Fuzzy Evolutionary Hybrid Metaheuristic for Network Topology Design

Abstract. Topology design of enterprise networks is a hard combina- torial optimization problem. It has numerous constraints, several objec- tives, and a very noisy solution space. Besides the NP-hard nature of this problem, many of the performance metrics of the network can only be estimated, given their dependence on many of the dynamic aspects of the network, e.g., routing and number and type of tra�c sources. Fur- ther, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present a fuzzy evolutionary hybrid metaheuristic for network topology design. This approach is dominance preserving and scales well with larger problem instances and a larger number of objective cri- teria. Experimental results are provided

Fuzzy evolutionary hybrid metaheuristic for network topology design

Topology design of enterprise networks is a hard combinatorial optimization problem. It has numerous constraints, several objectives, and a very noisy solution space. Besides the NP-hard nature of this problem, many of the performance metrics of the network can only be estimated, given their dependence on many of the dynamic aspects of the network, e.g., routing and number and type of traffic sources. Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present a fuzzy evolutionary hybrid metaheuristic for network topology design. This approach is dominance preserving and scales well with larger problem instances and a larger number of objective criteria. Experimental results are provided

Fuzzy evolutionary hybrid metaheuristic for network topology design

Topology design of enterprise networks is a hard combinatorial optimization problem. It has numerous constraints, several objectives, and a very noisy solution space. Besides the NP-hard nature of this problem, many of the performance metrics of the network can only be estimated, given their dependence on many of the dynamic aspects of the network, e.g., routing and number and type of traffic sources. Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present a fuzzy evolutionary hybrid metaheuristic for network topology design. This approach is dominance preserving and scales well with larger problem instances and a larger number of objective criteria. Experimental results are provided

GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

Wind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind farm layout design problem has traditionally been solved using nature-inspired algorithms. An important issue in nature-inspired algorithms is the termination condition, which governs the execution time of the algorithm. To optimize the execution time, appropriate termination conditions should be employed. This study proposes the concept of a rewarding mechanism to achieve optimization in termination conditions while maintaining the solution quality. The proposed rewarding mechanism, adopted from the stochastic evolution algorithm, is incorporated into a genetic algorithm. The proposed genetic algorithm with the rewarding mechanism (GARM) is empirically tested using real data from a potential wind farm site with different rewarding iterations