New developments gave better results, like ant colony system acs 2, the maxmin ant system mmas 7, the rankbased ant system as rank 8, among others. A statistical analysis of parameter values for the rankbased. The pheromonebased communication of biological ants is often the predominant paradigm used. Ant colony system acs 45 later in 1996, while stutzle and hoos proposed the maxmin ant system mmas 3. The algorithm tries to reproduce the behavior of the ants in the search process of solutions from the choice of the path to be followed until the process of updating the pheromone trail.
Rankbased ant colony algorithm for a thermal generator. The core architecture of the ant colony optimization algorithm is based on as 14, which is the first proposed optimization model of aco, and has been successfully applied in the traveling salesman problem. In this paper, we focus on a particular type of the aco algorithm, namely. Ant colony optimization for the traveling salesman problem based on ants with memory. In rankbased version of as rankbased as 15, the concept of ranking was applied and extended as follows. If the inline pdf is not rendering correctly, you can. Face recognition system using ant colony optimization. Face recognition system using ant colony optimizationbased. In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. This study proposes a rank based ant colony optimization aco method with a rank based nonlinear selective pressure function and a modified qlearning method to enhance the convergence characteristics of original aco dorigo et al. If q q0, then, among the feasible components, the component that maximizes the product. Some early applications include the elitist strategy for ant system eas 15, 18, rank based version of ant system asrank 6, maxmin ant system mmas 35, ant colony system acs 17, and multiple ant colony system macs 22. An efficient gpu implementation of ant colony optimization.
Ant system as was the first aco algorithm presented for shortest paths 1998. Part 4 the traveling salesman problem the reasons for the choice of the tsp. Strategy in ant colony optimization in fact, in general, aco algorithm, the initial position of agents assigned one agent in an each node at randomly. Ant colony optimization aco is a populationbased, general search technique for the solution of dif. Parallel ant colony optimization using cuda the ant colony optimization aco algorithm is a metaheuristic that is used to find shortest paths in graphs. The ant colony optimization metaheuristic is adopted from the natural foraging behavior of real ants and has been used to. This algorithm is a member of the ant colony algorithms family. The first algorithm which can be classified within this framework was presented in 1991 21, and, since then, many diverse variants of the basic principle have been reported in the literature. Introduction in computer science and operation research, the ant colony optimization algorithmaco is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Perlovsky abstract ant colony optimization is a technique for optimization that was introduced in the early 1990s. In 12 the authors proposed a rankbased ant system planar and spatial trusses. Experiments with argentine ants the ants prefer the shortest path from the nest to the food source 1991. Isbn 9789533071572, pdf isbn 9789535159803, published 20110204. Reactive power optimization in power system based on improved multiagent ant colony optimization.
Some of the variations include maxmin, elitist, ant system, and rank based system. This probability distribution of the random proportional. We present a hardware implementation of populationbased ant colony optimization paco on. The ant colony optimization algorithm aco mimics the behavior of real ant colonies. A statistical analysis of parameter values for the rank. Artificial ants stand for multiagent methods inspired by the behavior of real ants. Ant colony optimization formulations for dynamic topology problems 3. Monirul islam a, nonmembers, and kazuyuki murase, member summary ant colony optimization aco algorithms are a new branch of swarm intelligence. Ant colony optimization approach to portfolio optimization. Because of the structure of the aco algorithm it is amenable to being parallelized. Ant colony optimization for the traveling salesman problem based on ants with memory bifan li1, lipo wang1,2, and wu song3 1 college of information engineering, xiangtan university, xiangtan, hunan, china. Dorigo and colleagues as a novel natureinspired metaheuristic for the solution of hard combinatorial.
The ant colony optimization algorithm aco, introduced by marco dorigo, in the year 1992 and it is a paradigm for designing meta heuristic algorithms for optimization problems and is inspired by the foraging behavior of ant colonies. Case of study with tsp and qap, expert systems with applications, vol. Note that other variants of the initial ant system have been proposed as well, such as ant colony system 15, rankbased ant system 16, and elitist ant system 12. Aco has drawn much research attention and various extended versions of the aco paradigm were proposed, such as the bestworst ant system bwas 8, the rank based ant system ras 7 etc. Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm.
