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Differential evolution (DE) has been proven to be a simple and powerful evolutionary algorithm, and obtains many successful applications in scientific and engineering fields. The mutation strategy plays the key role in DE for finding global optimal solutions. In most of the DE algorithms, the base and difference vectors are randomly selected from the current population. Furthermore, both the neighborhood...
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...
In IP-over-WDM networks, a virtual topology is placed over a physical topology of the optical network. Since a simple link failure or a node failure on the physical topology can lead to a huge loss of information, an important challenge is to make the routing of the virtual topology on to the physical topology survivable. This problem is known as the Survivable Virtual Topology Mapping (SVTM), which...
In this paper, a neighborhood mutation strategy is proposed and integrated with various niching differential evolution (DE) algorithms to solve multimodal optimization problems. Although variants of DE are highly effective in locating a single global optimum, no DE variant performs competitively when solving multi-optima problems. In the proposed neighborhood based differential evolution, the mutation...
The influence of parallelism on the performance of competitive adaptive differential evolution is studied. Two serial competitive differential evolution variants described in literature and sixteen novel parallel variants were experimentally compared. All the parallel differential evolution variants in this study are based on a migration model with the star topology. The algorithms were compared on...
P2P-based optimization has recently gained interest among distributed function optimization scientists. Several well-known optimization heuristics have been recently re-designed to exploit the peculiarity of such a distributed environment. The final goal is to perform high quality function optimization by means of inexpensive, fully decentralized machines, which may either be purposely organized in...
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