Ant colony optimization based binary search for efficient point pattern matching in images

Sreeja, N. K.; Ashok, S.

doi:10.1016/j.ejor.2015.04.012

Cited by 12 publications

(5 citation statements)

References 16 publications

(18 reference statements)

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“…The pheromone evaporates over time, and its concentration decreases with the path length. The ants are more likely to follow the trails that present the highest pheromone concentrations [59], [61].…”

Section: ) Ant-inspiredmentioning

confidence: 99%

“…Note that changing (or flipping) a bit is also called state transition which means that a state 0 becomes a state 1 or vice versa [57]- [59]. The initial concentration of pheromone is usually the same in all edges.…”

Section: ) Ant-inspiredmentioning

confidence: 99%

“…Jiang et al [75] have upgraded the convergence parameter (ã) as shown in (58) in order to improve the algorithm. Hu et al [76] also proposed changes in the a parameter as described in (59) to improve feature selection using BGWO.…”

Section: ) Wolf-inspiredmentioning

confidence: 99%

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Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

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Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents -swarm (e.g. fish, bird, and ant)directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarmbased techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach -binary decision variables and binary search space -is more efficient for binary optimisation in accuracy and computational cost.

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Section: ) Ant-inspiredmentioning

confidence: 99%

Section: ) Ant-inspiredmentioning

confidence: 99%

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Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

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“…To solve this problem, we propose a GA-based CAS (GA-CAS) method by synergising the merits of the GA and CAS algorithm. In addition, we compared simulation results with GA [24], genetic simulated annealing algorithm (GSA) [25], and ant colony optimisation (ACO) [26].…”

Section: Iet Communicationsmentioning

confidence: 99%

“…ACO is used to find the optimal path in a graph [26]. It is inspired by research on real ant foraging behaviour.…”

Section: Comparison Algorithmmentioning

confidence: 99%