GRD Journals | Global Research and Development Journal for Engineering | Emerging Research and Innovations in Civil Engineering (ERICE - 2019) | February 2019
e-ISSN: 2455-5703
Optimizing Reservoir Capacity, Water Allocation and Crop Yield using Teaching Learning Based Optimization (TLBO) Technique 1Vijendra
Kumar 2S. M. Yadav Research Scholar 2Professor 1,2 Department of Civil Engineering 1,2 Sardar Vallabhbhai National Institute and Technology, Surat, Gujarat, India 1
Abstract In the present study ‘Teaching Learning Based Optimization’ (TLBO) optimization method has been applied to the water resources engineering problem. TLBO is a population-based natural-inspired evolutionary algorithm comparatively simple, easy and robust. TLBO algorithm is capable of providing a global solution. Four water resources problem such as optimizing crop water demand, maximization of benefits, minimization of reservoir capacity and minimization of reservoir capacity with evaporation losses solved using TLBO technique. The results were compared with linear programming & dynamic programming solutions. TLBO algorithm has proven to be providing the global and better results. The results obtained from TLBO were better in reservoir capacity problem with evaporation losses. The results were satisfactory for optimizing crop water demand, maximization of benefits and minimization of reservoir capacity. The TLBO technique provides a satisfactory solution as other popular optimization techniques. Keyword- TLBO, LINGO Software, Soft Computing, Linear Programming, Dynamic Programming __________________________________________________________________________________________________
I. INTRODUCTION Soft computing techniques are the leading methods for solving water resources complex problems. The water resources problems are solved using fuzzy logic, artificial neural networks, machine learning, probabilistic reasoning, population-based algorithm and neighbourhood-based algorithm etc. Population-based algorithms are swarm intelligence and evolutionary computation. Evolutionary computations are evolutionary programming, differential evolution, genetic algorithm, genetic programming, evolutionary strategies, artificial immune algorithm and bacteria foraging optimization etc. Swarm intelligence based algorithms examples are artificial bee colony, particle swarm optimization, ant colony optimization, firefly etc. Neighbourhood-based algorithms are simulated annealing and tabu search (Venkata Rao 2016; Kumar and Yadav 2018). Various algorithms have been used to solve reservoir operation problem such as Fuzzy logic (Russell and Campbell 1996), genetic algorithm (Chang et al. 2010; Fallah-Mehdipour et al. 2012; Ashofteh et al. 2015), particle swarm optimization (Nagesh Kumar and Janga Reddy 2007; SaberChenari et al. 2016; Bai et al. 2017) and Firefly (Garousi-Nejad et al. 2016). Soft computing techniques have been applied for studying Rainfall-runoff model, artificial neural network (Nourani 2017), genetic algorithm (Wu et al. 2012), particle swarm optimization (Taormina et al. 2012), fuzzy logic (Talei et al. 2010) and genetic programming (Rodríguez-Vázquez et al. 2012). Stage forecasting and prediction have been studied using machine learning (Wu et al. 2008; Taghi Sattari et al. 2013), artificial neural network (Deo et al. 2000) and particle swarm optimization (Chau 2006). Flood forecasting and prediction have been studied using an artificial neural network (Maier and Dandy 2000; Pramanik and Panda 2009; Yazdani and Zolfaghari 2014; Wu et al. 2010), machine learning (Yu et al. 2006) and genetic algorithm (Sahay and Srivastava 2014). The main objective of the paper is to study the preformation of the recently developed algorithm i.e. teaching learningbased optimization. Four different problems such as optimizing crop water demand, maximization of benefits, minimization of reservoir capacity and minimization of reservoir capacity with evaporation losses have been solved using TLBO technique. Apart the manual working procedure has been also shown for better understating of the algorithm.
II. METHODOLOGY A. Teaching Learning Based Optimization (TLBO) TLBO mimics a class, based on the teaching-learning process. Just like in class, teachers teach and students learn. To obtain an excellent result, a good and strong dependency is required between students and the teachers. Thus, every student tries to mimic or follow the teachers and improve its result. Even in class students try to interact with other students and improve its own result.
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