Adaptive Gravitational Search Algorithm Research Articles

Blind CFO estimation based on weighted subspace fitting criterion with fuzzy adaptive gravitational search algorithm

AbstractThis paper deals with the blind carrier frequency offset (CFO) estimation based on weighted subspace fitting (WSF) criterion with fuzzy adaptive gravitational search algorithm (GSA) for the interleaved orthogonal frequency-division multiplexing access (OFDMA) uplink system. For the CFO estimation problem, it is well known that the WSF has superior statistical characteristics and better estimation performance. However, the type of CFO estimation must pass through the high-dimensional space problem. Optimizing complex nonlinear multimodal functions requires a large computational load, which is difficult and not easy to maximize or minimize nonlinear cost functions in large parameter spaces. This paper firstly presents swarm intelligence (SI) optimization algorithms such as GSA, particle swarm optimization (PSO), and hybrid PSO and GSA (PSOGSA) to improve estimation accuracy and reduce the computational load of search. At the same time, this paper also integrates a fuzzy inference system to WSF-GSA to dynamically adjust the gravitational constant, which can not only reduce the searching computational load, but also improve the performance of GSA in the global optimization and solution accuracy. Finally, several simulation results are provided for illustrating the effectiveness of the proposed estimator.

Effective Workflow Scheduling in Cloud Platform Using Data Aware based Adaptive Gravitational Search Algorithm

Effective Workflow Scheduling in Cloud Platform Using Data Aware based Adaptive Gravitational Search Algorithm

Assembly sequence planning based on adaptive gravitational search algorithm

Assembly sequence planning based on adaptive gravitational search algorithm

An adaptive gravitational search algorithm for multilevel image thresholding

An adaptive gravitational search algorithm for multilevel image thresholding

Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything

The smart applications dominating the planet in the present day and age, have innovatively progressed to deploy Internet of Things (IoT) based systems and related infrastructures in all spectrums of life. Since, variety of applications are being developed using this IoT paradigm, there is an immense necessity for storing data, processing them to get meaningful information and render suitable services to the end-users. The “thing” in this decade is not only a smart sensor or a device; it can be any physical or household object, a smart device or a mobile. With the ever increasing rise in population and smart device usage in every sphere of life, when all of such “thing”s generates data, there is a chance of huge data traffic in the internet. This could be handled only by integrating “Internet of Everything (IoE)” paradigm with a completely diversified technology — Cloud Computing. In order to handle this heavy flow of data traffic and process the same to generate meaningful information, various services in the global environment are utilized. Hence the primary focus revolves in integrating these two diversified paradigm shifts to develop intelligent information processing systems. Energy Efficient Cloud Based Internet of Everything (EECloudIoE) architecture is proposed in this study, which acts as an initial step in integrating these two wide areas thereby providing valuable services to the end users. The utilization of energy is optimized by clustering the various IoT network using Wind Driven Optimization Algorithm. Next, an optimized Cluster Head (CH) is chosen for each cluster, using Firefly Algorithm resulting in reduced data traffic in comparison to other non-clustering schemes. The proposed clustering of IoE is further compared with the widely used state of the art techniques like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA) and Adaptive Gravitational Search algorithm (AGSA). The results justify the superiority of the proposed methodology outperforming the existing approaches with an increased life-time and reduction in traffic.

A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation

Two-dimensional (2D) multilevel thresholding is an important technique for noisy image segmentation which has drawn much attention during the past few years. The conventional image segmentation methods are efficient for 2D bi-level thresholding. However, the computational complexity grows exponentially when extended to 2D multilevel thresholding since they search the optimal thresholds by exhaustive strategy. To tackle this problem, a fuzzy adaptive gravitational search algorithm (FAGSA) using Tsallis entropy as its objective function has been presented to find the optimal 2D multilevel thresholds in this paper. In the FAGSA, fuzzy logic controllers are designed to tune the control parameters. The state-of-the-art heuristic algorithms are compared with this proposed algorithm. Both test images and noisy images are utilized in the experiments to evaluate the performance of the involved algorithms. The experimental results significantly demonstrate the superiority of our algorithm in terms of the objective function value, image quality measures and time consumption.

