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 hierarchical chain-based Archimedes optimization algorithm

The Archimedes optimization algorithm (AOA) has attracted much attention for its few parameters and competitive optimization effects. However, all agents in the canonical AOA are treated in the same way, resulting in slow convergence and local optima. To solve these problems, an improved hierarchical chain-based AOA (HCAOA) is proposed in this paper. The idea of HCAOA is to deal with individuals at different levels in different ways. The optimal individual is processed by an orthogonal learning mechanism based on refraction opposition to fully learn the information on all dimensions, effectively avoiding local optima. Superior individuals are handled by an Archimedes spiral mechanism based on Levy flight, avoiding clueless random mining and improving optimization speed. For general individuals, the conventional AOA is applied to maximize its inherent exploration and exploitation abilities. Moreover, a multi-strategy boundary processing mechanism is introduced to improve population diversity. Experimental outcomes on CEC 2017 test suite show that HCAOA outperforms AOA and other advanced competitors. The competitive optimization results achieved by HCAOA on four engineering design problems also demonstrate its ability to solve practical problems

Parameters identification of pv triple-diode model using improved generalized normal distribution algorithm

To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.</p

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

Energy management and power quality improvement of microgrid system through modified water wave optimization

A comparative study of energy management strategies and PQ improvement schemes for a Fuel Cell, Battery, and SuperCapacitor integrated Microgrid system has been projected utilizing the MATLAB/Simulink architecture in this research article. For effective energy management and PQ enhancement, a Modified Water Wave Optimization algorithm based on adaptive population size and adaptive wavelength coefficient has been proposed. The MWWO method robustly and dynamically tunes the parameters of the Proportional Integral controller for effective operation. The efficiency of the proposed technique has been compared with the traditional methods regarding hydrogen consumption, load power deliberation, power quality and overall system efficiency. The results obtained and the numerical analysis confers the superiority of the suggested technique over other methods for enhanced dynamic voltage response, low fuel consumption, reduced harmonics and better efficacy thereby proving its real-time employment.Web of Science96041602

A New Enhanced Hybrid Grey Wolf Optimizer (GWO) Combined with Elephant Herding Optimization (EHO) Algorithm for Engineering Optimization

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms