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Optimal Operation Of Multipurpose Multi Reservoir System Using Hybrid Artificial Bee Colony Algorithm And Its Performance Evaluation

Congress: 2015
Author(s): Ram Rimmalapudi, Janga Reddy Manne
Indian Institute of Technology Bombay1

Keyword(s): Sub-theme 10: Management of water resources,
Abstract

Introduction

Despite the development and growing use of optimization models over several years in the past for optimal reservoir operation,still there is a scope for their improvement of methods and solutions of reservoir planning and operation problems. Recently developed behavioral search techniques such as Artificial bee colony algorithm (ABC) and its variants have been receiving attention of various researchers. These techniques have capacity to effectively deal with complex real-world reservoir operation problems which may involve non continuous, non linear and non convex objective functions. This study adopts techniques like Artificial bee colony algorithm (ABC), Hybrid Artificial Bee Colony Algorithm (HABC) for solving reservoir operation problems, evaluates their performance by comparing with known solutions and Genetic Algorithm(GA) .

Methods

Hybrid Artificial Bee Colony algorithm (HABC) involves an additional phase of crossover operator of Genetic Algorithm to improve the canonical ABC for solving complex real world optimization problems. Simulated binary crossover is used as the crossover operator. The Artificial bee colony algorithm (ABC), Hybrid Artificial Bee Colony Algorithm(HABC) and Genetic Algorithm(GA) are applied for standard reservoir operation problems of four reservoir system and ten reservoir systems and sensitivity analysis is carried out for different parameters of the algorithm. Then, the methods are applied for real world case study of Chaliyar reservoir system in Kerala state, India to check whether the methodologies considered are effective in solving problems containing non linear functions..

Results and Discussions

It is inferred that the HABC algorithm performs better than ABC and GA, whereas ABC outperforms GA when checked on the bench mark problem of four reservoir system and ten reservoir system. ABC converged slowly to local optimum solutions in some cases but better results are obtained by HABC with faster convergence. For Chaliyar river system, the results obtained from all the three techniques is approximately same as the results obtained from LP except GA deviates in some of the time periods. However, HABC is found to be more accurate in finding the global optima for the given problem.

Conclusion

The study concludes that the HABC algorithm is very useful for solving large scale complex problems of multipurpose multi reservoir systems which may involve non-linear objective functions and constraints.

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