Hybrid Optimization Technique Research Articles

Optimization of a hybrid system for solar-wind-based water desalination by reverse osmosis: Comparison of approaches

Nineteen evolutionary algorithms, including single and hybrid optimization algorithms, are used for determining the optimum size of a hybrid renewable energy system (HRES) that is comprised of a wind turbine, a photovoltaic panel, a battery bank, and a reverse osmosis desalination unit. The main source electrical power for the reverse-osmosis desalination unit for producing fresh water is solar and wind energy, and batteries are used as back up units. Integer and continuous variables in the HRES optimization model for a remote area of Iran are considered. The optimization aims to meet the load continuously while minimizing the HRES life cycle cost subject to relevant constraints. Also, to ensure reliability, the reliability index is assessed for the loss of power supply probability. In order to achieve optimal performance, various versions well-known optimization approaches are used: particle swarm optimization, bee swarm optimization, harmony search, simulated annealing, chaotic search, and tabu search algorithm. The results show that hybrid optimization techniques provide the best performance among the considered evolutionary algorithms and that using the HRES reduces system costs and increases system reliability in general and for increasing fresh water availability.

Fractional‐order lead‐lag compensator‐based multi‐band power system stabiliser design using a hybrid dynamic GA‐PSO algorithm

Power system stabilisers (PSSs) are supplementary controllers connected to the excitation system of synchronous generators to damp electromechanical oscillations. Multi-band PSSs are reported as advanced PSSs with the ability to damp out all oscillation modes present in the power systems. This study presents the design of a robust fractional-order multi-band power system stabiliser (Fo-MBPSS) using a meta-heuristic hybrid algorithm for dynamic stability improvement of multi-machine power systems. The large bandwidth, memory effect and flat phase contribution in the frequency response of fractional-order controllers are exploited to make the Fo-MBPSS perform well against a wide range of system uncertainties. The parameter tuning problem of Fo-MBPSS is transformed to an optimisation problem that is solved using a hybrid algorithm by combining a dynamic genetic algorithm (DGA) with a standard particle swarm optimisation (PSO) algorithm. The performance of the proposed DGA-PSO-Fo-MBPSS is evaluated through eigenvalue analysis, non-linear time-domain simulations and some performance indices, in two different multi-machine systems under different loading conditions and disturbances. The results are compared with PSO-based conventional MBPSS and PSO based Fo-MBPSS (PSO-Fo-MBPSS) to establish the fractional parameter effect on the improvement of the system dynamic response and the relevance of the proposed hybrid optimisation technique in achieving robustness.

Optimal power diagrams via function approximation

In this paper, we present a novel method for generating cell complexes with anisotropy conforming to the Hessian of an arbitrary given function. This is done by variationally optimizing the discontinuous piecewise linear approximation of the given functions over power diagrams. The resulting cell complexes corresponding to the approximations are referred to as Optimal Power Diagram (OPD). A hybrid optimization technique, coupling a modified Monte Carlo method with a local search strategy, is tailored for effectively solving the specific optimization task. In contrast to the Optimal Voronoi Tessellation (OVT) method (Budninskiy et al., 2016), our OPD method does not restrict the target functions to be convex, providing more diverse classes of tessellations of the domain. Furthermore, our OPD method generally yields smaller approximation errors than the OVT method, which uses underlaid approximants. We conduct several experiments to demonstrate the efficacy of our optimization algorithm in finding good local minima and generating high-quality anisotropic polytopal meshes.

Hybrid multicriteria algorithms applied to structural design of wireless local area networks

This manuscript presents a novel approach based on hybrid optimization techniques for planning Wireless Local Area Networks in two stages: i) network structure design for access point (AP) placement and channel assignment and ii) channel assignment enhancement. We consider two objective functions: network load balance and signal-to-interference-plus-noise ratio; and three hard constraints: maximum AP capacities, client demand attendance, and minimum coverage levels. The proposed algorithm delivers an approximation of the efficient solution set, considering the two functions described above. The results from two scenarios were compared to the following four approaches: two multiobjective evolutionary algorithms, a well-known commercial tool, and a greedy technique. Finally, the solutions were subjected to sensitivity analysis to validate their robustness regarding user mobility and AP failures.

HYBRID OPTIMIZATION OF STAR GRAIN PERFORMANCE PREDICTION TOOL

ABSTRACTIn solid propellant rocket propulsion, the design of the propellant grain is a decisiveaspect. The grain design governs the entire motor performance and, hence, thewhole rocket mission. The ability to decide, during design phase, the proper graindesign that satisfies the predefined rocket mission with minimum losses is theultimate goal of solid propulsion experts. This study enables to predict the pressuretime curve of rocket motor with star grain configuration and also to optimize theperformance prediction tool through optimization methods to maximize its predictionefficiency. A hybrid optimization technique is used. Genetic Algorithm (GA) is firstimplemented to find the global optimum followed by Simulated Annealing (SA)optimization method to find the accurate local optimum. A program for predicting thepressure time curve of the rocket motor is created on MATLAB and then linked to GA- SA optimizers as an application on a case study. The purposed approach isvalidated against satisfying data. It is found that the developed optimized program iscapable of predicting rocket motor performance (including the effect of erosiveburning) with acceptable accuracy for preliminary design purposes.

A systematic procedure to optimize Integrated Solar Combined Cycle power plants (ISCCs)

This study aims to find the optimal way to integrate a parabolic trough solar field into a gas fired combined cycle. The combined cycle studied here is composed of a gas turbine and a triple pressure bottoming Rankine cycle. A general layout is proposed and allows the integration of the solar source at all levels of temperature. A thermodynamic model is proposed to evaluate the performance of the system and then coupled to a constrained nonlinear optimization algorithm that uses a hybrid optimization technique combining Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). This gives the optimal configuration of the heat exchanger networks used to harvest the solar source and the exhaust gases of the gas turbine. The effect of the system’s main parameters on the solar integration performance was investigated such as the amount of the solar heat rate injected to the bottoming cycle, the mass flow rate of the solar source, the inlet temperature of the solar source and the outlet temperatures of the high pressure super-heater n°1 and the re-heater n°1. The approach used here is general and gives finally the real potential of the integration of a parabolic trough solar field into a combined cycle.

Profit based unit commitment using hybrid optimization technique

A hybrid optimization technique based on the integration of binary successive approach (BSA) and civilized swarm optimization (CSO) has been proposed to solve profit based unit commitment (PBUC) problem. Since, PBUC is a mixed integer problem, hence to deal with binary and continuous variables, BSA and CSO technique has been employed, respectively. The BSA is based on evolutionary search and search process is initiated with random base point of the hypercube. The each base point further generates two more corner points of the hypercube. The search moves toward the point having better objective function value, and continues until the search has reached to the last branch of BSA tree. This strategy reduces the computational burden while searching the optimal unit status. The generation schedule from the committed unit is searched by CSO technique. The CSO is an integrated technique of PSO and society civilized algorithm (SCA) technique. Since, PSO has good exploration capability and SCA technique is emerging to improve the exploitation capability of the algorithm. Three PBUC test systems have been undertaken and obtained results have been compared with published results and found satisfactory. Further, Wilcoxon signed rank test is applied to investigate statistical performance of the proposed technique.

Friction Stir Welding of Aluminum and Magnesium Alloys—Experimental Investigation and Optimization Using Hybrid Grey Relational Analysis and Principal Component Analysis

Friction stir welding of dissimilar materials is investigated experimentally in this work and optimization is performed by applying a hybrid Taguchi-Grey relational analysis-Principal component analysis to maximize the tensile strength and hardness of the weld bead. Two dissimilar metals AA6061 and AZ61 is friction stir welded and considered for the experimentation. Experimental matrix is designed using Taguchi's Design of Experiment (DOE). Optimum inputs rotational speed, axial load and transverse speed is obtained by applying the hybrid optimization technique. Statistical analysis of Multi Response Performance Index (MRPI) through Analysis of Variance (ANOVA) shows that axial load is the significant parameter that contributes by 75.67% towards MRPI, followed by transverse speed and rotational speed. Confirmation experiment with optimum condition produces a better friction stir welding joint with higher tensile strength and hardness.

Hybrid Optimization Based FOPI Controller Design with Real-Time Validation

A hybrid optimization technique has been adopted for the tuning of fractional-order PI controller parameters for satisfying a predefined set of gain and phase margin specifications. The algorithm aims at determining a set of optimal controller parameters by minimizing an objective function framed using the equations defining the margins and corresponding cross-over frequencies. Further, the integrated controlled system has been implemented on a digital signal processor advanced control engineering (dSPACE DS1103) platform and the fractional controller design scheme has been experimentally validated in time and frequency domain for its real-world applicability.