Ants can find the shortest path from a food source to their nest by exploiting a chemical substance called pheromone. Part 3 ant system in the antcycle version the pheromone update was only done after all the ants had constructed the tours and the amount of pheromone deposited by each ant was set to be a function of the tour quality. The expression was introduced by gerardo beni and jing wang in 1989. An ant colony optimization based on information entropy. An ant colony optimization algorit hm based on the experience model wenjun pan1, and lipo wang2 1 college of information engineering, xiangtan university, xiangtan, hunan, china. This study proposes a rankbased ant colony optimization aco method with a rankbased nonlinear selective pressure function and a modified qlearning method to enhance the convergence characteristics of original aco dorigo et al. A regular clustering based sna algorithm can then be applied on this reduced graph. In aco, a set of software agents called artificial ants search for good solutions to a given optimization problem. Part 4 outline the traveling salesman problem aco algorithms for tsp ant system as elitist ant system eas rank based ant system rankas maxmin ant system mmas ant colony system acs search stagnation experimental evaluation aco plus local search references.
Introduction one of the vital problems in finance is portfolio optimization problem pop that has received a lot of attention in recent decades. The ant colony optimization aco algorithm was inspired through. Some early applications include the elitist strategy for ant system eas 15, 18, rankbased version of ant system asrank 6, maxmin ant system mmas 35, ant colony system. Selfevolving ant colony optimization and its application to traveling salesman problem xiaofan zhou and ronglong wang graduate school of engineering university of fukui. In rank based ant colony optimization that, only the best ants are considered. Ant colony system is an extension to the ant system algorithm and is related to other ant colony optimization methods such as elite ant system, and rankbased ant system. Rank based ant colony optimization is a new meta heuristic technique inspired by the nature of the real ants and helps in finding the optimal solution of the problems. Ant colony optimization and its application to adaptive. Ant colony optimization methods and applications intechopen. The first algorithm which can be classified within this framework was presented in 1991 21, and, since then.
The original ant colony optimization algorithm is known as ant system 68 and was proposed in the early nineties. Ant colony optimization aco is a metaheuristic for solving combinatorial optimization problems that is based on the foraging behavior of biological ant colonies. Ant colony optimization aco 17, which was inspired by the observation of ant colonies. Different ant colony optimization algorithms have been proposed. Ant colony optimization aco is an efficient algorithm for solving csps. In this paper, we introduce rankbased ant system algorithm 15, an imported version of basic ant system 16 of the family algorithms.
In this paper, we focus on a particular type of the aco algorithm, namely, the. To improve the solution quality of aco for solving csps, an ant colony optimization based. A number of best ants of the current iteration are allowed. Ant colony optimization and its application to the vehicle.
Ant colony optimization is the name given by dorigo. Fpga implementation of populationbased ant colony optimization. Ant colony optimization 01 iran university of science. See table 1 for a nonexhaustive list of successful variants. Optimization of traveling salesman problem using affinity. Ant colony optimization aco in the early 1990s, ant colony optimization aco was introduced by m. To apply aco, the optimization problem is transformed into the problem of finding. Ant colony optimization for the traveling salesman problem. A stochastic rankbased ant system for discrete structural.
Haco employs the multigaussian pdfs sampling in parallel and learns the next generations mean and variance values dynamically by pbilc. Automatic test paper generation based on ant colony. Automatic test paper generation based on ant colony algorithm. Ant colony system aco ant colony system aco ant colony system ants in acs use thepseudorandom proportional rule probability for an ant to move from city i to city j depends on a random variable q uniformly distributed over 0. The wilcoxon signedrank test, compare two sets of return 295. Nowadays, when referring to as, one actually refers to ant cycle since the two other variants were.
The metaphor of the ant colony and its application to combinatorial optimization based on theoretical biology work of jeanlouis deneubourg 1987 from individual to collective behavior in social insects. A detailed overview of these variants can be found in 8. A hybrid ant colony optimization for continuous domains. Other famous fs approaches are based on the genetic algorithm ga 10, simulated annealing 2 and ant colony optimization aco 2, 5. The thermal generator maintenance scheduling problem is a complex multivariable problem that is necessary. Part 1 ant colony optimization dynamic problems are defined as a function of some quantities whose value is set by the dynamics of an underlying system. Rankbased ant colony optimization applied to dynamic. Markowitz was the first to come up with a parametric optimization. Portfolio optimization, ant colony optimization aco, multiobjective 1. Rank based solution has been used in ant colony algorithms for single objective optimization by bullnheimer, hartl, and strau.
