Disadvantages Of Linear Programming

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Discussion- Optimization in operation of reservoir is one of the important activities in the field of reservoir operation that aim at an effective and efficient utilization of water with maximum benefits. Various algorithms have been applied to optimize the Reservoir operation and maximize the net benefit, but they have their own Advantages and Disadvantages. The conventional Linear Programming has been used mostly for the planning and design problems of single reservoir systems. Natural processes are rarely linear and solving the problem by Linear Programming forces approximations, which may lead to either, approximate or sometimes even to unrealistic solutions. In addition, Linear Programming yields only point solutions in the policy…show more content…
Its use is practically restricted to single reservoirs due to the "curse of dimensionality". In some cases, Deterministic Dynamic Programming has been applied to a system of three to four reservoirs but was found computationally inefficient. The use of Stochastic Dynamic Programming is restricted to single reservoirs only because of its requirement of discretization of state and space, the computer storage requirement increases exponentially with the increase in the number of states (reservoirs). In Artificial Neural Network, the neural networks need training to operate. The modeling results converge to a local minimum. Generalization and over fitting renders inaccuracy in some cases. The model provides results, which are hard to interpret occasionally. Ant Colony Optimization too has some disadvantages like the theoretical analysis is difficult, sequences of random decisions are present which are not independent, probability distribution changes with iteration, research is experimental rather than theoretical, and time for convergence is uncertain although convergence is…show more content…
Even though PSO is efficient, it also has some critical problems such as premature convergence and easily drops into regional optimum. It shares many common points with Genetic Algorithm (GA). They start with random based population and have fitness values to evaluate the population. Both update the population and search for the optimum with random techniques. Both systems do not guarantee success. Differential Evolution is an improved version of Genetic Algorithms. It relies on mutation rather than Crossover. Differential Evolution algorithm has advantages over Genetic algorithm as it can search randomly and it requires fewer parameters setting. It has high performance and it is applicable to high-dimensional complex optimization problems. But similar to PSO, DE has several drawbacks including unstable convergence in the last period and easy to drop into regional
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