scholarly journals Optimization of water productivity in Bhagwanpur distributary command of India employing TLBO and cuckoo search algorithms

Water Policy ◽  
2021 ◽  
Author(s):  
Ashruti Upadhyaya ◽  
Ashutosh Upadhyaya
Keyword(s):  
Objective Function ◽  
Water Productivity ◽  
Cuckoo Search ◽  
Cropping Pattern ◽  
Net Return ◽  
Teaching Learning Based Optimization ◽  
Optimum Productivity ◽  
Teaching Learning ◽  
Optimum Water ◽  
Better Than

Abstract Efficient and judicious use of land and water is the need of the hour. In other words, evolving a cropping pattern, which optimizes productivity or net return considering prevailing constraints is quite useful to farmers, because such a cropping pattern is expected to be better than the existing cropping pattern in terms of yielding optimum productivity or net return. A single objective problem consisting of an objective function of optimization of water productivity with prevailing constraints was formulated and three optimization algorithms, namely (i) LINPROG, (ii) teaching learning based optimization (TLBO) and (iii) cuckoo search (CS) were employed to compute optimum water productivity corresponding to various affinity levels. It was observed that all three approaches yielded exactly the same values of water productivity at different affinity levels. TLBO showed better convergence capability as it reached the optimum value of objective function at a lower number of iterations than CS technique. Optimum water productivity at 20% affinity level seems quite practical and reasonable to be recommended in this distributary command for adoption because water productivity value is 2.57 times higher with 98.25 ha less cropped area as compared to the value of water productivity for the existing cropping pattern.

PMU Placement Optimization for Fault Observation Using Different Techniques

2021 ◽  
pp. 196-220
Author(s):  
Hamid Bentarzi
Keyword(s):  
Cuckoo Search ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Placement Optimization ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Pmu Placement ◽  
Teaching Learning ◽  
Full Network

This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.

Synthesis of thinned planar antenna arrays using teaching–learning-based optimization

2014 ◽  
Vol 7 (5) ◽  
pp. 557-563 ◽  
Author(s):  
Nihad I. Dib
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Optimization Method ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Planar Antenna ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Level Reduction ◽  
Better Than

In this paper, the design of thinned planar antenna arrays of isotropic radiators with optimum side lobe level reduction is studied. The teaching–learning-based optimization (TLBO) method, a newly proposed global evolutionary optimization method, is used to determine an optimum set of turned-ON elements of thinned planar antenna arrays that provides a radiation pattern with optimum side lobe level reduction. The TLBO represents a new algorithm for optimization problems in antenna arrays design. It is shown that the TLBO provides results that are better than (or the same as) those obtained using other evolutionary algorithms.

scholarly journals Minimization of Machine Idle Time and Penalty Cost in Flexible Manufacturing System Scheduling

2019 ◽  
Vol 8 (6) ◽  
pp. 483-487
Keyword(s):  
Objective Function ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Cuckoo Search ◽  
Scheduling Problem ◽  
Jaya Algorithm ◽  
Penalty Cost ◽  
Paper Address ◽  
Better Than

This paper address the application of Jaya algorithm to solve Multi objective scheduling problem in Flexible Manufacturing System(FMS) to Minimize the Combined Objective Function(COF) Value. The effectiveness of this algorithm is tested on the problem of 43 jobs processed on 16 machines taken from literature. The MATLAB code is written to find best sequence and Combined Objective Function value by implementing Jaya Algorithm. Results obtained by Jaya Algorithm are compared with different algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Shortest Processing Time (SPT), Cuckoo Search (CS) and Modified Cuckoos Search (MCS) for the problem considered. It is observed from the results that COF value for the sequence obtained by Jaya Algorithm is better than other algorithms. It is concluded that the Jaya algorithm is best suitable for solving the Scheduling problem considered in Flexible Manufacturing System.

Multiobjective optimization of in situ process parameters in preparation of Al-4.5%Cu–TiC MMC using a grey relation based teaching–learning-based optimization algorithm

2017 ◽  
Vol 232 (4) ◽  
pp. 393-407 ◽  
Author(s):  
Biswajit Das ◽  
Susmita Roy ◽  
RN Rai ◽  
SC Saha
Keyword(s):  
Objective Function ◽  
Metal Matrix Composite ◽  
Parameter Optimization ◽  
Process Parameters ◽  
Optimization Algorithm ◽  
Metal Matrix ◽  
Optimization Problems ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In modern in situ composite fabrication processes, the selection of optimal process parameters is greatly important for the preparation of best quality metal matrix composite. For achieving high-quality composite, an efficient optimization technique is essential. The present study explores the potential of a new robust algorithm named teaching–learning-based optimization algorithm for in situ process parameter optimization problems in fabrication of Al-4.5%Cu–TiC metal matrix composite fabricated by stir casting technique. Optimization process is carried out for optimizing the in situ processing parameters i.e. pouring temperature, stirring speed, reaction time for achieving better mechanical properties, i.e. better microhardness, toughness, and ultimate tensile strength. Taguchi’s L25 orthogonal array design of experiment was used for performing the experiments. Grey relational analysis is used for the conversion of the multiobjective function into a single objective function, which is being used as the objective function in the teaching–learning-based optimization algorithm. Confirmation test results show that the developed teaching–learning-based optimization model is a very efficient and robust approach for engineering materials process parameter optimization problems.

