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This paper incorporate the multilevel selection (MLS) theory into the genetic algorithm. Based on this theory, a Multilevel Cooperative Genetic Algorithm (MLGA) is presented. In MLGA, a species is subdivided in a set of populations, each population is subdivided in groups, and evolution occurs at two levels so called individual and group level. A fast population dynamics occurs at individual level...
The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, is presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by...
In this paper we present a combinatorial optimization method based on particle swarm optimization and stochastic local search concept. Under this method, in order to balance between exploration and exploitation, at each iteration step a local exploration performed around particles. The stochastic local search encourages the particle to explore local region beyond that defined by the search algorithm...
The comparison of biological sequences is one of the oldest problems in computational biology. Global alignment is designed to search for highly similar regions in two DNA sequences, where appear in the same order and orientation. Longest Common Subsequence (LCS) is the most typical algorithm for global alignment that has optimal solution and independent to the shape of its input sequences. Since...
Ad hoc networks are characterized by multi-hop wireless connectivity and frequently changing network topology which have made them infrastructureless. These networks need efficient routing protocols, so various ad hoc routing protocols have been proposed and compared based on some metrics. In this paper we present performance comparisons of the DSDV, AODV, DSR and TORA routing protocols with respect...
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