In this paper, ant colony optimization (ACO) algorithms are proposed for reservoir operation. Through a collection of cooperative agents called ants, the near optimum solution to the reservoir operation can be effectively achieved. To apply ACO algorithms, the problem is approached by considering a finite horizon with a time series of inflow, classifying the reservoir volume to several intervals, and deciding for releases at each period with respect to a predefined optimality criterion. Three alternative formulations of ACO algorithms for a reservoir operation are presented using a single reservoir, deterministic, finite-horizon problem and applied to the Dez reservoir in Iran. It is concluded that the ant colony system global-best algorithm provides better and more comparable results with known global optimum results. As with any direct search method, the model is quite sensitive to setup parameters, hence fine tuning of the parameters is recommended.

This article presents a heuristic particle swarm ant colony optimization algorithm to solve engineering optimization problems. Although PSO has simple principle and ease to be implemented and can eventually locate the desired solution, however, its practical use in solving engineering optimization problems is severely limited by the high computational cost of the slow convergence rate. Here, ant colony and harmony search principles are employed to speed up local search and improve precision of the solutions. A modified feasible-based mechanism is described which handles the problem-specific constraints.Benchmark optimization problems are used to illustrate the reliability of the proposed algorithm.

Most real world engineering design problems, such as cross-country water mains, include combinations of continuous, discrete, and binary value decision variables. Very often, the binary decision variables associate with the presence and/or absence of some nominated alternatives or project’s components. This study extends an existing continuous Ant Colony Optimization (ACO) algorithm to simultaneously handle mixed-variable problems. The approach provides simultaneous solution to a binary value problem with both discrete and continuous variables to locate and size design components of the proposed system. This paper shows how the existing continuous ACO algorithm may be revised to cope with mixed-variable search spaces with binary variables. Performance of the proposed version of the ACO is tested on a set of mathematical benchmark problems followed by a highly nonlinear forced water main optimization problem. Comparing with few other optimization algorithms, the proposed optimization method demonstrates satisfactory performance in locating good near optimal solutions.