Quasi-oppositional Teaching Learning Based Optimization Research Articles

Modelling of Green Human Resource Management using Pythagorean Neutrosophic Bonferroni Mean Approach

Green Human Resource Management (GHRM) state a determination of the association using crossing points of employees to stimulate environment performance activity, increase the employee awareness and sustainable activities, consequently, increasing the employee awareness towards environmental challenges. The hotel industry is developing quickly in emerging nations owing to an upsurge in the tourism business; but, conversely, the hotel industry is mainly growing the problem of the environment. As a result, owing to the enormous amount of conservation problems that hotel business has faced, there is a growing potency to pay an accurate response to environmental problems and performing sustainable industry performance like the adoption of GHRM practice provides a win-win situation for its stakeholders and the organization. Accordingly, it indicates the requirement to scrutinize how GHRM performs will augment the environment in the hotel business. This manuscript models the design of GHRM using Pythagorean Neutrosophic Bonferroni Mean (GHRM-PNBM) approach. The presented GHRM-PNBM method objectives are to evaluate the limitation of hotel GRHM. Moreover, the presented technique constructs an expert system analysis technique for assessing the performance of hotel GHRM. Adaptive optimization of hotel GHRM assessment can be done using the PNBM technique, and the parameter selection method can be done using Quasi-Oppositional-Teaching-Learning-Based Optimization (QTLBO) method. The empirical analysis reports that the performance calculation of hotel GHRM has good confidence level and high accuracy

Optimal power flow-based approach for grid dispatch problems through Rao algorithms

In order to fulfill the load demand and operational restrictions, the grid dispatch problem's optimization goal is to reduce fuel cost through variation of active and reactive power of the generators within fixed limits and to uplift the security of the power plants during voltage instability through proper allocation of static voltage compensator to ensure minimum power loss. This paper proposes a collaborative optimization of economic load dispatch and reactive power dispatch using three metaphor-less algorithms called Rao algorithms, where the current solution will interact with the finest and inferior solutions in the population, as well as a randomly selected solution. These strategies are free from any specific control parameters. These algorithms are formulated on the IEEE 30 bus system. The outcomes of the suggested techniques are compared with those of existing optimization approaches such as Bat algorithm (BOA), Modified Harmonic search algorithm (MSG-HS), Root tree optimization (RTO), Adaptive Gravitational search optimization (AGSO), Whale optimization algorithm (WOA), Fruit fly optimization (MOFOA), Crow search algorithm (CSA), Quasi-Oppositional Teaching learning based optimization (QOTLBO), and Jaya algorithm (JA) which demonstrates that the proposed strategies have great convergence characteristics and are the most systematic way to addressing these complicated power system issues. They are unable to converge on a local optimal solution prematurely while maintaining a high level of precision.

Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm

In this article, a new optimization technique, the backtracking search algorithm (BSA), is proposed to solve the hydrothermal scheduling problem. The BSA has mainly unique five steps: (i) Initialization; (ii) Selection – I; (iii) Mutation; (iv) Crossover; and (v) Selection – II; which have been applied to minimize fuel cost of the hydro-thermal scheduling problem. The BSA is very fast, robust, reliable optimization technique and gives an accurate, optimized result. Mutation and crossover are very effective steps of the BSA, which help to determine the better optimum value of the objective function. Here, four hydro and three thermal power generating units are considered. Performance of each committed generating units (hydro and thermal) are also analyzed using a new proposed algorithm, the BSA. A multi-reservoir cascaded hydroelectric with a nonlinear relationship between water discharge rate and power generation is considered. The valve point loading effect is also considered with a fuel cost function. The proposed optimum fuel cost obtained from the BSA shows the better result as compared to other techniques like particle swarm optimization (PSO), teaching learning-based optimization (TLBO), quasi-oppositional teaching learning-based optimization (QOTLBO), real-coded genetic algorithm (RCGA), mixed-integer linear programming (MILP) and krill herd algorithm (KHA), etc.

Optimal active and reactive power allocation in distribution networks using a novel heuristic approach

A wide range of solution methodologies has been presented in the literature for the siting and sizing of active and reactive power sources in radial distribution systems. The solution techniques are mainly classified into mathematical programming algorithms, heuristics, meta-heuristic methods, and the analytical approaches. This paper proposes a novel heuristic method for optimal allocation of distributed generation (DG) and capacitor banks. The conducted approach is easy for implementation and does not suffer from the complicacy of the other methods. Based on some mathematical calculations, two main formulations are derived. Then, the resulting formulations are employed to site and size capacitor bank and DG unit in the 33-bus distribution network to verify its performance by comparing the obtained results with those of global search method. Afterward, the proposed method is used to allocate a specified number of DGs and capacitors in the 33 and 69-bus systems. Finally, the conducted approach is employed to allocate multiple DGs and capacitors, and also multiple DGs with optimal power factor in the 119-bus distribution network. The recently presented methodologies including particle swarm optimization (PSO), improved analytical (IA), hybrid analytical-PSO, Teaching Learning Based Optimization (TLBO), Quasi-Oppositional Teaching Learning based optimization (QOTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO), and LSF-BFOA algorithms are used for investigating the efficiency of the developed method. The simulation results demonstrate that the presented heuristic approach is robust in finding the optimal results, very fast and easy to implement, applicable to large distribution systems and it give better results than the algorithms presented so far.

