Hybrid Shuffled Frog Leaping Research Articles

A Robust Economic Framework for Integrated Energy Systems Based on Hybrid Shuffled Frog-Leaping and Local Search Algorithm

The safe and efficient operation of the integrated energy system is severely hampered by a number of unpredictable elements, such as the output of renewable energy sources, the cost of energy purchases, and full demand response (IES). The effectiveness and excellence of the integrated energy system scheduling method can be increased with advanced modeling of unpredictable aspects. Thus, the IES robust stochastic optimisation model is constructed and solved with the hybrid shuffled frog-leaping and local search (HSFLA–LS) algorithm. Finally, a simulation analysis considering the uncertainty of energy purchase price based on the hybrid SFLA–LS algorithm is reduced by USD 1.63 (0.64%) and USD 3.34 (1.3%), compared to PSO and GA, respectively. In addition, the time taken to execute the SFLA–LS algorithm for the program is reduced by 1.886 s (1.59%), and 3.117 s (2.7%), compared to PSO and GA, respectively. The findings demonstrate that the suggested approach can lower system running expenses, and achieve the coordination and optimization of economy and robustness.

A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system

In recent years, various control methods have been proposed for maximum power point tracking (MPPT) of photovoltaic (PV) power plants. Different MPPT methods for PV systems in the literature have been evaluated in terms of energy efficiency, energy conversion, dynamic performance and reliability in different environmental conditions. Among the various MPPT methods, the Artificial Neural Network (ANN) MPPT is one of the best methods due to its ability in noise rejection and no need for prior information of physical parameters. For implementing the ANN-based MPPT two input variables including temperature and irradiance and an output variable containing voltage of MPP are taken into account. In this paper, a hybrid shuffled frog leaping and pattern search (HSFL–PS) algorithm is used for optimizing ANN-based MPPT in a grid-tied PV system. The P&O approach is used for the tracking cycle procedure and starts a precise tracking scheme after training the ANN and specification of neuron weights. MATLAB/Simulink is utilized for simulation tests to confirm the performance of the offered MPPT method. The outcomes from simulation tests validate the improved performance of the recommended MPPT in comparison with the conventional methods with a fast response of 011 sec.

Sizing renewable energy systems with energy storage systems based microgrids for cost minimization using hybrid shuffled frog-leaping and pattern search algorithm

The future of the power system is tied to the use of distributed generation (DGs) like photovoltaic (PV) and wind turbine (WT) since international societies are concerned regarding environmental pollution. However, the high penetration of distributed energy resources (DERs) in power systems causes a lot of control and operational problems that should be addressed by network operators. Therefore, this article proposes optimal management of a grid-connected microgrid (MG) including various DERs such as PV systems and storage devices. The use of the PV system in this MG is based on proposing new mathematical modelling. In the proposed model, the influence of radiation on different days and seasons on optimal operation is considered. Also, to overcome the random behaviour of the PV system, WT and loads, the uncertainties of the problem are modelled using the scenario method. The optimization problem is solved with the hybrid shuffled frog leaping and pattern search (HSFLA-PS) algorithm. The proposed optimal management is simulated by MATLAB/Simulink considering a practical PV model in a real environmental condition. This used algorithm has some advantages such as fast computational time and accurate results. The results show that the suggested day-ahead scheduling using the HSFLA-PS has an edge over other conventional methods.

Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks

SummaryWireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes.

A hybrid shuffled frog leaping algorithm and intelligent water drops optimization for efficiency maximization in smart microgrids considering EV energy storage state of health

A hybrid shuffled frog leaping algorithm and intelligent water drops optimization for efficiency maximization in smart microgrids considering EV energy storage state of health

Renewable photovoltaic-thermal combined heat and power allocation optimization in radial and meshed integrated heat and electricity distribution networks with storages based on newly developed hybrid shuffled frog leaping algorithm

Benefiting from favorable operational efficiencies, photovoltaic-thermal (PVT) technology is considered as a viable alternative for combined heat and power (CHP) distributed generation; however, the realization of a full economic profit requires allocation optimization in terms of placement and size in integrated heat and electricity distribution networks. Also, the inability for dispatching renewable PVT requires considerations of heat and electricity storages. Further, the concerns with uncertainties in heat and electricity loads and uncertainties associated with solar irradiation for PVT-CHP must be addressed. The goal of this study is to develop a simulation model for allocation optimization of PVT-CHP as well as storages in radial and meshed integrated heat and electricity distribution networks considering growth in loads and prices, in addition to uncertainties, where the distribution company's (DisCo's) yearly economic profit from the employment of renewable PVT-CHP units is maximized over a long-term operation horizon. To enhance the utilization of PVT-CHP, electric heat pumps (EHPs) are used as links for the integration of heat and electricity distribution networks. For optimization, a newly developed hybrid shuffled frog leaping algorithm is proposed and validated in terms of accuracy and speed. The simulation results show that the DisCo's yearly economic profit is highly dependent on the allocation of PVT-CHP units, storages, EHPs, and the type of distribution network.

