Design of IoT-Based Improved Multimodal Ant Colony Optimızation (MM-ACO) Algorithm for Real-Time Applications
| Autor: | Kassahun Azezew, Mohammed Khalid Kaleem, Smegnew Asemie |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Ant colony optimization algorithms Real-time computing MathematicsofComputing_NUMERICALANALYSIS Python (programming language) ComputingMethodologies_ARTIFICIALINTELLIGENCE Swarm intelligence Traffic intensity Tree traversal Position (vector) Path (graph theory) Algorithm computer Intelligent transportation system computer.programming_language |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9789811651564 |
| DOI: | 10.1007/978-981-16-5157-1_15 |
| Popis: | In this paper, the design of IoT-based improved multimodal ant colony optimization (MM-ACO) algorithm for real-time applications is implemented. The main intent of multimodal ant colony optimization (MM-ACO) algorithm is to give good solution for finding an optimal traversal path. In this, first, data is initialized; next, map is loaded and destination is selected. By using IoT, the location is updated. Now, multimodal ant colony optimization (MM-ACO) algorithm will give transversal path using obtained data. By using this data, traffic intensity is calculated. After calculation of intensity, the position is updated globally. By using Python, the entire system is designed. Hence, from this, it can be observed that the accuracy and efficiency of this algorithm is high compared to ant colony optimization (ACO) algorithm. |
| Databáze: | OpenAIRE |
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