## What is/are Meta Heuristic Algorithm?

Meta Heuristic Algorithm - As the developed model was NP-hard, several meta-heuristic algorithms are used and two heuristic algorithms, namely, Improved Ant Colony Optimization (IACO) and Improved Harmony Search (IHS) algorithms are developed to solve the MSCN model in different problems.^{[1]}Then, both original data collected from a 5 kW solid oxide fuel cell stack and processed data are transferred to effectively guide eight prominent meta-heuristic algorithms for effective parameter extraction.

^{[2]}In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters.

^{[3]}Meta-heuristic algorithms are applied to find good or near-optimal solutions at a reasonable computational cost and time by exploring the search space in an efficient way.

^{[4]}The moth–flame optimization (MFO), a swarm-based meta-heuristic algorithm, was integrated to ANFIS, leading to a MFO–ANFIS model, to improve its accuracy.

^{[5]}In this work, we propose a modified gray wolf optimizer to tackle some of the challenges in meta-heuristic algorithms.

^{[6]}In this paper, the authors describe how these challenges can be resolved by designing an efficient WSN with the help of meta-heuristic algorithms.

^{[7]}This article presents an efficient hybrid meta-heuristic algorithm for topology, layout and sizing optimization of truss structures.

^{[8]}The NSGA II and MOPSO meta-heuristic algorithms both have been applied to solve the problem.

^{[9]}Because of the NP-hard nature of the problem some meta-heuristic algorithms are proposed to solve it efficiently.

^{[10]}We solve the resulting CPs using two algorithms, namely, commercial branch and bound solver and the state-of-the-art meta-heuristic algorithm to solve MDMKP.

^{[11]}This study contributes to the ongoing research in detecting and mitigating slow HTTP DDoS attacks with emphasis on the use of machine learning classification and meta-heuristic algorithms.

^{[12]}Recent studies show that the bio-inspired meta-heuristic algorithms for solving engineering problems such as energy reduction in autonomous networks in the multidisciplinary areas of WSN, Internet of Things (IoT) and Machine learning models.

^{[13]}The focus of this study aims at developing two novel hybrid intelligence models for forecasting copper prices in the future with high accuracy based on the extreme learning machine (ELM) and two meta-heuristic algorithms (i.

^{[14]}The performance of the LWMOGWO algorithm is evaluated through groups of experiments by comparing with multiple hybrid meta-heuristic algorithms, hybrid particle swarm optimization (NGPSO) algorithm, an improved grey wolf optimization (IGWO) algorithm, MOGWO, NSGA-II (Non-dominated sorting genetic algorithm), and PLMEAPS.

^{[15]}The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence.

^{[16]}To resolve this problem, a number of meta-heuristic algorithms have been developed.

^{[17]}The task offloading framework uses a meta-heuristic algorithm namely Gray Wolf Optimization (GWO) to schedule the task by optimizing the system parameters and this enables to make optimal decision on task offloading.

^{[18]}Besides, noised data, which is inevitable in various operation conditions, usually hinders meta-heuristic algorithms (MhAs) to obtain high-quality PEMFC parameters.

^{[19]}For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm.

^{[20]}Different traditional and meta-heuristic algorithms (MH) are employed to solve the problem and the obtained results are compared for the positional error of end-effector.

^{[21]}Recently, a novel meta-heuristic algorithm known as the marine predators algorithm (MPA) has been proposed for solving continuous optimization problems.

^{[22]}Symbiotic organisms search (SOS) algorithm is a nature-inspired meta-heuristic algorithm, which has been successfully applied to solve a large number of problems of different areas.

^{[23]}Consequently, the findings of this work justify quite competitive performance compared to other meta-heuristic algorithms.

^{[24]}A Powell’s method-based meta-heuristic algorithm handled task 2), while the Hyper-Process Model (HPM) technique was selected in task 3).

^{[25]}A structural architecture is designed to solve the large-scale real-life instances, and several meta-heuristic algorithms are embedded in the architecture to enhance the ability of finding good solutions.

