Search results

The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.

Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS).

At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work.

New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.

The paper aims to present an application of teaching learning-based optimization (TLBO) algorithm and static Var compensator (SVC) to improve the steady state and dynamic performance of self-excited induction generators (SEIG).

The TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with different types of prime mover. For a constant speed prime mover, the TLBO algorithm attains the optimal capacitance to have rated load voltage at different loading conditions. In the case of variable speed prime mover, the TLBO methodology is used to obtain the optimal capacitance and prime mover speed to have rated load voltage and frequency. The SVC of fixed capacitor and controlled reactor is used to have a fine tune in capacitance value and control the reactive power. The parameters of SVC are obtained using the TLBO algorithm.

The whole system of three-phase induction generator and SVC are established under MatLab/Simulink environment. The performance of the SEIG is demonstrated on two different ratings (i.e. 7.5 kW and 1.5 kW) using the TLBO algorithm and SVC. An experimental setup is built-up using a 1.5 kW three-phase induction machine to confirm the theoretical analysis. The TLBO results are matched with other meta heuristic optimization techniques.

The paper presents an application of the meta-heuristic algorithms and SVC to analysis the steady state and dynamic performance of SEIG with optimal performance.

The vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.

In this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).

The results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.

In this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

The purpose of this paper is to consider one of the recent and practical extensions of the resource-constrained project scheduling problem (RCPSP) termed as the multi-skill resource-constrained project scheduling problem (MSRCPSP) for investigation. The objective is the minimization of the makespan or total project duration.

To solve this complex problem, the authors propose a teaching–learning-based optimization (TLBO) algorithm in which self-study and examination have been used as additional features to enhance its exploration and exploitation capabilities. An activity list-based encoding scheme has been modified to include the resource assignment information because of the multi-skill nature of the algorithm. In addition, a genetic algorithm (GA) is also developed in this work for the purpose of comparisons. The computational experiments are performed on 216 test instances with varying complexity and characteristics generated for the purpose.

The results obtained after computations show that the TLBO has performed significantly better than GA in terms of average percentage deviation from the critical path-based lower bound for different combinations of three parameters, namely, skill factor, network complexity and modified resource strength.

The modified TLBO proposed in this paper can be conveniently applied to any product or service organization wherein human resources are involved in executing project activities.

The developed model can suitably handle resource allocation problems faced in real-life large-sized projects usually administered in software development companies, consultancy firms, R&D-based organizations, maintenance firms, big construction houses, etc. wherein human resources are involved.

The current work aims to propose an effective metaheuristic for a more realistic version of MSRCPSP, in which resource requirements of activities may be more than one. Moreover, to enhance the exploration and exploitation capabilities of the original TLBO, the authors use two additional concepts, namely, self-study and examination in the search process.

Directional Overcurrent Relay (DOCR) coordination computation allowing for desired and high level accuracy in interconnected power systems is very difficult and is a highly constraint oriented optimization problem. This paper aims to study the effectiveness of a newly reported optimization technique, Teaching Learning Based Optimization (TLBO), in protective relay coordination comparing with a widely used optimization technique, Particle Swarm Optimization (PSO).

DOCR coordination in electric power systems is considered as an optimization problem by formulating objective function and specifying problem constraints. Optimum values of the DOCR adjustment parameters (Time Dial Setting and Plug Setting) in terms of reliable coordination margin and operating times of relays are computed by both the algorithms, TLBO and PSO. Optimal coordination is verified in three test bus systems: IEEE 6-bus, WSCC 9-bus and IEEE 14-bus systems.

A comparison between the numerical results of using both the algorithms indicates that the TLBO gives better results in terms of the total operating times of relays and Coordination Time Interval (CTI).

This paper represents the performance of a newly reported optimization technique, TLBO which is till now unpopular to protection engineers to be applied in protective relay coordination applications. The technique provides better performance in comparison to the widely applied technique, PSO. It is expected that TLBO would facilitate protection engineers to decide the optimum and appropriate settings of the relays for leading exact relays coordination.

The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand.

The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as Teaching Learning Based Optimization (TLBO) for solving the SMS problem based on reliability.

The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology.

The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.

The purpose of this paper is to propose a method of optimal trajectory planning for robotic manipulators that applies an improved teaching-learning-based optimization (ITLBO) algorithm.

The ITLBO algorithm possesses better ability to escape from the local optimum by integrating the original TLBO with variable neighborhood search. The trajectory of robotic manipulators complying with the kinematical constraints is constructed by fifth-order B-spline curves. The objective function to be minimized is execution time of the trajectory.

Experimental results with a 6-DOF robotic manipulator applied to surface polishing of metallic workpiece verify the effectiveness of the method.

The presented ITLBO algorithm is more efficient than the original TLBO algorithm and its variants. It can be applied to any robotic manipulators to generate time-optimal trajectories.

Electric discharge drilling (EDD) is used to drill quality microholes on any conductive materials. EDD process parameters play a crucial role in the drilling. Depending upon the material characteristics, the cost of drilling also changes. Therefore, a suitable method is required to control the process parameters and drill quality microholes.

The input process parameters in the present work are peak current (Ip), pulse on-time (Ton) and pulse off-time (Toff). The trials were intended in accordance to central composite face-centered design of response surface methodology (RSM). The output responses, namely drilling rate (DR) and electrode wear ratio (EWR), were converted into a single response, that is, grade using Grey relational analysis (GRA). The grade value is further modeled by regression analysis. The empirical model was figured out using teaching–learning-based optimization (TLBO). The RSM-Grey-TLBO-based multicriteria decision-making (MCDM) is used to investigate the optimized process parameter setting.

The RSM-Grey-TLBO-based MCDM approach suggests that the optimized setting for DR and EWR is Ip: 3A; Ton: 40 µs; Toff: 42 µs. The percentage errors for the predicted and experimental results are 8.1 and 7.5% in DR and EWR, respectively.

The parametric optimization of EDD using RSM-Grey-TLBO-based MCDM approach while machining commercially pure titanium is still underway. Thus, this MCDM approach will give a path to the researchers working in this direction.

Stainless steel is widely used in different manufacturing sectors. The purpose of this study is to optimize the process parameters of machining while processing SS316L alloy. The optimization of machining characteristics in the case of SS316L alloy greatly improves the quality and productivity economically.

The machining variables in current research are depth of cut, spindle speed and feed rate. The optimization of response characteristics was carried out using the intelligent approach of grey, regression and teaching learning-based optimization (TLBO) and Taguchi-Grey approach. Planning of experiments was made using Taguchi’s based L27 orthogonal array. With the implementation of grey, the response characteristics were normalized and converted into a single response. The regression analysis was used for empirical modeling of the single response induced from the grey application. TLBO is further used to investigate the combinations of machining variables and compared with grey theory.

The grey-TLBO based multi-criteria decision-making approach suggests that the optimized setting for material removal rate, mean roughness depth (Rz) and cutting force (Fz) is spindle speed (N): 720 rpm; feed rate (F): 0.3 mm/rev; depth of cut (DoC): 1.7 mm. The grey theory suggests an optimized setting as N: 720 rpm; F: 0.2 mm/rev and DoC: 1.7 mm.

The parametric optimization during the turning of SS316L using grey-TLBO based intelligent approach is not performed till now. Thus, this intelligent approach will give a path to the researchers working in this direction. However, the grey theory performs better as compared to the grey-TLBO approach.