A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows

This paper proposes a novel hybrid shuffled frog leaping algorithm (HSFLA) for vehicle routing problem with time windows (VRPTW). The diversity control strategy is developed to construct the memeplexes of the HSFLA and avoid ending the search prematurely. The modified clone selection procedure is presented to improve the quality of the solutions and bring more diversity to the population. Improved and extended extremal optimization (EO) with alternative move operators is also introduced to the exploitation of the algorithm. Furthermore, the adaptive soft time windows penalty measure is proposed to allow the existence of infeasible solutions in the evolution process. Our approach is estimated and compared with other state-of-the-art heuristics using Solomon and Cordeau VRPTW test sets. The experimental results show that the presented algorithm is very effective for handling VRPTW.

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic to handle different large-scale optimization problems. SFLA is a population-based algorithm that combines the advantages of memetic algorithm and particle swarm optimization. This paper compares previous researches on SFLA and its effectiveness, with the most applied optimization algorithms reviewed and analyzed. Based on the literature, many efforts by previous researchers on SFLA denote the next generations of basic SFLA with diverse structures for modified SFLA or hybrid SFLA. As well, an attempt is made to highlight these structures, their enhancements and advantages. Moreover, this paper considers top improvements on SFLA for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population. The measured enhancements in SFLA are based on the statistical results obtained from 89 published papers and by considering the most common and effective modifications done by a large number of researchers. Finally, the quantitative validations address the SFLA as a robust algorithm employed in various applications which outperforms the other optimization algorithms.

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.

Hybrid shuffled frog leaping optimisation algorithm for multi-objective optimal design of laminate composites

In this paper, a hybrid shuffled frog-leaping algorithm (SFLA) is presented for solving multi-objective optimal design of laminate composite structures. A customized neighborhood search algorithm, an adaptive search factor and a crossover operator are suitably incorporated in the proposed algorithm to improve the convergence characteristics apart from features like Pareto dominance, density estimation, and an external archive to store the non-dominated solutions to handle multiple objectives. The performance of the proposed algorithm is demonstrated by solving laminate composite plate, shell and stiffened shell (isogrid) problems. Further, superiority of the proposed algorithm is demonstrated by comparing with four popular meta-heuristic algorithms.

A design automation system for CMOS analog integrated circuits using New Hybrid Shuffled Frog Leaping Algorithm

This paper presents a new computer-aided design (CAD) system for automated sizing of analog integrated circuits (ICs). A new evolutionary algorithm, called New Hybrid Shuffled Frog Leaping (NHSFL) Algorithm, is proposed to deal with the equality and inequality constraints in the problem and to deduce the device sizes optimizing the performance of the circuits. For this, a new frog leaping rule is proposed to improve the local exploration and performance of the standard SFLA. Also, to improve the convergence velocity of the algorithm, the mutation operator is used for generating new frog, instead of random mechanism. By linking MATLAB and HSPICE the system evaluates the circuit performance during electrical simulation, in the optimization loop in the MATLAB environment, to be selected as an appropriate circuit topology. To assess the system, complex and hard-to-design analog blocks with restricted design requirements are presented in 0.18μm CMOS process technology. The simulation results obtained from the proposed system verify that the design requirements are closely satisfied, even in tough conditions. Furthermore, some comparisons are made with available methods like Genetic algorithm (GA) and Imperialist Competitive algorithm (ICA) showing that the presented algorithm offers excellent qualifications in terms of time, efficiency and robustness.

Design of stabilising signals for power system damping using generalised predictive control optimised by a new hybrid shuffled frog leaping algorithm

This study presents a hybrid method based on generalised predictive control (GPC) and a proposed new hybrid shuffled frog leaping (NHSFL) algorithm to design stabilising signals to damp the multi-machine power system low-frequency oscillations. A linearised model predictive controller based on GPC is designed in which the proposed NHSFL algorithm is employed for optimising the cost function of the GPC. The numerical results are presented on a two-area four-machine and a five-area 16-machine power system. The effectiveness of the designed controllers is shown by considering various operating conditions. The proposed approach, which is called as GPC-NHSFL, is compared with a classical-based method, GPC algorithm and GPC-based standard SFL algorithm (GPC-SFL). The simulation results show the superiority and capability of the proposed approach to enhance power systems damping.

Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch

The optimal reactive power dispatch (ORPD) problem is a non-linear mixed-variable optimisation problem. This study employs a new evolutionary algorithm that expands the original shuffled frog leaping algorithm (SFLA) to solve this problem. In order to fully exploit the promising solution region, a local search algorithm known as Nelder–Mead (NM) algorithm is integrated with SFLA. The resultant NM-SFLA is very efficient in solving ORPD problem. The most important benefit of the proposed method is higher speed of convergence to a better solution. The proposed method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems and compared with four versions of particle swarm optimisation algorithm, two versions of differential evolutionary algorithm and SFLA. The optimal setting of control variables including generator voltages, transformer taps and shunt VAR compensation devices for active power loss minimisation in a transmission system is determined while all the constraints are satisfied. The simulation results show the efficiency of the proposed method.