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A Review on the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends
 Mathware & Soft Computing
, 2002
"... Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO ..."
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Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the eld and we conclude by showing several new trends and new research directions in this eld.
Optimal contraction theorem for explorationexploitation tradeoff in search and optimization
 IEEE Trans. Syst., Man, Cybern. A, Syst. Humans
, 2009
"... Abstract—Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and converge ..."
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Abstract—Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and convergence speed of optimization algorithms. In this paper, definitions of exploration and exploitation are first given based on information correlation among samplings. Then, some general indicators of optimization hardness are presented to characterize problem difficulties. By analyzing a typical contractionbased threestage optimization process, Optimal Contraction Theorem is presented to show that T:Er&Ei depends on the optimization hardness of problems to be optimized. T:Er&Ei will gradually lean toward exploration as optimization hardness increases. In the case of great optimization hardness, explorationdominated optimizers outperform exploitationdominated optimizers. In particular, random sampling will become an outstanding optimizer when optimization hardness reaches a certain degree. Besides, the optimal number of contraction stages increases with optimization hardness. In an optimal contraction way, the whole sampling cost is evenly distributed in all contraction stages, and each contraction takes the same contracting ratio. Furthermore, the characterization of optimization hardness is discussed in detail. The experiments with several typical global optimization algorithms used to optimize three groups of test problems validate the correctness of the conclusions made by T:Er&Ei analysis. Index Terms—Exploitation, exploration, global optimization, optimal contraction theorem, optimization hardness. I.
An agent based evolutionary approach to path detection for offroad vehicle guidance
 Pattern Recognition Letters
, 2006
"... This paper describes an ant colony optimization approach adopted to decide on roadborders to automatically guide a vehicle developed for the DARPA Grand Challenge 2004. Due to the complexity of offroad trails and different natural boundaries of the trails, lane markers detection schemes cannot wo ..."
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This paper describes an ant colony optimization approach adopted to decide on roadborders to automatically guide a vehicle developed for the DARPA Grand Challenge 2004. Due to the complexity of offroad trails and different natural boundaries of the trails, lane markers detection schemes cannot work. Hence border detection is based on Ant Colony Optimization technique. Two borders at two sides of the road (as seen by a camera fixed on the vehicle) are tracked by two agent colonies: agents ’ moves are inspired by the behaviors of biological ants when trying to find the shortest path from nest to food. Reinforcement learning is done by pheromone updating based on some heuristic function and by changing the heuristic balancing parameters with the experience over the last tracked results. Shadow removal has also been introduced to increase robustness. Results on different offroad environments, as provided in the DARPA Grand Challenge 2004, have been shown in the form of correct detections, false positives and false negatives and their dependence on number of antagents and balancing edge exploitation and pheromoneexploitation. Key words: Ant Colony Optimization, automatic offroad vehicle guidance, evolutionary algorithms, intelligent vehicles. 1
Applying Extremal Optimisation To Dynamic Optimisation Problems
"... Extremal Optimisation is a recently conceived addition to the range of stochastic solvers. Due to its deliberate lack of convergence behaviour, it can be expected to solve dynamic problems without having to be informed when a change occurs. Moreover, the severity of change does not seriously affect ..."
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Extremal Optimisation is a recently conceived addition to the range of stochastic solvers. Due to its deliberate lack of convergence behaviour, it can be expected to solve dynamic problems without having to be informed when a change occurs. Moreover, the severity of change does not seriously affect algorithm performance, allowing for unpredictable fluctuations without affecting the outcome. Experimental studies on three example problems confirmed this assumption but also raised some issues concerning the interaction of solver mechanisms with problem intricacies, a phenomenon shared by many algorithm implementations. Many of the problems used as benchmarks had not been solved with EO before. While EO is a very lightweight stochastic process, it is at its most effective when the choice of next move is modelled skilfully according to the problem characteristics. Some insights into effective neighbourhood modelling were revealed during the experimentations.
A Bioinspired Method for Distributed Deployment of Services
, 2011
"... Abstract We look at the wellknown problem of allocating software components to compute resources (nodes) in a network, given resource constraints on the infrastructure and the quality of service requirements of the components to be allocated to nodes. This problem has many twists and angles, and h ..."
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Abstract We look at the wellknown problem of allocating software components to compute resources (nodes) in a network, given resource constraints on the infrastructure and the quality of service requirements of the components to be allocated to nodes. This problem has many twists and angles, and has been studied extensively in the literature. Solving it is particularly problematic when there is extensive dynamism and scale involved. Typically, heuristics are needed. In this paper, we present a new breed of heuristics for solving this problem. The distinguishing feature of our approach is a decentralized optimization framework aimed at finding near optimal mappings within reasonable time and for large scale. Three different incarnations of the problem are explored through simulations. For one problem instance, we also provide exact solutions, and show that our technique is able to find near optimal solutions with low variance. In the largest example, a publicprivate cloud computing scenario is used, where different clouds are associated with financial costs, and we show that our approach is capable of balancing the load as expected for such a scenario.
Ant Colony Optimisation and the Traveling Salesperson Problem – Hardness, Features and Parameter Settings [Extended Abstract]
"... Our study on ant colony optimization (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance. Categories and Subject ..."
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Our study on ant colony optimization (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance. Categories and Subject Descriptors D.2.8 [Software Engineering]: Metrics—complexity measures,
Omicron ACO. A new ant colony optimization algorithm
"... Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel populationbased ACO ..."
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Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel populationbased ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two wellknown TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.
A running time analysis of an Ant ColonyOptimization algorithm for shortest paths in directed acyclic graphs
"... In this paper, we prove polynomial running time bounds for an Ant Colony Optimization (ACO) algorithm for the singledestination shortest path problem on directed acyclic graphs. More specifically, we show that the expected number of iterations required for an ACObased algorithm with n ants is O ( ..."
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In this paper, we prove polynomial running time bounds for an Ant Colony Optimization (ACO) algorithm for the singledestination shortest path problem on directed acyclic graphs. More specifically, we show that the expected number of iterations required for an ACObased algorithm with n ants is O ( 1 ρ n2m logn) for graphs with n nodes and m edges, where ρ is an evaporation rate. This result can be modified to show that an ACObased algorithm for OneMax with multiple ants converges in expected O ( 1 ρ n2 logn) iterations, where n is the number of variables. This result stands in sharp contrast with that of Neumann and Witt, where a singleant algorithm is shown to require an exponential running time if ρ = O(n−1−) for any > 0.
Development of ACO Algorithm for Service Restoration in Distribution system
"... restoration in power distribution system involves operating the line switches to restore as many loads as possible for the areas isolated by a fault. In case of partial restoration, the supply must be restored to highest priority customers (e.g. hospitals) and this fact should be reflected in the fi ..."
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restoration in power distribution system involves operating the line switches to restore as many loads as possible for the areas isolated by a fault. In case of partial restoration, the supply must be restored to highest priority customers (e.g. hospitals) and this fact should be reflected in the final solution of service restoration problem. Thus service restoration problem is formulated as multi objective, multi constraint combinatorial optimization problem. This paper proposes an Ant Colony Optimization (ACO) algorithm for a minimization problem of energy not supplied during restoration process. The proposed ACO algorithm is a new technique for combinatorial optimization borrowed from swarm intelligence. The operating time of manually controlled and automatically controlled switches is significantly different. Therefore both types of switches are considered separately.