A Review on Optimal Allocation and Sizing Techniques for DG in Distribution Systems

Distributed Generation (DG) offers the reliable and economic source of electricity to consumers. These are connected directly to the distribution system at consumer load points. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. However, Optimal DG (ODG) allocation and sizing is always a challenging task for utilities as well as consumers. The major objective of ODG allocation and sizing is to improve system overall efficiency with minimum power loss, maximum system security, voltage stability, and reliability. Analytical techniques are performing well for small and simple systems, not suitable for a system with large and complex networks. However, various meta-heuristic techniques are performing better in terms of accuracy and convergence for extensively large and complex networks. A hybrid optimization is a combination of two or more optimization techniques. This technique offers efficient and reliable global optimum solutions for complex multi-objective problems. In this context, a comprehensive literature review of DG fundamentals and the different technical approaches for DG integration into the distribution system are analyzed here. Furthermore, an attempt has been made for comparison of analytical, classical (non-heuristic), meta-heuristic and hybrid optimization techniques with respect to objective function, test system, advantages, and disadvantages. This present study will give in-depth knowledge and acts as a forthright reference for imminent investigators and investors for ODG allocation and sizing in a distribution system.Â

Energy conservation routing algorithm for wireless sensor networks using hybrid optimisation approach

In designing wireless sensor network (WSN), the conversation of energy is an important issue to prolong the network lifetime. Most of the energy is wasted during transmission. To overcome this, an energy efficient routing protocol is proposed in this paper using a hybrid optimisation technique. Moreover, a nature inspired hybrid optimisation approach called bacterial swarm optimisation (BSO) is used in this work for energy optimisation in WSN. BSO is an amalgam of two optimisation techniques namely particle swarm optimisation (PSO) and bacterial foraging optimisation (BFO). BSO is used to identify the optimal next hop node for each gateway which in turn results in optimal route. The optimal relay nodes are selected based on lifetime of the gateways and distance in hops between gateways and base station. The results obtained from the simulation show that this proposed BSO based algorithm outperforms the other existing algorithm for routing.

A novel approach for diagnosing heart disease with hybrid classifier

A Cardiovascular disease which is additionally referred to as heart diseases that have been a common and steady issue in the field of medical research. Nowadays various methods were applied which is not robust for the prediction of human being expenses and disease risks for patients. This paper proposed an Orthogonal Local Preserving Projection (OLPP) method to reduce the function dimension of the input high-dimensional data. The dimension reduction improves the prediction rate with the help of hybrid classifier i.e. Group Search Optimization Algorithm (GSO) combine with the Levenberg-Marquardt (LM) training algorithm in the neural network. The LM training algorithm is used to solve the optimization problem and it determines the best network parameters such as weights and bias that minimizes the error. The final output of the optimization technique is combined with the performance metrics as accuracy, sensitivity, and specificity. From the result, it is observed that hybrid optimization techniques increase the accuracy of the heart disease prediction system.

Study of Hybrid Optimization Technique for Grain Optimum Design

Study of Hybrid Optimization Technique for Grain Optimum Design

Deriving Explicit Equations for Optimum Design of a Circular Channel Incorporating a Variable Roughness

The need for optimum design of water conveyance structures provides an active area of study in water resources engineering. A literature review on optimum design of circular channels indicates that variation of Manning’s roughness coefficient (n) with water depth is not taken into account. In this study, this variation has been implemented in the optimum design of lined circular channels. The significant discrepancy between the results obtained for constant and variable roughness scenarios demonstrates the necessity for considering roughness coefficient variability with water depth in circular sections. Furthermore, a new explicit equation for optimum design of section parameters has been proposed using a hybrid optimization technique, which combines the Modified Honey Bee Mating Optimization with Generalized Reduced Gradient algorithms. Solving a typical design problem in the literature by the proposed equation showed not only its adequate performance but also the necessity for considering variable roughness in circular channels design procedure.

A Standalone PV System with a Hybrid P&O MPPT Optimization Technique

In this paper a maximum power point tracking (MPPT) design for a photovoltaic (PV) system using a hybrid optimization technique is proposed. For maximum power transfer, maximum harvestable power from a PV cell in a dynamically changing surrounding should be known. The proposed technique is compared with the conventional Perturb and Observe (P&O) technique. A comparative analysis of power-voltage and current-voltage characteristics of a PV cell with and without the MPPT module when connected to the grid was performed in SIMULINK, to demonstrate the increment in the efficiency of the PV module after using the MPPT module.

Numerical simulation and optimization of steam-assisted gravity drainage with temperature, rate, and well distance control using an efficient hybrid optimization technique

ABSTRACTSteam-assisted gravity drainage (SAGD) is a thermal enhanced oil recovery technique through drilling of two horizontal wells. Effects of steam injection temperature, well rates, and their distance on oil recovery were analyzed and optimized. Steam temperature and well distances remarkably affect SAGD performance. Four metaheuristic algorithms (particle swarm optimization (PSO), imperialist competitive algorithm, cultural algorithm, and Bees algorithm) and pattern search optimization algorithm (PSA) are used for optimization. PSO performs better than other metaheuristics and PSA is the fastest one, while it is probable to be trapped in local optimums. Hybrid PSO-PS is proposed that starts with PSO and proceeds with PSA, and tested in an SAGD project and showed excellence over other techniques.

