Ant Colony Optimization: Overview and Recent Advances

Dorigo, Marco; Stützle, Thomas

doi:10.1007/978-1-4419-1665-5_8

Cited by 350 publications

(193 citation statements)

References 97 publications

Supporting

Mentioning

170

Contrasting

Unclassified

Order By: Relevance

“…Ant Colony Optimization (ACO), proposed in [12] and explored in [78], has been inspired by the foraging behavior of real ants. Ants possess natural ability to find the shortest tour between the food source and their nest.…”

Section: B Ant Colony Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Investigating Performance of Various Natural Computing Algorithms

Chawda¹,

Patel²

2017

IJISA

View full text Add to dashboard Cite

Abstract-Nature is there since millenniums. Natural elements have withstood harsh complexities since years and have proved their efficiency in tackling them. This aspect has inspired many researchers to design algorithms based on phenomena in the natural world since the last couple of decades. Such algorithms are known as natural computing algorithms or nature inspired algorithms. These algorithms have established their ability to solve a large number of real-world complex problems by providing optimal solutions within the reasonable time duration. This paper presents an investigation by assessing the performance of some of the well-known natural computing algorithms with their variations. These algorithms include Genetic Algorithms, Ant Colony Optimization, River Formation Dynamics, Firefly Algorithm and Cuckoo Search. The Traveling Salesman Problem is used here as a test bed problem for performance evaluation of these algorithms. It is a kind of combinatorial optimization problem and known as one the most famous NP-Hard problems. It is simple and easy to understand, but at the same time, very difficult to find the optimal solution in a reasonable time -particularly with the increase in a number of cities. The source code for the above natural computing algorithms is developed in MATLAB R2015b and applied on several TSP instances given in TSPLIB library. Results obtained are analyzed based on various criteria such as tour length, required iterations, convergence time and quality of solutions. Conclusions derived from this analysis help to establish the superiority of Firefly Algorithms over the other algorithms in comparative terms.

show abstract

Section: B Ant Colony Optimizationmentioning

confidence: 99%

“…depositing more pheromone on better tours and vice versa. In Elitist ACO [78], an extra amount of pheromone is deposited on best tour to increase the chance of selecting that tour by ants in next iteration. The modified Elitist ACO, which is implemented here, deposits pheromone only on the best tour.…”

Section: B Ant Colony Optimizationmentioning

confidence: 99%

Investigating Performance of Various Natural Computing Algorithms

Chawda¹,

Patel²

2017

IJISA

View full text Add to dashboard Cite

show abstract

“…Nevertheless, evolutionary techniques as Artificial Bee Colony (ABC) [21] or Ant Colony Optimization (ACO) [22] algorithms are becoming popular. No evolutionary technique is better than the others, since each have drawbacks and advantages.…”

Section: Evolutionary Multi-objective Optimisationmentioning

confidence: 99%

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Reynoso-Meza

Blasco

Sanchis

et al. 2014

Control Engineering Practice

117

View full text Add to dashboard Cite

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach to calculate a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective searching for a set of potentially preferable solutions; the designer may then analyse the trade-off among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are commented on. Through this paper it is noticed that EMO research has been developing towards different optimization statements, but such statements are not commonly used on controller tuning. Therefore gaps between EMO research and EMO applications on controller tuning are detected and suggested as potential trends for research.

show abstract

“…ACO has also successfully been applied for many network applications [19][20][21][22]. It has been empirically shown to have favourable results compared to other routing protocols with respect to short path routing and reduced load balancing.…”

Section: Ant Colony Optimisation (Aco)mentioning

confidence: 99%

Escape planning in realistic fire scenarios with Ant Colony Optimisation

2014

View full text Add to dashboard Cite

An emergency requiring evacuation is a chaotic event, filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when predefined escape routes are blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape routes in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony Optimisation, suggests a near optimal escape plan for every affected personconsidering dynamic spread of fires, movability impairments caused by the hazards and faulty unreliable data. Special focus in this paper is on empirical tests for the proposed algorithms. This paper brings together the Ant Colony approach with a realistic fire dynamics simulator, and shows that the proposed solution is not only able to outperform comparable alternatives in static and dynamic environments, but also in environments with realistic spreading of fire and smoke causing fatalities. The aim of the solutions is usage by both individuals, such as from a personal smartphone of one of the evacuees, or for emergency personnel trying to assist large groups from remote locations.

show abstract

Ant Colony Optimization: Overview and Recent Advances

Cited by 350 publications

References 97 publications

Investigating Performance of Various Natural Computing Algorithms

Investigating Performance of Various Natural Computing Algorithms

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Escape planning in realistic fire scenarios with Ant Colony Optimisation

Contact Info

Product

Resources

About