A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems
TL;DR: This paper adapts the Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for the new problem by introducing two adjustments, and model the problem as a bi-objective optimization problem to minimize the total cost and the maximum transmission delay of a multicast.
About: This article is published in Information Sciences.The article was published on 2013-06-01 and is currently open access. It has received 36 citations till now. The article focuses on the topics: Linear network coding & Multicast.
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TL;DR: The simulation results on benchmark instances demonstrate that with the integrated five extended mechanisms, the modified ant colony optimization algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time.
Abstract: This paper presents a modified ant colony optimization (ACO) approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the concerned problem: 1) a multidimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the capability of heuristic search (neighboring area search); 3) a tabu-table-based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; and 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ACO can well exploit the global and local information of routing-related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the integrated five extended mechanisms, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time.
67 citations
13 Jun 2018
TL;DR: The socio-ecological fabric of innovation in the extractive industries is considered through an integrative approach that brings together engineers, natural scientists, and social scientists as mentioned in this paper, giving an empirically grounded and realistic evaluation of the innovations in this sector.
Abstract: This book considers the most contemporary innovations propelling the extractive industries forward while also creating new environmental and social challenges. The socio-ecological fabric of innovation in the extractive industries is considered through an integrative approach that brings together engineers, natural scientists, and social scientists—academics and practitioners—giving an empirically grounded and realistic evaluation of the innovations in this sector. It synthesizes a series of questions including: - Why have these sectors been historically slow to innovate? - What specific strategies can improve innovation and uptake of new technologies? - What new forms of technology will shape the sector in the decades ahead? - What impact will new technologies have on resource extraction and energy production? - How are digital technologies changing the competitive landscape and industry architecture? - How will new technologies impact sustainability of the sector and can technologies improve social performance and environmental stewardship?
38 citations
TL;DR: This paper designs the cognitive behaviors summarized in the cognitive science for the network nodes and proposes a QoS multicast routing protocol oriented to cognitive network, named as CogMRT, which has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors.
Abstract: The human intervention in the network management and maintenance should be reduced to alleviate the ever-increasing spatial and temporal complexity. By mimicking the cognitive behaviors of human being, the cognitive network improves the scalability, self-adaptation, self-organization, and self-protection in the network. To implement the cognitive network, the cognitive behaviors for the network nodes need to be carefully designed. Quality of service (QoS) multicast is an important network problem. Therefore, it is appealing to develop an effective QoS multicast routing protocol oriented to cognitive network.
In this paper, we design the cognitive behaviors summarized in the cognitive science for the network nodes. Based on the cognitive behaviors, we propose a QoS multicast routing protocol oriented to cognitive network, named as CogMRT. It is a distributed protocol where each node only maintains local information. The routing search is in a hop by hop way. Inspired by the small-world phenomenon, the cognitive behaviors help to accumulate the experiential route information. Since the QoS multicast routing is a typical combinatorial optimization problem and it is proved to be NP-Complete, we have applied the competitive coevolutionary algorithm (CCA) for the multicast tree construction. The CCA adopts novel encoding method and genetic operations which leverage the characteristics of the problem. We implement and evaluate CogMRT and other two promising alternative protocols in NS2 platform. The results show that CogMRT has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors.
29 citations
Cites methods from "A nondominated sorting genetic algo..."
...In [36], the network coding based multicast routing problem has been investigated with two optimization objectives, i....
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TL;DR: The multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously.
26 citations
Cites background or methods from "A nondominated sorting genetic algo..."
...This paper extends the problem models in [36, 40], and establishes a new multi-objective NCM routing model, where all the key factors in NCM data transmission, namely, the coding cost, link cost and the average end-to-end delay, are formulated as three objectives....
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...generated networks (Rnd-1 to Rnd-8, with network size from 20 to 50, see details in [40])....
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...However, the problem concerned in the paper is highly complicated and constrained, and feasible individuals only account for a very small proportion of the population [40]....
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...objective optimization problems and MOPs [10, 11, 12, 13, 14, 15, 19, 40]....
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...Therefore, our previous work investigated the estimated distribution of feasible solutions over the entire search space [40]....
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TL;DR: In this paper, the authors proposed a model for social model learning at the Central Economics & Mathematics Institute (CEMI), Russian Academy of Sciences, Nakhimovsky pr. 47, Moscow 117418, Russia 3 Department of Computing, Sumy State University, Rimsky-Korsakov St. 2, Summy 40007, Ukraine 4 Department of Industrial Engineering, Texas Tech University, P.O. Box 43061, Lubbock, TX 79409, USA 5 Facultad de Ciencias Fisico-Matematicas
Abstract: 1Department of Systems & Industrial Engineering, Tecnologico de Monterrey (ITESM), Campus Monterrey, Avenida Eugenio Garza Sada 2501 Sur, 64849 Monterrey, NL, Mexico 2Department of Social Modeling, Central Economics & Mathematics Institute (CEMI), Russian Academy of Sciences, Nakhimovsky pr. 47, Moscow 117418, Russia 3Department of Computing, Sumy State University, Rimsky-Korsakov St. 2, Sumy 40007, Ukraine 4Department of Industrial Engineering, Texas Tech University, P.O. Box 43061, Lubbock, TX 79409, USA 5Facultad de Ciencias Fisico-Matematicas, Universidad Autonoma de Nuevo Leon, Avenida Universidad S/N, 66450 San Nicolas de los Garza, NL, Mexico 6Kharkiv Institute of Banking of the University of Banking of the National Bank of Ukraine, Peremogy Avenue 55, Kharkiv 61174, Ukraine
20 citations
References
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TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Abstract: Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) 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(MN/sup 2/) 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.
37,111 citations
TL;DR: This work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated, and by employing coding at the nodes, which the work refers to as network coding, bandwidth can in general be saved.
Abstract: We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be multicast to certain sets of destinations. We assume that the information sources are mutually independent. The problem is to characterize the admissible coding rate region. This model subsumes all previously studied models along the same line. We study the problem with one information source, and we have obtained a simple characterization of the admissible coding rate region. Our result can be regarded as the max-flow min-cut theorem for network information flow. Contrary to one's intuition, our work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated. Rather, by employing coding at the nodes, which we refer to as network coding, bandwidth can in general be saved. This finding may have significant impact on future design of switching systems.
8,533 citations
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Abstract: Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.
7,512 citations
"A nondominated sorting genetic algo..." refers background in this paper
...− SPEA2: the strength Pareto evolutionary algorithm 2 [53], one of the widely applied and recognized multiobjective evolutionary algorithms....
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TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Abstract: Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.
6,657 citations
"A nondominated sorting genetic algo..." refers methods in this paper
...2 Performance Measures To evaluate the performance of a multiobjective evolutionary algorithm from various aspects, we use the following three performance metrics which have been widely recognized in the studies of multiobjective optimization problems [33][41][51]: Inverted generational distance (IGD): Let PFref be a reference set of nondominated solutions of the true PF and PFknown be the set of nondominated solutions obtained by an algorithm....
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TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
Abstract: In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.
6,411 citations