A Hybrid Optimization for improving Network Performance in Scalable Mobile ad hoc network

Objectives: To optimize the throughput of the network for ensure the seamless connection in a decentralized mobile ad hoc network. Methods/Statistical Analysis: This paper proposes a hybrid optimization technique (ACO-FDR PSO) of Ant Colony Optimization (ACO) integrated with Fitness Distance Ratio Particle Swarm Optimization (FDR PSO) to optimize throughput. ACO algorithm identifies unique path in the network based on higher pheromone concentration. FDR PSO optimizes throughput based on Packet Delivery Factor (PDF). Since FDR PSO employs N-best particle in the velocity update equation, the drawback of PSO such as premature convergence and getting stuck into local optimum can be prevented. Findings: Simulations performed with network simulator 2 on a 100 node network and the metrics, throughput, delay, overhead, energy and packet delivery ratio have been analyzed. Through the results it has been showed that the proposed hybrid technique achieves better results than standard AODV, ACO, PSO and ACO-PSO techniques. Application/ Improvements: The proposed technique facilitates reliable transmission in emergency situations like military application, disaster management etc, where data intensive wireless networks are required. Keywords: Ant Colony Optimization (ACO), Fitness Distance Ratio Particle Swarm Optimization (FDR PSO), Mobile Ad-Hoc Networks (MANET), Optimization, Particle Swarm Optimization (PSO), Position and Velocity Update

Hybrid optimization of a vibration isolation system considering layout of structure and locations of components

A vibration isolation system is designed using novel hybrid optimization techniques, where locations of machines, locations of isolators and layout of supporting structure are all taken as design variables. Instead of conventional parametric optimization model, the 0-1 programming model is established to optimize the locations of machines and isolators so that the time-consuming remeshing procedure and the complicated sensitivity analysis with respect to position parameters can be circumvented. The 0-1 sequence for position design variables is treated as binary bits so as to reduce the actual number of design variables to a great extent. This way the 0-1 programming can be solved in a quite efficient manner using a special version of genetic algorithm(GA) that has been published by the authors. The layout of supporting structure is optimized using SIMP based topology optimization method, where the fictitious elemental densities are taken as design variables ranging from 0 to 1. Influence of different design variables is firstly investigated by numerical examples. Then a hybrid multilevel optimization method is proposed and implemented to simultaneously take all design variables into account.

Optimizing Phylogenetic Queries for Performance.

The vast majority of phylogenetic databases do not support declarative querying using which their contents can be flexibly and conveniently accessed and the template based query interfaces they support do not allow arbitrary speculative queries. They therefore also do not support query optimization leveraging unique phylogeny properties. While a small number of graph query languages such as XQuery, Cypher, and GraphQL exist for computer savvy users, most are too general and complex to be useful for biologists, and too inefficient for large phylogeny querying. In this paper, we discuss a recently introduced visual query language, called PhyQL, that leverages phylogeny specific properties to support essential and powerful constructs for a large class of phylogentic queries. We develop a range of pruning aids, and propose a substantial set of query optimization strategies using these aids suitable for large phylogeny querying. A hybrid optimization technique that exploits a set of indices and "graphlet" partitioning is discussed. A "fail soonest" strategy is used to avoid hopeless processing and is shown to produce dividends. Possible novel optimization techniques yet to be explored are also discussed.

Experimental investigation and control optimization of a ground source heat pump system

This paper presents an experimental investigation and control optimization of a ground source heat pump (GSHP) system. A set of experimental tests was first carried out to examine the performance of the GSHP system under different ground loop differential pressure set-points and different configurations of the ground heat exchangers (GHEs). A model-based optimization strategy was then developed to determine the optimal operating speed of the variable speed pumps in the ground loop system, in which a hybrid optimization technique integrating a performance map-based near-optimal strategy and the exhaustive search method was used to search for the optimal solutions of the optimization problem. The results from the experiments showed that, for the GSHP tested, the coefficient of performance (COP) of the heat pump units increased while that of the whole GSHP system decreased with the increase of the ground loop differential pressure set-point. The GSHP system with GHEs in the parallel operation outperformed that in the series operation. Using the proposed model-based strategy, energy savings of 8.0% during the heating period, and 9.0% during the cooling period can be achieved in comparison to a rule-based strategy with two-stage control for variable speed pumps.