scholarly journals Bare-Bones Teaching-Learning-Based Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Feng Zou ◽  
Lei Wang ◽  
Xinhong Hei ◽  
Debao Chen ◽  
Qiaoyong Jiang ◽  
...  
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Class Room ◽  
Teaching Learning ◽  
Bare Bones ◽  
Gaussian Sampling ◽  
Better Than

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. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

scholarly journals Improvement of Teaching-Learning-Based-Optimization

2020 ◽  
Vol 189 ◽  
pp. 03027
Author(s):  
Meifang Tan ◽  
Xin Wang ◽  
Jianjian Wang ◽  
Xiaotian Lv
Keyword(s):  
Optimization Algorithm ◽  
Learning Effect ◽  
Teaching Process ◽  
Teaching Mode ◽  
Teaching Learning Based Optimization ◽  
One To One ◽  
Teaching Learning ◽  
Better Than

Teaching-Learning-Based-Optimization is an optimization algorithm that simulates the teaching process. In the standard Teaching-Learning-Based-Optimization there are some problems such as precocity and low optimization accuracy. Through daily discovery, students’ learning effect is better when there are exercise lessons than when there are no exercise lessons. Students who study toward teacher on one’s own learn better than students who do not. Therefore, this paper proposes a teaching mode that combines exercise lessons and one-to-one to improve the Teaching-Learning-Based-Optimization.

scholarly journals Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 178
Author(s):  
Lindokuhle J. Mpanza ◽  
Jimoh Olarewaju Pedro
Keyword(s):  
Objective Function ◽  
Flight Control ◽  
Degrees Of Freedom ◽  
Cuckoo Search ◽  
Pid Controllers ◽  
Flight Control System ◽  
Forward Flight ◽  
Six Degrees Of Freedom ◽  
Medium Scale ◽  
Better Than

This paper presents the parameter optimisation of the flight control system of a singlerotor medium-scale rotorcraft. The six degrees-of-freedom (DOF) nonlinear mathematical model of the rotorcraft is developed. This model is then used to develop proportional–integral–derivative (PID)-based controllers. Since the majority of PID controllers installed in industry are poorly tuned, this paper presents a comparison of the optimised tuning of the flight controller parameters using particle swarm optimisation (PSO), genetic algorithm (GA), ant colony optimisation (ACO) and cuckoo search (CS) optimisation algorithms. The aim is to find the best PID parameters that minimise the specified objective function. Two trim conditions are investigated, i.e., hover and 10 m/s forward flight. The four algorithms performed better than manual tuning of the PID controllers. It was found, through numerical simulation, that the ACO algorithm converges the fastest and finds the best gains for the selected objective function in hover trim conditions. However, for 10 m/s forward flight trim, the GA algorithm was found to be the best. Both the tuned flight controllers managed to reject a gust wind of up to 5 m/s in the lateral axis in hover and in forward flight.

An Analysis on the Parametric Optimization of Electrochemical Honing Process

2020 ◽  
Vol 19 (02) ◽  
pp. 249-276
Author(s):  
Sunny Diyaley ◽  
Shankar Chakraborthy
Keyword(s):  
Removal Rate ◽  
Electrochemical Machining ◽  
Optimal Solution ◽  
Dimensional Accuracy ◽  
Cuckoo Search ◽  
Machining Process ◽  
De Algorithm ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Electrochemical honing (ECH) is a nontraditional machining process hybridizing the conjoint benefits of electrochemical machining (ECM) and mechanical honing actions. In this process, maximum amount of material is removed through anodic dissolution, followed by mechanical abrasion. In present day manufacturing industries, it has found wide ranging applications, mainly in finishing of varieties of gears, due to its various advantages, like increased material removal rate, long tool life, burr-free operation, achievement of higher surface finish and dimensional accuracy, generation of no residual stress, reduced noise, less material damage, etc. In order to achieve maximum machining capability from this process, it is always recommended to set its various input parameters at their optimal operating levels. In this paper, four powerful metaheuristic algorithms, i.e. firefly algorithm, differential evolution (DE) algorithm, cuckoo search (CS) algorithm and teaching–learning-based optimization (TLBO) algorithm are applied for single as well as multi-objective optimization of pulsed-ECH (PECH) and ECH processes. It is observed that TLBO algorithm supersedes other techniques in optimizing the two ECH processes with respect to the value of the derived optimal solution, consistency of the solutions and computational speed.

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