^{[26]}To solve the problem, three meta-heuristic algorithms are designed including the original and augmented Tabu Search (TS) algorithms and Differential Evolution (DE) algorithm.

^{[27]}In this study, the Koyna concrete gravity dam section in India and the Kalat Zavin concrete gravity dam in Khorasan Razavi province were optimized using the election meta-heuristic algorithm (EA) concerning the stability conditions.

^{[28]}This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting.

^{[29]}Artificial Bee Colony (ABC) algorithm is a meta-heuristic algorithm, is inspired by the bee’s food search behaviour based on swarm intelligence.

^{[30]}To address this issue, the present study develops a MOO model using machine learning (ML) techniques and a new meta-heuristic algorithm.

^{[31]}In order to solve the formulated mathematical model, we used epsilon-constraint method and the best-worst method as a multi-criteria decision-making method for small-scale cases, and meta-heuristic algorithms (NSGA-II, PESA, and SPEA) for large-scale problems.

^{[32]}Besides, due to the growing concern of the developing efficient optimization methods and algorithms in line with the increasing needs of microgrids, the focus of this study is on using the whale meta-heuristic algorithm for operation management of microgrids.

^{[33]}IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes.

^{[34]}Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem.

^{[35]}In structural methods, the problem is converted into a search problem and meta-heuristic algorithms are used to solve it.

^{[36]}In considering that, university course timetabling problem is in vast NP-Hard dimensions, a meta-heuristic algorithm is employed, based on a non-dominated sorting genetic algorithm.

^{[37]}Three new heuristics (H-1), (H-2), and (H-3) have been proposed and to validate the model, two new meta-heuristic algorithms, namely, an Improved Social Engineering Optimization (ISEO) and Hybrid Firefly and Simulated Annealing Algorithm (HFFA-SA) have been developed.

^{[38]}Mainly, this review focuses on the various meta-heuristic algorithms to be suited for solving the WSN localization problem.

^{[39]}In present time, meta-heuristic algorithms have been widely adopted for solving diverse classes of optimization problems.

^{[40]}Hunger games search (HGS) is a recently proposed meta-heuristic algorithm based on hunger-driven activities and animal behavior choices, and it has been proven to possess global exploration ability and can solve both constrained and unconstrained problems effectively.

^{[41]}These problems were also solved in large-scale problems with NSGA-II and SFLA meta-heuristic algorithms using MATLAB software in single-objective and multi-objective mode due to the NP-Hard nature of this group of large and real dimensional problems.

^{[42]}Two meta-heuristic algorithms (cuckoo search and differential evolution) were implemented and verified on a real example of a music festival scenario.

^{[43]}Equilibrium optimizer(EO) is a physics-based meta-heuristic algorithm, which is inspired from a well-mixed dynamic mass balanceon a control volume that has good exploration and exploitation capabilities.

^{[44]}These days, a range of meta-heuristic algorithms have been utilized for the beamforming of antenna arrays.

^{[45]}Most of them are meta-heuristic algorithms.

^{[46]}The meta-heuristic algorithm inspired by natural may reduce the optimization performance due to excessive imitation.

^{[47]}In addition, the results indicate the superiority of ISHO algorithm to three other meta-heuristic algorithms including particle swarm optimization, firefly algorithm, and bat algorithm.

^{[48]}Simultaneous Heat Transfer Search (SHTS) is a novel meta-heuristic algorithm proposed recently and it can solve some optimization problems.

^{[49]}The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios.

^{[50]}

## particle swarm optimization

The performance of the LWMOGWO algorithm is evaluated through groups of experiments by comparing with multiple hybrid meta-heuristic algorithms, hybrid particle swarm optimization (NGPSO) algorithm, an improved grey wolf optimization (IGWO) algorithm, MOGWO, NSGA-II (Non-dominated sorting genetic algorithm), and PLMEAPS.^{[1]}The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence.

^{[2]}In addition, the results indicate the superiority of ISHO algorithm to three other meta-heuristic algorithms including particle swarm optimization, firefly algorithm, and bat algorithm.

^{[3]}Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO).

