Multireservoir System Optimization Using Multi-Objective Genetic Algorithms

Abstract

Multireservoir system consists of several reservoirs which are connected serially or parallel in the same basin. To optimize such a complex multireservoir system, the dynamic programming (DP), linear programming (LP) and non-linear programming (NLP) have been widely applied to real problems. However, when DP is applied to multireservoir system it has a major problem, so called `the curse of dimensionality' and LP and NLP have essential approximation problems dealing with discontinuous, nondifferentiable, non-convex, or multi-modal objective functions. Recently, there has been an increasing interest in a biologically motivated adaptive system for solving optimization problems. The genetic algorithms (GAs) are one of the most promising techniques in natural adaptive system field and receiving many attentions because of their flexibility and effectiveness for optimizing complex systems. Optimization of multireservoir system is to solve multi-dimensional and multi-objective problems and GAs are appropriate optimization methods to multireservoir system. GAs are not restricted by a number of dimensions because computer memory increases by dimensions linearly, not exponentially. Thus, there is no `curse of dimensionality'. Especially classical optimization methods such as DP, LP, and NLP are not proper to multi-objective optimization because these methods use a point-by-point approach, in which the outcome of classical optimization methods is a single optimal solution. However, GAs use a population of solutions in each iteration instead of a single solution, so they are called as population-based approaches. This is one of the most striking differences between classical optimization methods and GAs.