A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users’ awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

This paper discusses the development of a Symbiotic Organisms Search Algorithm (SOS) variant, called Adaptive Fuzzy SOS (FSOS). Like SOS, FSOS exploits three types of symbiosis operators namely mutualism, commensalism, and parasitism in order to undertake the search process. Unlike SOS, FSOS is able to adaptively select a single or any combination of mutualism, commensalism, and parasitism update operator(s) as the search progresses based on the current search status controlled by their individual probabilities via the fuzzy decision-making. To validate its performance, we have evaluated FSOS to solve 23 benchmark functions and take a t-way test generation as our case study. Experimental results demonstrate that FSOS exhibits competitive performance against its predecessor (SOS) and other competing metaheuristic algorithms

Multi-Agent cubature Kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems

Optimization problems arise in diverse fields such as engineering, economics, and industry. Metaheuristic algorithms, including the Simulated Kalman Filter (SKF), have been developed to solve these problems. SKF, inspired by the Kalman Filter (KF) in control engineering, requires three parameters (initial error covariance P(0), measurement noise Q, and process noise R). However, studies have yet to focus on tuning these parameters. Furthermore, no significant improvement is shown by the parameter-less SKF (with randomized P(0), Q, and R). Randomly choosing values between 0 and 1 may lead to too small values. As an estimator, KF raises concerns with excessively small Q and R values, which can introduce numerical stability issues and result in unreliable outcomes. Tuning parameters for SKF is a challenging and time-consuming task. The Multi-Agent Cubature Kalman Filter (MACKO), inspired by the Cubature Kalman filter (CKF), was introduced in this work. The nature of the Cubature Kalman filter (CKF) allows the use of small values for parameters P(0), Q, and R. In the MACKO algorithm, Cubature Transformation Techniques (CTT) are employed. CTT can use small values for parameters P(0), Q, and R, so CKF was developed to overcome KF and other estimation algorithms. Moreover, in CTT, the term local neighborhoods is used to propagate the cubature point in local search, where the radius, δ, of local search is updated in every iteration to balance between the exploration and exploitation processes. MACKO is evaluated on the CEC 2014 benchmark suite with 30 optimization problems, and its performance is compared with nine existing metaheuristic algorithms. Simulation results demonstrate that MACKO is superior, outperforming the benchmark algorithms, as indicated by Friedman's test with a 5 % significance level

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.This work was supported by grants from the Spanish Ministry of Science (TIN2016-8113-R, TIN2017-89517-P and TIN2017-83132-C2- 2-R) and Universidad Politécnica de Madrid (PINV-18-XEOGHQ-19- 4QTEBP). Eneko Osaba and Javier Del Ser-would also like to thank the Basque Government for its funding support through the ELKARTEK and EMAITEK programs. Javier Del Ser-receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government