^{[4]}To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS).

^{[5]}Among numerous meta-heuristic algorithms, Differential evolution (DE) and Particle Swarm Optimization (PSO) are found to be an efficient and powerful optimization algorithm.

^{[6]}In this article, a design optimization problem is proposed for weight minimization of steel pipe rack structures, and then the problem is solved through three meta-heuristic algorithms consisting of a modified particle swarm optimization (PSO), grey wolf optimizer (GWO), and the recently developed improved grey wolf optimizer (IGWO).

^{[7]}The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

^{[8]}This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements.

^{[9]}The contribution in this scientific paper is to define the (FOPID) parameters according to the closed loop responses of the system, and these parameters were adjusted using new meta-heuristic algorithms including the Invasive Weed Optimization (IWO), the PSO Particle Swarm optimization, the Genetic Algorithm (GA), The bat optimization algorithm (BA) and (ACO).

^{[10]}The load amid SG user’s tasks and service providers is balanced by implementing four different meta-heuristic algorithms like particle swarm optimization, ant colony optimization (ACO),artificial bee colony (ABC), and gradient-based optimizer (GBO).

^{[11]}The optimum values of the thresholds of the ACs at different levels are optimized using two meta-heuristic algorithms, particle swarm optimization (PSO) and grey wolf optimization (GWO) for comparison.

^{[12]}Adopting meta-heuristic algorithms, in which particle swarm optimization (PSO) is the most widely used, is a popular approach.

^{[13]}Particle swarm optimization (PSO) and differential evolution (DE) are two efficient meta-heuristic algorithms, achieving excellent performance in a wide variety of optimization problems.

^{[14]}Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm.

^{[15]}One meta-heuristic algorithm is the Particle Swarm Optimization (PSO).

^{[16]}Particle swarm optimization (PSO) is a meta-heuristic algorithm which has demonstrated excellent performance in feature selection tasks.

^{[17]}Finally, the proposed GA-HH is proved as an efficient, effective, and robust algorithm to solve the MMSC model with considerations of multitask and uncertainty, by comparing it with other well-known meta-heuristic algorithms such as the genetic algorithm and particle swarm optimization.

^{[18]}To place spare links in the ASNoC topology, a meta-heuristic algorithm based on Particle Swarm Optimization (PSO) is proposed.

^{[19]}In the present study, the efficiency of a new machine learning method, named fuzzy c-means clustering- based adaptive neural-fuzzy inference system combined with a new meta-heuristic algorithm, hybrid particle swarm optimization – gravity search algorithm (ANFIS-FCM-PSOGSA), is investigated in order to model wetting front redistribution of drip irrigation systems (IS) using soil and system parameters as inputs under continuous and pulse surface/subsurface IS.

^{[20]}The VMShield extracts the system call features using Bag of n-gram approach and selects important features using the meta-heuristic algorithm, binary particle swarm optimization.

^{[21]}Nature-inspired optimization algorithms turned out to be progressively more accepted in modern era, and the majority of these meta-heuristic algorithms, such as ‘Bat algorithm’, ‘Lion Algorithm’, ‘Particle swarm optimization’, ‘Water wave optimization algorithm’, ‘Elephant herding optimization algorithm’, ‘Optics inspired optimization algorithm’, ‘Cuckoo search’, ‘Flower algorithms’, ‘Genetic algorithms’, ‘Differential evolution’, ‘Harmony search’, ‘Simulated annealing’, and many more.

^{[22]}A switching based bi-level meta-heuristic algorithm (SBMA) is developed incorporating Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to solve the bi-level LFJO problem.

^{[23]}

## sine cosine algorithm

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions.^{[1]}The sine cosine algorithm (SCA) is a recently developed meta-heuristic algorithm for solving global optimization problems.

^{[2]}A new hybrid meta-heuristic algorithm named hybrid grey wolf optimiser-sine cosine algorithm (HGWOSCA) based on the exponential decreasing function (EDF) with high accuracy and speed of optimisation in achieving to the global solution is applied to determine the optimal size of system components.

^{[3]}Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme.

