Swarm intelligence metaheuristics have been successfully used for hard optimization problems. After the initial introduction phase such algorithms are further improved by modifications and hybridizations. Parallelization is usually introduced for performance improvement and better resources utilization. In this paper we present an improved parallelized artificial bee colony (ABC) algorithm with multiple swarm inter-communication and learning that not only significantly improves computational time, but also improves the results. Proposed algorithm was tested on large set of standard benchmark functions and it outperformed the state-of-art ABC algorithm.

Usage of handheld devices such as tablets and smartphones as the second screen devices in television is becoming more popular. The main reason for that is great improvement and development of those handheld devices as well as television popularity. In this paper, the system architecture and framework of the second screen features are presented. The paper focuses on implementation of mosaic content list as well as other digital television services such as Electronic Program Guide, Teletext, Personal Video Recording control or live TV on the second screen.

Since their introduction, bio-inspired algorithms, especially the ones based on the social behaviour of the animals that live in colonies have demonstrated great potential in finding near-optimal solutions for both unconstrained and constrained hard optimization problems. In this research, a parallel version of the popular Artificial Bee Colony (ABC) algorithm for optimization of constrained problems, has been introduced. An island-based model, in which the whole population is divided into subpopulations, is used. Subpopulations execute the serial version of the original algorithm and occasionally exchange the obtained results. The proposed algorithm has been tested based on a set of well-known constraint benchmark functions and five real-world engineering design problems. The results demonstrate clear improvements compared with those obtained with the original ABC algorithm.

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