The ant colony optimization meta-heuristic
New ideas in optimization
Future Generation Computer Systems
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. Ant colony foraging behavior may also be considered as a typical swarm-based approach to optimization. In this paper, ant colony optimization algorithm (ACO) is presented and tested with few benchmark examples. To test the performance of the algorithm, three benchmarks constrained and/or unconstrained real valued mathematical models were selected. The first example is the Ackley's function which is a continuous and multimodal test function obtained by modulating an exponential function with a cosine wave of moderate amplitude. The algorithm application resulted in the global optimal with reasonable CPU time. To show the efficiency of the algorithm in constraint handling, the model was applied to a two-variable, two constraint highly nonlinear problem. It was shown that the performance of the model is quite comparable with the results of well developed GA. The third example is a real world water resources operation optimization problem. The developed model was applied to a single reservoir with 60 periods with objective of minimizing the total square deviation from target demand. Results obtained are quit promising and compares well with the results of some other well-known heuristic approaches.