Introduction to artificial life
Introduction to artificial life
The ant colony optimization meta-heuristic
New ideas in optimization
Future Generation Computer Systems
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
A Method for Solving Optimization Problems in Continuous Space Using Ant Colony Algorithm
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization
Mathematics and Computers in Simulation
Ant colony algorithm for traffic signal timing optimization
Advances in Engineering Software
Layout and size optimization of sanitary sewer network using intelligent ants
Advances in Engineering Software
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This paper describes the application of the newly introduced Continuous Ant Colony Optimization Algorithm (CACOA) to optimal design of sewer networks. Two alternative approaches to implement the algorithm is presented and applied to a storm sewer network in which the nodal elevations of the network are considered as the decision variables of the optimization problem. In the first and unconstrained approach, a Gaussian probability density function is used to represent the pheromone concentration over the allowable range of each decision variable. The pheromone concentration function is used by each ant to randomly sample the nodal elevations of the trial networks. This method, however, will lead to solutions which may be infeasible regarding some or all of the constraints of the problem and in particular the minimum slope constraint. In the second and constrained approach, known value of the elevation at downstream node of a pipe is used to define new bounds on the elevation of the upstream node satisfying the explicit constraints on the pipe slopes. Two alternative formulations of the constrained algorithm are used to solve a test example and the results are presented and compared with those of unconstrained approach. The methods are shown to be very effective in locating the optimal solution and efficient in terms of the convergence characteristics of the resulting algorithms. The proposed algorithms are also found to be relatively insensitive to the initial colony and size of the colony used compared to the original algorithm.