Ant colony optimization 03 iran university of science and. Solving the constraint satisfaction problem csp is to find an assignment of values to variables that satisfies a set of constraints. Ant colony optimization aco is a population based, general search technique for. The problem instance changes therefore at run time and the optimization algorithm must be capable of adapting online to the changing environment. As rank can nd better solutions and nd these solutions for a shorter period. Solid waste routing by rank based ant colony optimization. Introduced by marco dorigo in his phd thesis 1992 and initially applied to the travelling salesman problem, the aco field. Ant colony optimization techniques and applications. A new local search based ant colony optimization algorithm.
By doing this, we aim to overcome the performance loss of sna methods on largescale networks. Ant colony algorithm is a kind of colony intelligence searching method, and is equipped with positive feedback paralleling mechanism, with strong searching capability, enabling it to be appropriate for the solution of automatic test paper generation, especially binary ant colony algorithm, which enables ant to only select between 0 and. Part 1 ant colony optimization examples of aco algorithms ant system as elitist ant system eas rankbased ant system asrank min max ant system mmas ant colony system acs approximate nondeterministic tree search ants hypercube framework. The metaphor of the ant colony and its application to combinatorial optimization based on theoretical biology work of jeanlouis deneubourg. New developments gave better results, like ant colony system acs 2, the maxmin ant system mmas 7, the rank based ant system as rank 8, among others. Ant colony optimization aco is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Aco has been applied to a variety of different problems 8, such as vehicle routing 1719, scheduling 20, 21. But, in this paper, we propose a new improved bioinspired ant colony algorithm using the backtracing strategy of current global path and diversification strategy. The ant colony optimization algorithm aco, introduced by marco dorigo, in the year 1992 and it is a paradigm for designing meta heuristic algorithms for optimization problems and is inspired by.
Standard aco applied to dynamic topology optimization. A modified ant colony optimization algorithm for dynamic. Pdf a statistical analysis of parameter values for the. Oct 21, 2011 ant colony optimization aco is a population based metaheuristic that can be used to find approximate solutions to difficult optimization problems. In computer science and operations research, the ant colony optimization algorithm aco is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Starting with the 1996 seminal paper by dorigo, maniezzo and colorni, aco techniques have been used to solve the traveling salesperson problem tsp. Ant colony optimization aco is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. Part 3 ant system in the ant cycle version the pheromone update was only done after all the ants had constructed the tours and the amount of pheromone deposited by each ant was set to be a function of the tour quality.
Nowadays, when referring to as, one actually refers to antcycle since the two other variants were. Ant colony optimization aco, one of the most commonly used swarm intelligence techniques in literature, is based on the behavior of real ants. Inspiration the ant colony system algorithm is inspired by the foraging behavior of ants, specifically the pheromone communication between ants regarding a good path between. By using cuda to implement a aco algorithm, we achieved significant improvement in performance over a. Rankbased ant colony optimization applied to dynamic traveling salesman problems, engineering optimization, vol. Reactive power optimization in power system based on. The idea of aco is based on the behavior of real ants exploring a path between their colony and a source of food. Ant colony optimization aco was introduced as a natureinspired metaheuristic for the solution of combinatorial optimization problems 4, 5.
In this paper, we introduce rank based ant system algorithm 15, an imported version of basic ant system 16 of the family algorithms. But, in this paper, we propose a new improved bioinspired ant colony algorithm using the backtracing strategy of. Jul 09, 2018 dorigo 1 introduces an ant based algorithm called ant colony optimization aco. Ant colony optimization algorithm using backtracing and. Since then, a number of other aco algorithms were introduced. Ant colony system is an extension to the ant system algorithm and is related to other ant colony optimization methods such as elite ant system, and rank based ant system. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph. A new local search based ant colony optimization algorithm for solving combinatorial optimization problems md. It is important to note that the solution qualities for the cuda. Ant colony optimization 03 iran university of science. Read online ant colony optimization and its application to adaptive ant colony optimization and its in computer science and operations research, the ant colony optimization algorithm aco is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant colony optimization 01 iran university of science and. However, the existing acobased algorithms suffer from the constructed assignment with high cost.
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