^{[4]}Ant lion optimizer (ALO), moth-flame optimization (MFO), dragonfly algorithm (DA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), particle swarm optimization (PSO), and whale optimization algorithm (WOA) meta-heuristic algorithms were applied to control the flow of power between sources.

^{[5]}The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm.

^{[6]}

## whale optimization algorithm

In this paper, an Improved Whale Optimization Algorithm which is intended towards the better optimization of the solutions under the category of meta-heuristic algorithms is proposed.^{[1]}Whale optimization algorithm (WOA) is a new meta-heuristic algorithm for mathematical description of the foraging behavior of whales.

^{[2]}Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms.

^{[3]}Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy.

^{[4]}To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed.

^{[5]}

## ant colony optimization

As the developed model was NP-hard, several meta-heuristic algorithms are used and two heuristic algorithms, namely, Improved Ant Colony Optimization (IACO) and Improved Harmony Search (IHS) algorithms are developed to solve the MSCN model in different problems.^{[1]}Therefore, this research presents applying ant colony optimization which is meta-heuristic algorithms for solving complex optimization problems to find good solutions with acceptance in computation time.

^{[2]}The meta-heuristic algorithms (FPA, PSO or chaotic PSO) and ant colony optimization are used with a hierarchical collaboration schema in addition to a local search mechanism.

^{[3]}Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model.

^{[4]}

## grey wolf optimization

This paper plans to integrate the two meta-heuristic algorithms like Beetle swarm optimization (BSO) and Grey Wolf Optimization called the Alternative Grey Wolf with Beetle Swarm Optimization (AGW-BSO) for developing the Hybrid Beam Selection (HBS) scheme in MIMO-NOMA and the new HBS scheme could support the multiple SBS scheme in beamspace MIMO-NOMA systems.^{[1]}The applied meta-heuristic algorithms to the problem are Orthogonal Crossover based Differential Evolution (OXDE), Hybrid Grey Wolf Optimization, and Particle Swarm Optimization Algorithm (HGWO-PSO), Sine Cosine Algorithm (SCA), and Hybrid PSO and Genetic Algorithm (HPSO-GA).

^{[2]}In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful meta-heuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms.

^{[3]}The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms.

^{[4]}

## multi objective optimization

Currently, one of the most popular research topics is the development of a new meta-heuristic algorithm for solving multi-objective optimization problems.^{[1]}Those dramatic outcomes, in addition to our recently-proposed strategies for helping meta-heuristic algorithms in fulfilling better outcomes for the multi-objective optimization problems, motivate us to make a comprehensive study to see the performance of MPA alone and with those strategies for those optimization problems.

^{[2]}Given that the proposed model is NP-hard, a meta-heuristic algorithm to solve the multi-objective optimization problems called multi-objective black widow optimization (MOBWO) algorithm is presented.

^{[3]}Then, an Edge Server placement based on meta-Heuristic alGorithM (ESH-GM) has been proposed to achieve multi-objective optimization.

^{[4]}

## optimal feature selection

The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA).^{[1]}Here, the optimal feature selection is performed by a new variant of a meta-heuristic algorithm termed as Self Adaptive-Sea Lion Optimization (SA-SLnO) Algorithm.

^{[2]}Further, the optimal feature selection is done by the improved meta-heuristic algorithm called best and worst position updated deer hunting optimization algorithm (BWP-DHOA).

^{[3]}As the main novelty, the optimal feature selection and optimized RNN depends on an improved meta-heuristic algorithm called fitness oriented improved Jaya algorithm.

^{[4]}

## two well known

So as to solve the proposed model, two well-known meta-heuristic algorithms with particular encoding and decoding procedures, including a genetic algorithm (GA) and an imperialist competitive algorithm (ICA), are employed due to the Np-hard nature of the model.^{[1]}Due to the complexity of the model when considering large-scale samples, two well-known meta-heuristic algorithms such as Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms have been utilized.

^{[2]}In each example, the results of the MFIBO are also compared with those attained by two well-known meta-heuristic algorithms, namely the differential evolution and the teaching–learning-based optimization.

