Performance Of Teaching-learning-based Optimization Research Articles

An enhanced teaching-learning-based optimization algorithm with self-adaptive and learning operators and its search bias towards origin

Abstract Teaching-learning-based optimization (TLBO) is a recently proposed meta-heuristic optimization method and demonstrates outstanding performance for solving numerous sciences and engineering problems. However, many studies have shown that TLBO has a strong bias towards converging to origin and poorly performs in the problems with shifted solutions. To conquer this problem, a novel self-adaptive hybrid self-learning based TLBO (SHSLTLBO) is proposed in this paper. By constructing a self-adaptive framework, the original TLBO is fused with the Gaussian distribution, and the novel updating law switches in two modes corresponding to the fitness during the searching process. Additionally, in order to avoid local convergence in the initialization, a self-learning phase is introduced, associated to the original teaching and learning phase. The performance of the proposed SHSLTLBO is tested in the numerical benchmark functions, where the comprehensive comparison carries out with the state-of-the-art TLBO variants and other meta-heuristic approaches. The results demonstrate superior advantages of the proposed algorithm at balancing along the evolutionary stages among the TLBO variants. In the experiments, the two well-performed meta-heuristic optimization methods, namely LSHADE and HCLPSO, are used as the comparative methods to SHSLTLBO, where their superior performances of them are shown on the shifted problems. With increasing dimensionality, the performance of TLBO goes down rapidly while LSHADE's relative ranking improves. Apart from these scenarios, the comparative results of different dimensional problems indicate that the proposed SHSLTLBO has the best convergence and stability in solving all 28 benchmarks with low dimension and shifted solutions.

Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm

Reservoir operation and management are complex engineering problems, due to the stochastic nature of inflow, various demands and as well as tailwater in the downstream. The complexity increases when the number of reservoirs gets increased such as multi-reservoir system or chain system. To obtain optimal operation in such condition become more difficult. It requires powerful optimization algorithm to solve aforesaid problems. Teaching Learning Based Optimization (TLBO) algorithm and Jaya Algorithm (JA) are recently developed advanced optimization techniques a novel approach comparatively simple, easy, and robust. The main advantages of these algorithms are it only requires the common control parameters such as number of iterations and population size. In the present study, three different benchmark problems were evaluated to check the applicability and performance of TLBO and JA in multi-reservoir operation problems. The benchmark problems are the discrete time four-reservoir operation (DFRO), the continuous time four-reservoir operation (CFRO), and the ten-reservoir operation (TRO). The results from the TLBO and JA are compared with different approaches from the literature. The optimal net benefits obtained from JA for DFRO, CFRO and TRO problems are 401.44, 308.40 and 1194.59, respectively, and that of TLBO algorithm are 401.33, 308.30 and 1194.44, respectively. It is found that both JA and TLBO algorithms provided a satisfactory solution as other optimization techniques, from literature. In conclusion, JA outperformed over TLBO.

Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms

Abstract This paper explores use of Teaching Learning Based Optimization (TLBO), ‘JAYA’ (Sanskrit word means Victory) and Genetic Algorithm (GA) for the combined minimization of roughness of machined surface and forces generated in cutting in turning of Ti-6Al-4V. Experimentation was carried out with Response Surface Methodology (RSM) and the Central Composite Design (CCD). Speed of cutting (m/min), feed rate (mm/min) and depth of cut (mm) were the design variables for optimization. Two responses (roughness of machined surface and force of cutting) were independently minimized. RSM was useful in finding empirical relations and the effect of each parameter and their interactions on the responses considered. Analysis of variance (ANOVA) was used to find out the effective and non-effective factors and correctness of the models. Later on, a multi-objective optimization function was developed for minimizing both – roughness in machined surface and force generated in cutting using weights method and the correctness of weights were confirmed by Analytical Hierarchy Process (AHP). After formulating the combined objective function, TLBO, ‘JAYA’ and GA methods were used for further parameter optimization of the turning process. Performance of TLBO and ‘JAYA’ algorithm was compared with that of Genetic Algorithm (GA). It is found that TLBO and ‘JAYA’ performed better than GA in the combined minimization of roughness and forces in while turning Ti-6Al-4V. It is also found from the results that higher cutting speed (171.4 m/min) and lower feed rate (55.6 mm/min) can produce better surface roughness and minimum cutting forces in machining of Ti-6Al-4V. Highlights This paper Presents, implementation of Advanced Algorithms for multi objective optimization of cutting force and surface roughness in machining of difficult to cut Ti-6Al-4V. Two newly developed advanced algorithms such as JAYA and Teaching learning based optimization (TLBO) ‘without algorithm control’ parameters are used for machining response optimization. Objective functions for surface roughness and cutting forces are developed after actual face milling operation performed in sequential manner with response surface methodology. Developed models are verified with statistical test (ANOVA, residual plots) as well as confirmation experiments. It is concluded from the results that machining parameters can be optimize using advanced algorithms. This work can help machinists to select cutting parameters based on desired machining response.

An improved teaching–learning-based optimization with neighborhood search for applications of ANN

Teaching–learning-based optimization (TLBO) algorithm, which simulates the teaching–learning process of the class room, is one of the recently proposed swarm intelligent (SI) algorithms. The performance of TLBO is maintained by the teaching and learning process, but when the learners cannot found a better position than the old one at some successive iteration, the population might be trapped into local optima. In this paper, an improved teaching–learning-based optimization algorithm with neighborhood search (NSTLBO) is presented. In the proposed method, a ring neighborhood topology is introduced into the original TLBO algorithm to maintain the exploration ability of the population. Different than the traditional method to utilize the global information, the mutation of each learner is now restricted within a certain neighboring area so as to fully utilize the whole space and avoid over-congestion around local optima. Moreover, a mutation operation is presented to NSTLBO during the duplicate eliminations in order to maintain the diversity of population. To verify the performance of the proposed algorithm, thirty-two benchmark functions are utilized. Finally, three application problems of artificial neural network are examined. The results in thirty-two benchmark functions and three applications of ANN indicate that the proposed algorithm has shown interesting outcomes.