2009
DOI: 10.1016/j.amc.2009.01.025
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A new solution algorithm for improving performance of ant colony optimization

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Cited by 40 publications
(21 citation statements)
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“…This process is repeated until stopping criteria is met. In this study, ACORSES algorithm proposed by Baskan et al (2009) to solve upper-level problem is used to tackle the optimization of signal timings with stochastic equilibrium link flows. The ACORSES algorithm is based on each ant searches only around the best solution of the previous iteration with reduced search space.…”
Section: Acorses Algorithm For Optimising Of Signal Timings (Upper-lementioning
confidence: 99%
See 1 more Smart Citation
“…This process is repeated until stopping criteria is met. In this study, ACORSES algorithm proposed by Baskan et al (2009) to solve upper-level problem is used to tackle the optimization of signal timings with stochastic equilibrium link flows. The ACORSES algorithm is based on each ant searches only around the best solution of the previous iteration with reduced search space.…”
Section: Acorses Algorithm For Optimising Of Signal Timings (Upper-lementioning
confidence: 99%
“…2a and 2b, where figures are adapted from Ceylan & Bell (2004 (Ceylan & Bell, 2004) www.intechopen.com (Ceylan & Bell, 2004) The ACORSES is performed with the following user-specified parameters. vector should be chosen according to constraints of cycle time as proposed by Baskan et al (2009). Colony size (R) is 50.…”
Section: Numerical Examplementioning
confidence: 99%
“…Optimization techniques range widely from the early gradient techniques 1 to the latest random techniques 16,18,19 including ant colony optimization 13,17 . Gradient techniques are very powerful when applied to smooth well-behaved objective functions, and especially, when applied to a monotonic function with a single optimum.…”
Section: Introductionmentioning
confidence: 99%
“…The path with more pheromone is selected with the high probability, he pheromone concentration rapidly decreases to increase the opportunity for choosing the other path, which is helpful to jump out the local optimum. At the same time, in order to prevent the low pheromone concentration of the global optimal path, in the first iteration, if there is no one ant to choose the global optimal path, the path is globally updated according the formula (14) in order to ensure that the ant will not deviate from the global optimal path. This method also has the adaptive function.…”
Section: The Supervisory Mechanismmentioning
confidence: 99%
“…Experiments show that the algorithm has a better performance than the other routing algorithm and establish the multicast tree quickly. Geng et al [14] proposed an improved solution algorithm using ant colony optimization (ACO) for finding global optimum for any given test functions. The proposed algorithm is based on each ant searches only around the best solution of the previous iteration with β. Zhang and Tang [15] proposed a novel hybrid ant colony optimization approach called SS_ACO algorithm to solve the vehicle routing problem.…”
Section: Introductionmentioning
confidence: 99%