Escape planning in realistic fire scenarios with Ant Colony Optimisation

Goodwin, Morten; Granmo, Ole‐Christoffer; Radianti, Jaziar

doi:10.1007/s10489-014-0538-9

Cited by 31 publications

(10 citation statements)

References 23 publications

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“…ACO has later found increased popularity due to its low complexity and ability to work in dynamic environments. The flexibility of ACO is apparent as it has been shown to be used in a wide variety of problems [54] such as solving NP hard problems [56], traffic optimisation [57], evacuation path computation in emergency situations [58,59], classification with neural networks, [60] Bayesian classifiers [61], rule based classification [62] and routing in communication networks.…”

Section: Aco For Classificationmentioning

confidence: 99%

Distributed learning automata-based scheme for classification using novel pursuit scheme

Goodwin

Yazidi

2020

Appl Intell

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Learning Automata (LA) is a popular decision making mechanism to "determine the optimal action out of a set of allowable actions" [1]. The distinguishing characteristic of automata-based learning is that the search for the optimising parameter vector is conducted in the space of probability distributions defined over the parameter space, rather than in the parameter space itself [2]. Recently, Goodwin and Yazidi pioneered the use of Ant Colony Optimisation (ACO) for solving classification problems [3]. In this paper, we propose a novel classifier based on the theory of LA. The classification problem is formulated as a deterministic optimization problem involving a team of LA that operate collectively to optimize an objective function.Although many LA algorithms have been devised in the literature, those LA schemes are not able to solve deterministic optimization problems as they suppose that the environment is stochastic. In this paper, we develop a novel pursuit LA which can be seen as the counterpart of the family of pursuit LA developed for stochastic environments [4]. While classical pursuit LA are able to pursue the action with the highest reward estimate, our pursuit LA rather pursues the collection of actions that yield the highest performance. The theoretical analysis of the pursuit scheme does not follow classical LA proofs and can pave the way towards more schemes where LA can be applied to solve deterministic optimization problems.When applied to classification, the essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a

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Section: Aco For Classificationmentioning

confidence: 99%

Distributed learning automata-based scheme for classification using novel pursuit scheme

Goodwin

Yazidi

2020

Appl Intell

Self Cite

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“…To design heuristic algorithms for the dynamic problem model, researchers in [21,22] designed simulated evolutionary algorithms. They took full advantages of the positive feedback information mechanism of ant colony algorithm and the fast convergence of the genetic algorithm [23].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Route Network Planning Problem for Emergency Evacuation in Restricted-Space Scenarios

Hong

et al. 2018

Journal of Advanced Transportation

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We investigate a dynamic route planning problem in restricted-space evacuation, namely, the Multiobjective Dynamic Route Network Planning (MODRNP) problem. It models the multisource to multidestination evacuation in restricted-space scenarios, with the objectives of minimizing the whole evacuation delay and maximizing the evacuation efficiency. We study the problem in 3D scenarios, which can provide intuition vision for the geographic space and contribute to the evacuation plan and implementation. Based on the auxiliary graph transformation, we propose a heuristic algorithm referred to the classical problem, Minimum Weighted Set Cover. We finally conduct extensive experiments to evaluate the performance of the proposed algorithm and give an application instance on a typical kind of restricted-space scenarios. The results indicate that the proposed algorithm outperforms the existing alternatives in terms of the utilization as well as timeliness.

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“…In order to find the evacuation path in a short time, some methods [12] simplified the route topology to reduce the computation time, and the incomplete dynamic route topology made the new path different from the shortest one. With the development of stochastic optimization, some intelligent algorithms such as ant colony algorithm [13][14][15], particle swarm algorithm [16][17][18], bee colony algorithm [19][20][21], and genetic algorithm [22] or greedy algorithm [23] were used to find the dynamic path. When route topography and constraints changed, intelligent algorithms took the old path as a feasible solution for dynamic path searching.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Path Optimization with Real-Time Information for Emergency Evacuation

Zhang

Zhao

Cheng

et al. 2021

Mathematical Problems in Engineering

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In order to find the optimal path for emergency evacuation, this paper proposes a dynamic path optimization algorithm based on real-time information to search the optimal path and it takes fire accident as an example to introduce the algorithm principle. Before the accidents, it uses the Dijkstra algorithm to get the prior evacuation network which includes evacuation paths from each node to the exit port. When the accidents occur, the evacuees are unable to pass through the passage where the accident point and the blocking point are located, then the proposed method uses the breadth-first search strategy to solve the path optimization problem based on the prior evacuation network, and it dynamically updates the evacuation path according to the real-time information. Because the prior evacuation network includes global optimal evacuation paths from each node to the exit port, the breadth-first search algorithm only searches local optimal paths to avoid the blockage node or dangerous area. Because the online optimization solves a local pathfinding problem and the entire topology optimization is an offline calculation, the proposed method can find the optimal path in a short time when the accident situation changes. The simulation tests the performances of the proposed algorithm with different situations based on the topology of a building, and the results show that the proposed algorithm is effective to get the optimal path in a short time when it faces changes caused by the factors such as evacuee size, people distribution, blockage location, and accident points.

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Escape planning in realistic fire scenarios with Ant Colony Optimisation

Cited by 31 publications

References 23 publications

Distributed learning automata-based scheme for classification using novel pursuit scheme

Distributed learning automata-based scheme for classification using novel pursuit scheme

Dynamic Route Network Planning Problem for Emergency Evacuation in Restricted-Space Scenarios

Dynamic Path Optimization with Real-Time Information for Emergency Evacuation

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