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Differential evolution (DE) is an efficient and robust evolutionary algorithm, which has been widely and successfully applied to solve global optimization problems. Although many methods have been developed based on the population topology to improve the performance of DE, the effects of population topology interacted with the functions being optimized are not considered in most of the algorithm designs...
A perpendicular multi-layer (PML) architecture with multiple controllers and a dynamic orchestra plane (DOP) for multi-domain software defined optical networks is proposed in this paper. Cross-domain services with on-demand bandwidth can be deployed via unified interfaces provided by DOP. Practical capture of signal processing and emulation results are presented.
In this paper, by combination of some approaches we propose a new approach of Differential Evolution (DE) algorithm, called DE with nonlinear simplex method and dynamic neighborhood search (DENNS). In our approach the nonlinear simplex method (NSM) is used for population initialization and local neighborhood search. Moreover, local and global neighborhood search operators are employed to generate...
By learning from different particle in local neighborhood and global neighborhood Particle Swarm Optimization (PSO) algorithm achieved a trade-off between exploration and exploitation abilities. In this paper, we propose a new approach by combining diversity mechanism and neighborhood search strategies, called a novel enhanced PSO method with Diversity and Neighborhood Search (EPSODNS). In this paper...
Topology optimization of truss structures is considered in this paper. Trusses are widely used in various constructions: bridges, towers, roof supporting structures. Topology optimization of trusses requires large amount of computing resources. Therefore distributed computer networks are used to solve this kind of problems. In this paper a distributed branch-and-bound combinatorial algorithm for topology...
This paper presents a novel Differential Evolution (DE) algorithm, called DE enhanced by neighborhood search (DENS), which differs from pervious works of utilizing the neighborhood search in DE, such as DE with neighborhood search (NSDE) and self-adaptive DE with neighborhood search (SaNSDE). In DENS, we focus on searching the neighbors of individuals, while the latter two algorithms (NSDE and SaNSDE)...
How to distribute radio spectrum across network nodes is a critical problem in spectrum auctions and management. In this paper, we consider the problem of distributing spectrum using SINR-driven physical interference models. We propose Optimus, a new line of approximation algorithms that perform within a constant distance of min {2?? + 1, 10} from the optimum in terms of spectrum usage efficiency,...
Application-specific Network-on-Chip (NoC) in MPSoC designs often requires irregular topology to optimize power and performance. However, efficient deadlock-free routing, which avoids restricting critical routes and also does not significantly increase power for irregular NoC, has remained an open problem until now. In this paper an application-specific cycle elimination and splitting (ACES) method...
A hybrid differential evolution (HDE) approach derived from both the differential evolution (DE) and the particle swarm optimization (PSO) is proposed. In HDE, individuals in a new generation are created, not only by crossover and mutation operation as in DE, but also by PSO operations. The concepts of inertia weight and neighbor topology are adopted in HDE. The former is employed to provide consistency...
Particle Swarm Optimization (PSO) is arguably one of the most popular nature- inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO is mostly based on the gbest (global best) particle topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes. The present standard PSO (SPSO 2007) uses an lbest (local...
This paper introduced an almost control parameters free modified differential evolution for global optimization problems. The modifications are derived from the mechanisms of particle swarm optimization viz., topologies, inertial weight, neighborhood best and individual best, with which each individual performed the mutation operator based on its current position, the neighborhood best and its individual...
To gain the equivalent mechanical parameter of rock in underground engineering and to improve the precision of numerical simulation of excavation, a new multi-information intelligent recognition method of rock mechanical parameter is put forward. By coupling artificial neural network and genetic algorithm as a whole algorithm and by building an associated fitness function to absorb multi-information,...
In the field of evolutionary algorithm, Differential Evolution (DE) has gained a great focus due to its strong global optimization capability and simple implementation. In DE, mutant vector, which plays the role of leading individuals to explore the search space, is generated by combining a base vector and a difference vector. However, these two vectors are selected either randomly or greedily according...
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