نتایج جستجو برای: dominated sorting ant colony optimization

تعداد نتایج: 464690  

2009
Shouju Li

A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this st...

Journal: :Journal of Object Technology 2009
Richard Wiener

Successful heuristic algorithms for solving combinatorial optimization problems have mimicked processes observed in nature. Two highly successful families of algorithms that do this are simulated annealing and genetic algorithms. Here, a third family of algorithms, ant colony optimization is explored and implemented in C#. The test bed for evaluating the quality of solutions is based on several...

2011
Zhu Xiaoguang Wang Zhangqi Han Qingyao

The basic ant colony algorithm for mobile robot path planning has many problems, such as lack of stability,algorithm premature convergence, more difficult to find optimal solution for complex problems and so on. This paper proposes four improvement measures. 1. Apply genetic algorithm to optimization and configuration parameters of the basic ant colony algorithm; 2. Apply max min ant method to ...

2011
Jin Zhang Teng Fei Ting Liu Liyi Zhang Xinyi Zhao

According to the principle of the weak economy, mathematical model of emergency logistics routing optimization has been established in this paper, and the model is solved by the uniform mutation ant colony algorithm, simulation shows that the optimal results of uniform mutation ant colony algorithm are better than ant colony algorithm. At the same time, compared with the mathematical model of t...

2009
A. B. Dariane A. M. Moradi

A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this st...

2006
Broderick Crawford Carlos Castro

In this paper, we solve some benchmarks of Set Covering Problem and Equality Constrained Set Covering or Set Partitioning Problem. The resolution techniques used to solve them were Ant Colony Optimization algorithms and Hybridizations of Ant Colony Optimization with Constraint Programming techniques based on Arc Consistency. The concept of Arc Consistency plays an essential role in constraint s...

2014
Bo Gu Xiaodan Li Daoyin Qiu Lingyun Zhang

For the shortcoming that the PI controller parameters can’t been dynamic tuning in constant voltage control system of doubly-fed wind turbines, a PI controller parameters dynamic tuning strategy based on the ant colony optimization (ACO) algorithm is presented. This strategy makes the two parameters in PI controller as the ant of the ant colony, the controlled absolute error integral function t...

2012
Benlian Xu Jihong Zhu Qinlan Chen

Swarm intelligence is a relatively novel approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful one is the ant colony optimization. Ant colony optimization (ACO) algorithm, a novel population-based and meta-heuristic app...

Journal: :Advances in Complex Systems 1998
Hozefa M. Botee Eric Bonabeau

Ant Colony Optimization (ACO) is a promising new approach to combinatorial optimization. Here ACO is applied to the traveling salesman problem (TSP). Using a genetic algorithm (GA) to nd the best set of parameters, we demonstrate the good performance of ACO in nding good solutions

2007
Marco Chiarandini

Population-based method in which artificial ants iteratively construct candidate solutions. Solution construction is probabilistically biased by pheromone trail information, heuristic information and partial candidate solution of each ant (memory). Pheromone trails are modified during the search process to reflect collective experience.

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