This paper studies a traffic grooming in wavelength-division multiplexing (WDM) mesh networks for the SONET/SDH streams requested between node pairs. The traffic could be groomed at the access node before converting t...
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(纸本)0780379454
This paper studies a traffic grooming in wavelength-division multiplexing (WDM) mesh networks for the SONET/SDH streams requested between node pairs. The traffic could be groomed at the access node before converting to an optical signal carried in the all-optical network. We design a virtual topology with a given physical topology to satisfy multiple objectives and constraints. The grooming problem of a static demand is considered as an optimization problem. The traditional algorithms found in the literatures mostly focus on a single objective either to maximize the performance or to minimize the cost. We propose a multi-objective evolutionary algorithm to solve a grooming problem that optimizes multiple objectives all together at the same time. In this paper we consider the optimization of three objectives: maximize the traffic throughput, minimize the number of transceivers, and minimize the average propagation delay or average hop counts. The simulation results show that our approach is superior to an existing heuristic approaches in an acceptable running time.
multi-objective evolutionary algorithm (MOEA) is used to solve multi-objective optimization problems (MOOPs). In order to improve the efficiency of MOEA, a fast method of constructing non-dominated set called Arena...
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multi-objective evolutionary algorithm (MOEA) is used to solve multi-objective optimization problems (MOOPs). In order to improve the efficiency of MOEA, a fast method of constructing non-dominated set called Arena' s Principle is suggested. The time complexity of the Arena's Principle is better than that of Deb's and Jensen's on constructing non-dominated set. In the experiments, Arena's Principle is compared with Deb' and Jensen's approach. It is shown that the experimental results satisfy the theoretical analysis.
A hybrid heuristic approach combining multi-objectiveevolutionary and problem-specific local search methods is proposed to support the risk-return analysis of credit portfolios. Its goal is to compute approximations ...
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A hybrid heuristic approach combining multi-objectiveevolutionary and problem-specific local search methods is proposed to support the risk-return analysis of credit portfolios. Its goal is to compute approximations of discrete sets of Pareto-efficient portfolio structures concerning both the respective portfolio return and the respective portfolio risk using the non-linear, non-convex Credit-Value-at-Risk downside risk measure which is relevant to real world credit portfolio optimization. In addition, constraints like capital budget restrictions are considered in the hybrid heuristic framework. The computational complexity of selected parts of the algorithm is analyzed. Moreover, empirical results indicate that the hybrid method is superior in convergence speed to a non-hybrid evolutionary approach and finds approximations of risk-return efficient portfolios within reasonable time. (C) 2003 Elsevier B.V. All rights reserved.
multi-objective evolutionary algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search an...
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multi-objective evolutionary algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete multi-objective Optimization Problem (MOP): multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.
Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitnes...
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Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.
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