Titlebook: Nature-Inspired Computing and Optimization; Theory and Applicati Srikanta Patnaik,Xin-She Yang,Kazumi Nakamatsu Book 2017 Springer Internat

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2196-7326 on to real-world problems.Includes theoretical analysis and .The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduc

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Book 2017approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management

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Chance-Constrained Fuzzy Goal Programming with Penalty Functions for Academic Resource Planning in oals of the problem. A sensitivity analysis with variations in priority structure of goals is also performed, and . is used to identify appropriate priority structure to reach optimal decision. A demonstrative case example is considered to illustrate the approach.

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The Nature of Nature: Why Nature-Inspired Algorithms Work,bout nature, but also to an understanding of deep relationships between familiar algorithms. Here, we show that network properties underlie and define a whole family of nature-inspired algorithms. In particular, the network defined by neighbourhoods within landscapes (real or virtual) underlies the

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Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environmennima. These problems are multimodal with objective functions exhibiting multiple peaks, valleys, and hyperplanes of varying heights. Furthermore, they are nonlinear, non-smooth, non-quadratic, and can have multiple satisfactory solutions. In order to select a best solution among several possible sol

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Multi-objective Ant Colony Optimisation in Wireless Sensor Networks,eresting area of application for ACO-based algorithms is their use in wireless sensor networks (WSNs). Due to their robustness and self-organisation, ACO-based algorithms are well-suited for the distributed, autonomous and self-organising structure of WSNs. While the original ACO-based algorithm and

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