An Improved Teaching-Learning-Based Optimization with Differential Learning and Its Application

Zou, Feng; Wang, Lei; Chen, Debao; Hei, Xinhong

doi:10.1155/2015/754562

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…It is also noteworthy that the strategy of assigning same mean position that represents the mainstream knowledge of population for all learners is contradictory with real-world scenario of teaching and learning because each learner supposed to have slightly different perception on the mainstream knowledge of classroom [42]. In addition, it is observed that the learner phases of some TLBO variants [9,10,15,16,19,20,23,30,[36][37][38][39] did not accurately reflect the actual scenario of peer interaction in classroom. Some of these TLBO variants also only allowed each learner to interact with same peer learner in all dimensions during the learner phase.…”

Section: B Challenges Of Existing Workmentioning

confidence: 97%

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Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

Natarajan

Kaviarasan²,

Ang

et al. 2022

IEEE Access

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Teaching learning-based optimization (TLBO) is a popular algorithm used to solve various optimization problems. Nevertheless, conventional TLBO and some improved variants tends to suffer with premature convergence due to rapid loss of population diversity, especially when handling the challenging optimization problems. Furthermore, it is not practical to tackle real-world multiobjective problems using prior approach given the frequent changes of customers' requirements. Motivated by these challenges, an improved variant known as Modified Multi-objective Teaching Learning Based Optimization-Refined Learning Scheme (MMTLBO-RLS) was proposed as a posterior approach to solve challenging multiobjective optimization problems, including the prediction of optimum turning parameters to machine Polyether ether ketone material (PEEK). Substantial modifications were introduced for teacher and learner phases of MMTLBO-RLS to achieve better balancing of exploration and exploitation searches without incurring excessive computational cost. For modified teacher phase of MMTLBO-RLS, each learner was guided by a unique teacher solution and unique mean position to perform searching with better diversity. Meanwhile, two new learning strategies are incorporated into the modified learner phase of MMTLBO-RLS, enabling all learners to enhance their knowledge more efficiently based on their learning preferences. A systematic approach was followed to develop modelling equations required for optimization. The developed algorithm was then employed in single objective optimization as well as multiobjective optimization to cater its performances in any real-world environment. The prediction model reports that surface roughness of 1.1042μm and material removal rate of 22.8991 cm 3 /minute can be achieved. The predicted results differ from validation results by less than 2.69% in any case of optimization. A benchmarking on the performance of MMTLBO-RLS in solving CEC 2009 multiobjective benchmark functions was further carried out with other seven meta-heuristic algorithms. The superior performance of MMTLBO-RLS proves that it is not only suitable to be used in industries to produce the parts of PEEK with supportive quality and quantity, but it is also able to solve other multiobjective optimization problems with competitive performances.

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Section: B Challenges Of Existing Workmentioning

confidence: 97%

“…In [15], differential learning TLBO (DLTLBO) was proposed to search for the optimal parameters of digital infinite impulse response filter. It was leveraged by interactive learning strategies designed in teacher phase.…”

Section: ) Single Objective Optimizationmentioning

confidence: 99%

Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

Natarajan

Kaviarasan²,

Ang

et al. 2022

IEEE Access

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“…Huang et al [30] proposed an effective teaching-learning-based cuckoo search (TLCS) algorithm for parameter optimization problems in structure designing and machining processes. Zou et al [31] proposed an improved teaching-learning-based optimization with differential learning (DLTLBO) for IIR System Identification problems. Wang et al [32] combined TLBO with differential evolution to propose TLBO-DE for chaotic time series prediction.…”

Section: Improvements On Tlbomentioning

confidence: 99%

“…(i) Basic TLBO [11] (ii) Modified TLBO (mTLBO) [23] (iii) Differential learning TLBO (DLTLBO) [31] (iv) Nonlinear inertia weighted TLBO (NIWTLBO) [22] (v) TLBO with learning experience of other learners (LETLBO) [24] (vi) Generalized oppositional TLBO (GOTLBO) [44] Table 1 lists the parameter settings for LebTLBO and the other TLBO algorithms. Table 2 shows the mean error and standard deviation (in bracket) of the function error values obtained by all the TLBO algorithms.…”

Section: Comparison With Other Tlbo Algorithmsmentioning

confidence: 99%

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

et al. 2018

Journal of Applied Mathematics

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Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

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“…One of the most important issues is that the population tends to be trapped in the local optima solution because of diversity loss. To improve the performance of the original TLBO, a few modified or improved algorithms are proposed in recent years, such as teaching-learning-based optimization with dynamic group strategy (DGSTLBO) [20], teaching-learning-based optimization with neighborhood search (NSTLBO) [21], an elitist teaching-learning-based optimization algorithm (ETLBO) [22], and a variant of teaching-learning-based optimization algorithm with differential learning (DLTLBO) [23]. These modified TLBOs have better performance than the original TLBO on classical benchmark functions.…”

Section: Introductionmentioning

confidence: 99%

Parameter Determination of Milling Process Using a Novel Teaching-Learning-Based Optimization Algorithm

Zhai

Liu

2015

Mathematical Problems in Engineering

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Cutting parameter optimization dramatically affects the production time, cost, profit rate, and the quality of the final products, in milling operations. Aiming to select the optimum machining parameters in multitool milling operations such as corner milling, face milling, pocket milling, and slot milling, this paper presents a novel version of TLBO, TLBO with dynamic assignment learning strategy (DATLBO), in which all the learners are divided into three categories based on their results in “Learner Phase”: good learners, moderate learners, and poor ones. Good learners are self-motivated and try to learn by themselves; each moderate learner uses a probabilistic approach to select one of good learners to learn; each poor learner also uses a probabilistic approach to select several moderate learners to learn. The CEC2005 contest benchmark problems are first used to illustrate the effectiveness of the proposed algorithm. Finally, the DATLBO algorithm is applied to a multitool milling process based on maximum profit rate criterion with five practical technological constraints. The unit time, unit cost, and profit rate from the Handbook (HB), Feasible Direction (FD) method, Genetic Algorithm (GA) method, five other TLBO variants, and DATLBO are compared, illustrating that the proposed approach is more effective than HB, FD, GA, and five other TLBO variants.

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An Improved Teaching-Learning-Based Optimization with Differential Learning and Its Application

Cited by 9 publications

References 35 publications

Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

Parameter Determination of Milling Process Using a Novel Teaching-Learning-Based Optimization Algorithm

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