^{[3]}Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard.

^{[4]}

## colliding bodies optimization

Finally, since the proposed model is an NP-hard problem, two meta-heuristic algorithms called improved ray optimization and colliding bodies optimization are employed to solve the proposed model.^{[1]}For this reason, the seven meta-heuristic algorithms namely colliding bodies optimization (CBO), enhanced colliding bodies optimization (ECBO), water strider algorithm (WSA), dynamic water strider algorithm (DWSA), ray optimization (RO) algorithm, teaching-learning-based optimization (TLBO) algorithm and plasma generation optimization (PGO) are used to find the TMD parameters considering soil-structure interaction (SSI) effects.

^{[2]}In this paper, the recently developed meta-heuristic algorithm, so-called Enhanced Colliding Bodies Optimization (ECBO), and one new proposed version of ECBO (called NECBO), including a new mechanism for finding new solutions, and two hybrid versions of both methods mentioned above (ECBO, NECBO) are utilized for the optimal design of sloped-roof rigid frames with non-prismatic members.

^{[3]}Colliding Bodies Optimization (CBO) is a new meta-heuristic algorithm that uses collisions between objects to move to a better position so that the solution tends to be an optimal solution.

^{[4]}

## namely multi objective

For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm.^{[1]}Due to the suggested model’s complexity and nonlinearity, we linearized the mathematical model as much as possible, then two meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO) and non-dominated sorted genetic algorithm (NSGA-II), are used to solve the model.

^{[2]}To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented.

^{[3]}

## non dominated sorting

In considering that, university course timetabling problem is in vast NP-Hard dimensions, a meta-heuristic algorithm is employed, based on a non-dominated sorting genetic algorithm.^{[1]}Also, the proposed posteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is a crucial problem in multi-objective meta-heuristic algorithms.

^{[2]}Since the problem under consideration is Non-deterministic Polynomial-time hard (NP-hard), two meta-heuristic algorithms of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO) are proposed to solve the problem.

^{[3]}

## imperialist competitive algorithm

Finally, Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) as two well-known meta-heuristic algorithms are applied to solve the model with larger dimensions.^{[1]}Another aim of the present paper was to assess if ensembles of ANFIS with two meta-heuristic algorithms (imperialist competitive algorithm (ICA) and gray wolf optimization (GWO)) would yield a better prediction performance.

^{[2]}Thus, in this paper, meta-heuristic algorithms namely genetic algorithm, imperialist competitive algorithm, and a new hybrid meta-heuristic algorithm, resulted from the principal component analysis (PCA) and an imperialist competitive algorithm (ICA) called PCICA are proposed and used.

^{[3]}

## artificial neural network

This study, therefore, proposed a novel SCM based on a robust meta-heuristic algorithm, namely Hunger Games Search (HGS) and artificial neural network (ANN), abbreviated as HGS–ANN model, for predicting BIGV.^{[1]}Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers.

^{[2]}Also, the artificial neural network (ANN) has been employed based on three meta-heuristic algorithms, including genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and imperialist competitive algorithm (ICA).

^{[3]}

## called multi objective

In this paper, a new meta-heuristic algorithm, called multi-objective ant lion optimizer (MOALO) is presented to solve environmental economic dispatch (EED) problem considering transmission losses.^{[1]}The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO).

^{[2]}

## grey wolf optimizer

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems.^{[1]}This paper proposes a task optimization mechanism based on the meta-heuristic algorithm of the Grey Wolf Optimizer, called TOVEC.

^{[2]}

## Inspired Meta Heuristic Algorithm

For getting faster and accurate results Particle Swarm Optimization (PSO) algorithm is applied, which is nature inspired meta heuristic algorithm.^{[1]}The purpose behind this chapter is to provide information to the users on how to build and investigate a hybrid Feed-forward Neural Network (FNN) using nature inspired meta heuristic algorithms such as the Gravitational Search Algorithm (GSA), Binary Bat Algorithm (BBAT), and hybrid BBATGSA algorithm for the prediction of sensor network data.

^{[2]}