A metaheuristic optimization approach for energy efficiency in the IoT networks

SummaryRecently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state‐of‐the‐art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

An adaptive gravitational search algorithm for global optimization

<span>Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.</span>

Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things

With ample technological developments and flourishing demand for digital assistance in daily life and work environments that goes along with it, technologies are required to derive this application domain to the subsequent level. Internet of Things (IoT) is considered as one of vision for such technologies. This paper intends to develop self adaptive whale optimization algorithm (SAWOA) for the accomplishment of energy-aware cluster head selection and clustering protocols under wireless sensor network (WSN)—based IoT. Along with the parameters like energy, distance, and delay of sensor nodes in WSN, this simulation considers both load and temperature of IloT devices. After modeling the simulation, it carries out a valuable performance analysis in terms of network efficiency, normalized energy and load and temperature of the selected cluster head. The performance analysis compares the effectiveness of proposed SAWOA over traditional artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), adaptive GSA (AGSA) and WOA-based cluster head selection models. The outcome from simulation model proves the successful performance of SAWOA in cluster head selection so that the lifetime of network is prolonged.

Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy

An adaptive gravitational search algorithm (GSA) that switches between synchronous and asynchronous update is presented in this work. The proposed adaptive switching synchronous–asynchronous GSA (ASw-GSA) improves GSA through manipulation of its iteration strategy. The iteration strategy is switched from synchronous to asynchronous update and vice versa. The switching is conducted so that the population is adaptively switched between convergence and divergence. Synchronous update allows convergence, while switching to asynchronous update causes disruption to the population’s convergence. The ASw-GSA agents switch their iteration strategy when the best found solution is not improved after a period of time. The period is based on a switching threshold. The threshold determines how soon is the switching, and also the frequency of switching in ASw-GSA. ASw-GSA has been comprehensively evaluated based on CEC2014’s benchmark functions. The effect of the switching threshold has been studied and it is found that, in comparison with multiple and early switches, one-time switching towards the end of the search is better and substantially enhances the performance of ASw-GSA. The proposed ASw-GSA is also compared to original GSA, particle swarm optimization (PSO), genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). The statistical analysis results show that ASw-GSA performs significantly better than GA and BA and as well as PSO, the original GSA and GWO.12

An Adaptive Centric Gravitational Search Algorithm for Complex Function Optimization

An Adaptive Centric Gravitational Search Algorithm for Complex Function Optimization

Optimal setting of FACTS devices for voltage stability improvement using PSO adaptive GSA hybrid algorithm

This paper presents a novel technique for optimizing the FACTS devices, so as to maintain the voltage stability in the power transmission systems. Here, the particle swarm optimization algorithm (PSO) and the adaptive gravitational search algorithm (GSA) technique are proposed for improving the voltage stability of the power transmission systems. In the proposed approach, the PSO algorithm is used for optimizing the gravitational constant and to improve the searching performance of the GSA. Using the proposed technique, the optimal settings of the FACTS devices are determined. The proposed algorithm is an effective method for finding out the optimal location and the sizing of the FACTS controllers. The optimal locations and the power ratings of the FACTS devices are determined based on the voltage collapse rating as well as the power loss of the system. Here, two FACTS devices are used to evaluate the performance of the proposed algorithm, namely, the unified power flow controller (UPFC) and the interline power flow controller (IPFC). The Newton–Raphson load flow study is used for analyzing the power flow in the transmission system. From the power flow analysis, bus voltages, active power, reactive power, and power loss of the transmission systems are determined. Then, the voltage stability is enhanced while satisfying a given set of operating and physical constraints. The proposed technique is implemented in the MATLAB platform and consequently, its performance is evaluated and compared with the existing GA based GSA hybrid technique. The performance of the proposed technique is tested with the benchmark system of IEEE 30 bus using two FACTS devices such as, the UPFC and the IPFC.

Adaptive GSA-based optimal tuning of PI controlled servo systems with reduced process parametric sensitivity, robust stability and controller robustness.

This paper suggests a new generation of optimal PI controllers for a class of servo systems characterized by saturation and dead zone static nonlinearities and second-order models with an integral component. The objective functions are expressed as the integral of time multiplied by absolute error plus the weighted sum of the integrals of output sensitivity functions of the state sensitivity models with respect to two process parametric variations. The PI controller tuning conditions applied to a simplified linear process model involve a single design parameter specific to the extended symmetrical optimum (ESO) method which offers the desired tradeoff to several control system performance indices. An original back-calculation and tracking anti-windup scheme is proposed in order to prevent the integrator wind-up and to compensate for the dead zone nonlinearity of the process. The minimization of the objective functions is carried out in the framework of optimization problems with inequality constraints which guarantee the robust stability with respect to the process parametric variations and the controller robustness. An adaptive gravitational search algorithm (GSA) solves the optimization problems focused on the optimal tuning of the design parameter specific to the ESO method and of the anti-windup tracking gain. A tuning method for PI controllers is proposed as an efficient approach to the design of resilient control systems. The tuning method and the PI controllers are experimentally validated by the adaptive GSA-based tuning of PI controllers for the angular position control of a laboratory servo system.

Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm

This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retaining walls. The proposed algorithm combines an adaptive gravitational search algorithm (AGSA) with pattern search (PS) called AGSA–PS. In the resulting hybrid approach, the PS algorithm is employed as a local search algorithm around the global solution found by AGSA. The proposed algorithm was tested on a set of five well-known benchmark functions and simulation results demonstrate the superiority of the new method compared with the standard algorithm. Thereafter, the proposed AGSA–PS is applied for multi-objective optimisation of RC retaining walls. Two objective functions include total cost and embedded CO2 emissions of retaining wall are considered. The reliability and efficiency of the AGSA–PS for multi-objective optimisation of retaining structures are investigated by considering two design examples of retaining walls. Experimental results demonstrate that the resulting algorithm has high viability, accuracy and significantly outperforms the original algorithm and some other methods in the literature.

Fuzzy logic‐based adaptive gravitational search algorithm for optimal tuning of fuzzy‐controlled servo systems

This study proposes an adaptive gravitational search algorithm (AGSA) which carries out adaptation of depreciation law of the gravitational constant and of a parameter in the weighted sum of all forces exerted from the other agents to the iteration index. The adaptation is ensured by a simple single input–two output (SITO) fuzzy block in the algorithm's structure. SITO fuzzy block operates in the iteration domain, the iteration index is the input variable and the gravitational constant and the parameter in the weighted sum of all forces are the output variables. AGSA's convergence is guaranteed by a theorem derived from Popov's hyperstability analysis results. AGSA is embedded in an original design and tuning method for Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs) dedicated to servo systems modelled by second-order models with an integral component and variable parameters. AGSA solves a minimisation-type optimisation problem based on an objective function which depends on the sensitivity function with respect to process gain variations, therefore a reduced process gain sensitivity is offered. AGSA is validated by a case study that optimally tunes a T-S PI-FC for position control of a laboratory servo system.Representative experimental results are presented.

Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market

A novel stochastic optimization approach to solve optimal bidding strategy problem in a pool based electricity market using fuzzy adaptive gravitational search algorithm (FAGSA) is presented. Generating companies (suppliers) participate in the bidding process in order to maximize their profits in an electricity market. Each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. The gravitational search algorithm (GSA) is tedious to solve the optimal bidding strategy problem because, the optimum selection of gravitational constant (G). To overcome this problem, FAGSA is applied for the first time to tune the gravitational constant using fuzzy “IF/THEN” rules. The fuzzy rule-based systems are natural candidates to design gravitational constant, because they provide a way to develop decision mechanism based on specific nature of search regions, transitions between their boundaries and completely dependent on the problem. The proposed method is tested on IEEE 30-bus system and 75-bus Indian practical system and compared with GSA, particle swarm optimization (PSO) and genetic algorithm (GA). The results show that, fuzzification of the gravitational constant, improve search behavior, solution quality and reduced computational time compared against standard constant parameter algorithms.

Combined heat, power and hydrogen production optimal planning of fuel cell power plants in distribution networks

Recently, due to technology improvements and governmental incentives for using the green energies, Fuel Cell Power Plants (FCPPs) seem to be a promising approach for electricity generation. FCPPs, as Distributed Generation (DG) units, can be considered as Combined sources of Heat, Power, and Hydrogen (CHPH). CHPH operation of FCPPs improves system efficiency because of produced hydrogen which can be stored for future use of FCPPs or can be sold for profit. Using 2m+1 Point Estimate Method (2m+1 PEM), a probabilistic load flow approach is employed to model the uncertainties in electrical and thermal load demands, pressure of input oxygen and hydrogen importing to FCPPs, and temperature of FCPPs. Minimizing the operation costs of electrical energy generated by distribution substation and FCPPs, minimizing the voltage deviation, and minimizing total emissions produced by distribution substations and FCPPs are selected as objective functions. This paper just considers the placement of CHPH FCPPs without assuming the devices investment cost. A powerful optimization technique, θ-Self Adaptive Gravitational Search Algorithm (θ-SAGSA), is proposed to achieve the optimal places for FCPPs and daily optimal active powers of distribution substation and FCPPs. For solving the proposed multi-objective problem, this paper utilizes the Pareto Optimality idea to obtain a set of solutions in the multi-objective problem instead of a sole solution. The proposed method effectiveness is validated on a 69-bus distribution system.

Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems

This paper presents a novel adaptive Gravitational Search Algorithm (GSA) for the optimal tuning of fuzzy controlled servo systems characterized by second-order models with an integral component and variable parameters. The objective functions consist of the output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The proposed adaptive GSA solves the optimization problems resulting in a new generation of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs) with a reduced time constant sensitivity. A design method for T-S PI-FCs is then proposed and experimentally validated in the representative case study of the optimal tuning of T-S PI-FCs for the position control system of a servo system.