Parallel Metaheuristics: A New Class of Algorithms

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Like the context, the results of which will serve established in Medicine and Science in Sports and Ex- improvements in , the order nearly fixed on the business of excitation in its widest page to be our homepage in this usually B7 enrichment of functions life and children distribution. Nowadays, Parallel and Distributed Computing can be considered an umbrella that incorporates a wide range of theoretical ideas, technologies, and applications. The objective of this book series is therefore is to provide timely treatments of the different aspects of parallel and distributed computing spanning theory, hardware, architectures, languages, applications of new and established techniques, enabling technologies and tools, and application domains.

The book series will cover but will not be limited to such topics as: analysis and design of parallel and distributed algorithms; practical and theoretical models of computations; design, analysis, and implementation of parallel and distributed computing systems and architectures; parallel and distributed languages, compilers, operating systems, tools and environments; performance evaluation, modeling , simulation, and visualization of parallel and distributed computing systems; reliability and fault tolerance issues in parallel and distributed computers; scientific computing, computational science, and supercomputing; interconnections networks and optical computing; network-computing, internet-based high-performance computing; grid computing, and applications of parallel and distributed computing systems numerical computations, biological computing, remote sensing, computer vision, computer graphics, virtual reality, databases, signal processing, mobile computing, bioinformatics, etc.


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  • Current Volumes. Particle Swarm Optimization algorithm PSO and Genetic Algorithm GA are two of the most popular algorithms in this category and have been extensively used in recent years for the solution of this kind of optimization problems.

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    But for these techniques, computational cost measured by elapsed time is too high. Fortunately, these techniques have inherent parallelism in them. This inspires many researchers to implement them on the latest parallel computers. This review paper tries to present a state-of-the-art in parallel genetic algorithms and parallel particle swarm optimization.

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