Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on Biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. the original BBO sometimes has not resulted in desirable outcomes. Migration, mutation and elitism are three Principal operators in BBO. The migration operator plays an important role in sharing information among candidate habitats. This paper proposes a novel migration operator in Original BBO. The proposed BBO is named as PBBO and new migration operator is examined over 12 test problems. Also, results are compared with original BBO and others Meta-heuristic algorithms. Results show that PBBO outperforms over basic BBO and other considered variants of BBO.

Biogeography based optimization (BBO) is a new stochastic force based on the science of Biogeography.Biogeography is the schoolwork of geographical allotment of biological organisms. BBO utilizes migration operator to share information between the problem solutions. The problem solutions are known as habitats and sharing of features is called migration. In this paper, BBO algorithm is developed to optimize the current excitations of concentric circular antenna arrays (CCAA). Concentric Circular Antenna Array (CCAA) has numerous attractive features that make it essential in mobile and communication applications. The goal of the optimization is to reduce the side lobe levels and the primary lobe beam width as much as possible. To confirm the capabilities of BBO, three different CCAA antennas of different sizes are taken. The results obtained by BBO are compared with the Real coded Genetic Algorithm (RGA), Craziness based Particle Swarm Optimization (CRPSO) and Hybrid Evolutionary Programming (HEP).

This paper objective presents static synchronous series compensation FACTS device with Biogeography based optimization (BBO) to deal for obtaining worthwhile power flow control. The Biogeography based optimization method is utilized to find the optimal fitted child sets by surviving parents with the help of migration and mutation Jaipur process. The present BBO technique from the evolutionary strategy with static synchronous series compensator provides improved outcomes in comparison to other optimization methods. The simulation outcomes illustrate that the proposed BBO algorithm is efficacious, secure and correct to search the optimized values with SSSC based FACTS devices. The proposed method is considering the solution quality an optimistic substitute method for extricating the OPF problems. The simplification and effectiveness of this method are validated on the IEEE 57 bus and 75 bus Systems. In this paper, from the outcome results, it is clearly shown that the proposed technique execution can be properly and effectively applied to the optimal position of multiple OPF problems. i.e. BBO based algorithm with SSSC FACTS device is found better results when compared to without SSSC device in all aspects.

The Biogeography of genusLinaria was revealed by the available chromosome counts from all over the world.Chromosome numbers of 92 taxa of the genusLinaria are included in an on-line karyological database.Furthermore, information about chromosome numbers taken from 374 literature sources was used in this paper.Each database record includes name of taxon, data on chromosome and data on the origin of the material and species-area distribution. Each database also provides a reported chromosome number and place of publication.More than half of the available species in chromosome data are diploid or consist of both diploid and polyploid populations and a few numbers of the species are polyploid. For the first time, in this study, mitosis and meiosis chromosome numbers of 13 populations belonging to three species, namelyL. lineolata Boiss (2n=2x=12), L.fastigiataChav. (2n=2x=12) and L. dalmatica (L.) Mill. (2n=2x=12), were determined by examining anthers.In addition, pollen stainability has been documented inLinaria populations in Iran.