Journal ArticleDOI
Evacuation path optimization based on quantum ant colony algorithm
TLDR
Inspired by the promising performance of heuristic algorithms to solve combinatorial problems, an improved quantum ant colony algorithm (QACA) is proposed for exhaustive optimization of the evacuation path that people can evacuate from hazardous areas to safe areas.About:
This article is published in Advanced Engineering Informatics.The article was published on 2016-08-01. It has received 69 citations till now. The article focuses on the topics: Metaheuristic & Ant colony optimization algorithms.read more
Citations
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Journal ArticleDOI
A best-path-updating information-guided ant colony optimization algorithm
TL;DR: A novel strengthened pheromone update mechanism is designed that strengthens the phersomone on the edges, which had never been done before, utilizing dynamic information to perform path optimization.
Journal ArticleDOI
Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm
TL;DR: The purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments using an improved quantum ant colony algorithm.
Journal ArticleDOI
Enhanced Beetle Antennae Search with Zeroing Neural Network for online solution of constrained optimization
TL;DR: In this paper, the authors proposed a continuous-time enhanced variant of Beetle Antennae Search (BAS), a metaheuristic algorithm that mimics the food searching nature of beetles.
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Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead
TL;DR: This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS), complemented by an initiatory taxonomic introduction to bioinspired computational intelligence.
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Promoting low carbon agenda in the urban logistics network distribution system
TL;DR: A case study is used to validate the feasibility and practicality of the proposed green logistics distribution model to help end-users optimize their daily operations and show that there is a negative correlation between the cost and carbon emissions under the shortest distribution routes.
References
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Journal ArticleDOI
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
Kuk-Hyun Han,Jong-Hwan Kim +1 more
TL;DR: The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm, and a Q-gate is introduced as a variation operator to drive the individuals toward better solutions.
Journal ArticleDOI
Ant colony optimization for continuous domains
Krzysztof Socha,Marco Dorigo +1 more
TL;DR: This paper shows how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure, and compares the results with those reported in the literature for other continuous optimization methods.
Journal ArticleDOI
A network flow model for lane-based evacuation routing
Thomas J. Cova,Justin P. Johnson +1 more
TL;DR: A network flow model for identifying optimal lane-based evacuation routing plans in a complex road network is presented, an integer extension of the minimum-cost flow problem that can be used to generate routing plans that trade total vehicle travel-distance against merging, while preventing traffic crossing-conflicts at intersections.
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Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub /spl epsi// gate, and two-phase scheme
Kuk-Hyun Han,Jong-Hwan Kim +1 more
TL;DR: The results show that the updated QEA makes QEA more powerful than the previous QEA in terms of convergence speed, fitness, and robustness.
Journal ArticleDOI
Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions
TL;DR: An extensive survey of protocols developed according to the principles of swarm intelligence, taking inspiration from the foraging behaviors of ant and bee colonies, and introduces a novel taxonomy for routing protocols in wireless sensor networks.