A hybrid teaching–learning-based optimization technique for optimal DG sizing and placement in radial distribution systems

Distributed generation (DG) technology has proved to be an efficient and economical way of generation of power. DGs are intended to generate power near the load centers. Optimal allocation of DG resources enhances the overall performance of distribution systems. This paper presents a hybrid teaching–learning-based optimization (HTLBO) technique for the optimal allocation of DGs in distribution systems. The proposed technique is proficient in handling continuous as well as discrete variables and has the capability to escape strong local minima/maxima trappings. The validity and effectiveness of HTLBO are tested on well-defined standard mathematical benchmark functions. The proposed method is further implemented for optimal allocation of DGs in the IEEE 33-bus, 69-bus and 118-bus radial distribution test systems for minimization of power losses, voltage deviation and maximization of voltage stability index. The multi-objective function for DG allocation uses the ɛ-constraints approach. The obtained results reveal improved convergence characteristics over both teaching–learning-based optimization and quasi-oppositional teaching–learning-based optimization.

A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems

Distributed generation (DG) is a better alternative to meet power demand near the load centers than centralized power generation. Optimal placement and sizing of DGs plays a crucial role in improving the performance of distribution systems in terms of network loss reduction, voltage profile improvement, reliability of power supply and stability issues. This paper presents a comprehensive teaching learning-based optimization (CTLBO) technique for the optimal allocation of DGs in radial distribution systems to improve network loss reduction, voltage profile and annual energy savings. The proposed technique can handle mixed integer variables, is parameter independent and possesses immunity to local extrema trappings. The effectiveness of the proposed method is first validated on standard mathematical benchmark functions. It is observed to have better convergence characteristics than teaching learning-based optimization (TLBO) and quasi-oppositional teaching learning-based optimization (QOTLBO). Subsequently, it is applied to optimal DG allocation in IEEE 33-bus, 69-bus and 118-bus radial distribution test systems. Both single and multi-objective formulations are considered. In addition, the selection of the optimal number of DGs in the distribution networks is also investigated and case studies are carried out. Results demonstrate that optimal allocation of DGs using the proposed technique results in marked improvement in the performance of distribution systems over TLBO and QOTLBO. The applicability of the proposed technique for DG allocation in distribution systems with practical load profiles results in further improvement in annual energy loss reduction and cost savings.

Optimal Allocation of Static Synchronous Series Compensator Controllers using Chemical Reaction Optimization for Reactive Power Dispatch

Static synchronous series compensator (SSSC) is one of the most effective flexible AC transmission systems (FACTS) devices used for enhancing power system security. In this paper, optimal location and sizing of SSSC are investigated for solving the optimal reactive power dispatch (ORPD) problem in order to minimize the active power loss in the transmission networks. A new and efficient chemical reaction optimization (CRO) is proposed to find the feasible optimal solution of the SSSC based optimal reactive power dispatch (ORPD) problem. The proposed approach is carried out on the standard IEEE 30 bus and IEEE 57 bus test systems. The optimization results obtained by the proposed CRO are analyzed and compared with the same obtained from genetic algorithm (GA), teaching learning based optimization (TLBO), quasi-oppositional TLBO (QOTLBO) and strength pareto evolutionary algorithm (SPEA). The results demonstrate the capabilities of the proposed approach to generate true and well-distributed optimal solutions.

Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

In this paper, quasi-oppositional teaching-learning based optimization (QOTLBO) is introduced and successfully applied for solving an optimal power flow (OPF) problem in power system incorporating flexible AC transmission systems (FACTS). The main drawback of the original teaching-learning based optimization (TLBO) is that it gives a local optimal solution rather than the near global optimal one in limited iteration cycles. In this paper, opposition based learning (OBL) concept is introduced to improve the convergence speed and simulation results of TLBO. The effectiveness of the proposed method implemented with MATLAB and tested on modified IEEE 30-bus system in four different cases. The simulation results show the effectiveness and accuracy of the proposed QOTLBO algorithm over other methods like conventional BBO and hybrid biogeography-based optimization (HDE-BBO). This method gives better solution quality in finding the optimal parameter settings for FACTS devices to solve OPF problems. The simulation study also shows that using FACTS devices, it is possible to improve the quality of the electric power supply thereby providing an economically attractive solution to power system problems.

Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network

Abstract Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.

Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems

This paper presents a novel quasi-oppositional teaching learning based optimization (QOTLBO) methodology in order to find the optimal location of distributed generator to simultaneously optimize power loss, voltage stability index and voltage deviation of radial distribution network. The basic disadvantage of the original teaching learning based optimization (TLBO) algorithm is that it gives a near optimal solution rather than an optimal one in a limited iteration cycles. In this paper, opposition based learning (OBL) and quasi OBL concepts are introduced in original TLBO algorithm for improving the convergence speed and simulation results of TLBO. In order to show the effectiveness and superiority, the proposed algorithms are tested on 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results of the proposed methods are compared with those obtained by other artificial intelligence techniques like GA/PSO, GA, PSO and loss sensitivity factor simulated annealing (LSFSA). The results show that the QOTLBO surpasses the other techniques in terms of solution quality.

Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.

Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem

This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques.

Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization

This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem.