzler
- S Künzli
- X Yao
- E K Burke
- J A Lozano
- J Smith
- J J Merelo-Guervós
- J A Bullinaria
- J E Rowe
Simulated binary crossover for continuous search space
- K Deb
- R B Agrawal
- K. Deb