Nature-Inspired Computing

Spider Monkey Optimization (SMO) technique is most recent member in the family of swarm optimization algorithms.SMO algorithm fall in class of Nature Inspired Algorithm (NIA). SMO algorithm is good in exploration and exploitation of local search space and it is well balanced algorithm most of the times. This paper presents a new strategy to update position of solution during local leader phase using fitness of individuals. The proposed algorithm is named as Fitness based Position Update in SMO (FPSMO) algorithm as it updates position of individuals based on their fitness. The anticipated strategy enhances the rate of convergence. The planned FPSMO approach tested over nineteen benchmark functions and for one real world problem so as to establish superiority of it over basic SMO algorithm.

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research.

In this work, a modi…ed tanh􀀀coth method is used to derive travelling wave solutions for (2 + 1)- dimensional Zakharov-Kuznetsov (ZK) equation and (3 + 1)-dimensional Burgers equation. A new variable is used to solve these equations and establish new travelling wave solutions.

Keywords: tanh-coth method; travelling wave solution; (2 + 1)-dimensional Zakharov-Kuznetsov equation;
(3 + 1)-dimensional Burgers equation

— Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.

This book is about nature considered as the totality of physical existence, the universe, and our present day attempts to understand it. If we see the universe as a network of networks of computational processes at many different levels of organization, what can we learn about physics, biology, cognition, social systems, and ecology expressed through interacting networks of elementary particles, atoms, molecules, cells, (and especially neurons when it comes to understanding of cognition and intelligence), organs, organisms and their ecologies?

Regarding our computational models of natural phenomena Feynman famously wondered: “Why should it take an infinite amount of logic to figure out what one tiny piece of space/time is going to do?” Phenomena themselves occur so quickly and automatically in nature. Can we learn how to harness nature’s computational power as we harness its energy and materials?

This volume includes a selection of contributions from the Symposium on Natural Computing/Unconventional Computing and Its Philosophical Significance, organized during the AISB/IACAP World Congress 2012, held in Birmingham, UK, on July 2-6, on the occasion of the centenary of Alan Turing’s birth. In this book, leading researchers investigated questions of computing nature by exploring various facets of computation as we find it in nature: relationships between different levels of computation, cognition with learning and intelligence, mathematical background, relationships to classical Turing computation and Turing’s ideas about computing nature - unorganized machines and morphogenesis. It addresses questions of information, representation and computation, interaction as communication, concurrency and agent models; in short this book presents natural computing and unconventional computing as extension of the idea of computation as symbol manipulation.

https://www.amazon.com/Computing-Nature-Perspective-Philosophy-Epistemology-dp-3642372244/dp/3642372244/ref=mt_hardcover?_encoding=UTF8&me=&qid=

Stochastic components such as random walks have become an intrinsic part of modern metaheursitic algorithms. The efficiency of a metaheuristic algorithm may implicitly depend on the appropriate use of such randomization. In this paper, we provide some basic analysis and observations about random walks, Lévy flights, step sizes and efficiency using Markov theory. We show that the reason why Lévy flights are more efficient than Gaussian random walks, and the good performance of Eagle Strategy. Finally, we use bat algorithm to design a PID controller and have achieved equally good results as the classic Ziegler-Nichols tuning scheme.

Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization Field. Just like agents in Ant Colony Optimization are Artificial Ants, agents in Bee Colony Optimization are Artificial Bees, agents in Genetic Algorithms are Artificial chromosomes, agents in Particle Swarm Optimization are Artificial Birds or Artificial Fishes, similarly agents in Artificial Human Optimization Algorithms are Artificial Humans. “Multiple Strategy Human Optimization (MSHO)” is a new algorithm designed recently based on Artificial Humans. The key concept in MSHO is to use more than one strategy in the optimization process. Two strategies are used in MSHO. One strategy is to move towards the best individual in one generation. Another strategy is to move away from the worst individual in next generation. Differential Evolution is a popular algorithm for solving optimization problems in various domains. In this paper “Human Inspired Differential Evolution (HIDE)” is proposed. The idea of HIDE algorithm is to use the concept of Multiple Strategies of MSHO algorithm in Differential Evolution. The mutation operator of Differential Evolution algorithm is modified to incorporate the key concept of MSHO algorithm in Differential Evolution. The proposed HIDE algorithm is tested by applying it on a complex benchmark problem.

The term "Nature Plus Plus Inspired Computing" is coined by us in this article. The abbreviation for this new term is "N++IC." Just like the C++ programming language is a superset of C programming language, Nature Plus Plus Inspired Computing (N++IC) field is a superset of the Nature Inspired Computing (NIC) field. We defined and introduced "Nature Plus Plus Inspired Computing Field" in this work. Several interesting opportunities in N++IC Field are shown for Artificial Intelligence Field Scientists and Students. We show a literature review of the N++IC Field after showing the definition of Nature Inspired Computing (NIC) Field. The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch. As the focus of this article is to introduce N++IC to researchers across the globe, we added N++IC Field concepts to the Particle Swarm Optimization algorithm and created the "Children Cycle Riding Algorithm (CCR Algorithm)." Finally, results obtained by CCR Algorithm are shown, followed by Conclusions.