Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems

Abstract

This paper extends the application of Constrained Ant Coiony Optimization Algorithms (CACOAs) to optimal operation of muiti-reservoir systems. Three different formuiations of the constrained Ant Colony Optimization (ACO) are outlined here using Max-Min Ant System for the solution of multireservoir operation problems. In the first two versions, called Partially Constrained ACO algorithms, the constraints of the muiti-reservoir operation problems are satisfied partiaiiy. In the third formulation, aii the constraints of the underlying probiem are implicitly satisfied by the provision of tabu iists to the ants which contain only feasibie options. The ants are, therefore, forced to construct feasible solutions and hence the method is referred to as a Fully Constrained ACO algorithm. The proposed constrained ACO algorithms are formuiated for both possible cases of taking storage/ release volumes as the decision variables of the problem. The proposed methods are used to optimally solve the well-known problems of four-and ten-reservoir operations and the results are presented and compared with those of the conventional unconstrained ACO algorithm and existing methods in the literature. The results indicate the superiority of the proposed methods over conventional ACOs and existing methods to optimally solve large scale multi-reservoir operation problems.

State of the Art Review of Ant Colony Optimization Applications in Water Resource Management

“…Partially constrained and fully constrained version of the proposed method were formulated and used to derive the optimal periods-of-record releases in a hydropower reservoir operation. The constrained ACO algorithm was extended in a more recent contribution to optimal operation of multiple reservoir system (Moeini and Afshar 2013a).…”

Arc based ant colony optimization algorithm for solving sewer network design optimization problem

“…Arc Based ACOA (ABACOA) formulation, is used to solve sewer network design optimization problem. The ABACOA was previously proposed for single and multi-reservoir operation problems by Moeini and Afshar [39,40]. The ABACOA has two signi cant advantages over the alternative point-based formulation as presented in the following.…”

Contrastive analysis of three parallel modes in multi-dimensional dynamic programming and its application in cascade reservoirs operation

“…Modern heuristic random search algorithms such as the Genetic Algorithm (GA) (Baskar et al, 2003;Chiang, 2007;Dariane and Momtahen, 2009), Particle Swarm Optimization (PSO) (Cai et al, 2001;Nagesh Kumar and Janga Reddy, 2007;Zhang et al, 2014), Ant Colony Optimization (ACO) (Zhou and Ji, 2007;Ji et al, 2011;Moeini and Afshar, 2013), Simulated Annealing (SA) (Basu, 2005), Evolutionary Programming (EP) (Basu, 2004;Malekmohammadi et al, 2009), Fuzzy Neural Network (FNN) (Chaves and Kojiri, 2007;Deka and Chandramouli, 2009) and Differential Evolution algorithm (DE) (Yuan et al, 2008;Yuan and Wu, 2012) have been extensively used to solve the CROO problems with nonlinear and non-convex objective functions. Many heuristic random search algorithms have been proved to possess a global convergence, while as they are affected by stochastic characteristics, they cannot guarantee a global optimum with finite iterations.…”