Titlebook: Cuckoo Search and Firefly Algorithm; Theory and Applicati Xin-She Yang Book 2014 Springer International Publishing Switzerland 2014 Computa

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Hybridization of Cuckoo Search and Firefly Algorithms for Selecting the Optimal Solution in Semantitimal or near-optimal solution in semantic Web service composition. Cuckoo Search and Firefly Algorithm are hybridized with genetic, reinforcement learning and tabu principles to achieve a proper exploration and exploitation of the search process. The hybrid algorithms are applied on an enhanced pla

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Geometric Firefly Algorithms on Graphical Processing Units,ant since search spaces are not always parametric. Of particular interest are combinatorial spaces such as those of programs that are searchable by parametric optimisers, providing they have been specially adapted in this way. This typically involves redefining concepts of distance, crossover and mu

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A Parallelised Firefly Algorithm for Structural Size and Shape Optimisation with Multimodal Constra due to shape modifications. This may impose severe restrictions to gradient-based optimisation methods. Here, in this chapter, it is investigated the use of the Firefly Algorithm (FA) as an optimization engine of such problems. It is suggested some new implementations in the basic algorithm, such a

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aggrieve

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1860-949X d applications with detailed algorithm analysis, implementat.Nature-inspired algorithms such as cuckoo search and firefly algorithm have become popular and widely used in recent years in many applications. These algorithms are flexible, efficient and easy to implement. New progress has been made in

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Beatriz Muñoz-Seca,Josep Riverolaied-up capital. As in most real-world problems, the two optimization objectives are conflicting and improving performance on one of them deteriorates performance of the other. To handle the conflicting objectives, the original Cuckoo Search algorithm is extended based on the concepts of multi-objective Pareto-optimization.

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