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1 – 10 of 138The 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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Bourahla Kheireddine, Belli Zoubida and Hacib Tarik
This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.
Abstract
Purpose
This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.
Design/methodology/approach
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).
Findings
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.
Originality value
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.
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Xueshan Gao, Yu Mu and Yongzhuo Gao
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
Experimental results with a 6-DOF robotic manipulator applied to surface polishing of metallic workpiece verify the effectiveness of the method.
Originality/value
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.
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Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri and Sung-Bae Cho
This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the…
Abstract
This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.
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Bourahla Kheireddine, Belli Zoubida, Hacib Tarik and Achoui Imed
This study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm…
Abstract
Purpose
This study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization.
Design/methodology/approach
The optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field.
Findings
To improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized.
Originality/value
The proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.
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Nurcan Sarikaya Basturk and Abdurrahman Sahinkaya
The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control…
Abstract
Purpose
The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control problem.
Design/methodology/approach
Landing sequence and corresponding landing times for the aircrafts were determined by using population-based optimization algorithms such as artificial bee colony, particle swarm, differential evolution, biogeography-based optimization, simulated annealing, firefly and teaching–learning-based optimization. To obtain a fair comparison, all simulations were repeated 30 times for each of the seven algorithms, two different problems and two different population sizes, and many different criteria were used.
Findings
Compared to conventional methods that depend on a single solution at the same time, population-based algorithms have simultaneously produced many alternate possible solutions that can be used recursively to achieve better results.
Research limitations/implications
In some cases, it may take slightly longer to obtain the optimum landing sequence and times compared to the methods that give a direct result; however, the processing times can be reduced using powerful computers or GPU computations.
Practical implications
The simulation results showed that using population-based optimization algorithms were useful to obtain optimal landing sequence and corresponding landing times. Thus, the proposed air traffic control method can also be used effectively in real airport applications.
Social implications
By using population-based algorithms, air traffic control can be performed more effectively. In this way, there will be more efficient planning of passengers’ travel schedules and efficient airport operations.
Originality/value
The study compares the performances of recent and state-of-the-art optimization algorithms in terms of effective air traffic control and provides a useful approach.
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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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Debasree Saha, Asim Datta, Biman Kumar Saha Roy and Priyanath Das
Directional Overcurrent Relay (DOCR) coordination computation allowing for desired and high level accuracy in interconnected power systems is very difficult and is a highly…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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).
Originality/value
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.
Dheeraj Joshi, M.L. Mittal, Milind Kumar Sharma and Manish Kumar
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
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Farbod Zahedi, Hamidreza Kia and Mohammad Khalilzadeh
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…
Abstract
Purpose
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.
Design/methodology/approach
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).
Findings
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.
Originality/value
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.
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