نتایج جستجو برای: ant colony optimization algorithm
تعداد نتایج: 1023213 فیلتر نتایج به سال:
the capacitated clustering problem (ccp) is one of the most importantcombinational optimization problems that nowadays has many real applications inindustrial and service problems. in the ccp, a given n nodes with known demandsmust be partitioned into k distinct clusters in which each cluster is detailed by anode acting as a cluster center of this cluster. the objective is to minimize the sumof...
ant colony optimisation (aco) algorithm and adaptive refinement mechanism are used in this paper for solution of optimization problems. many of the real engineering problems are، however، of continuous nature and finding their solution by discrete ant based algorithms requires discretisation of the decision variables in which affected the convergence and performance of the algorithm. in this pa...
in this paper, a multi-objective reconfiguration problem has been solved simultaneously by a modified ant colony optimization algorithm. two objective functions, real power loss and energy not supplied index (ens), were utilized. multi-objective modified ant colony optimization algorithm has been generated by adding non-dominated sorting technique and changing the pheromone updating rule of ori...
Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages long running time and easy stagnation still restrict its further wide application many fields. In this study, saltatory evolution (SEACO) is proposed to incr...
One of the most important issues in the field of optimizing water resources management is the optimal utilization of the dam reservoirs. In the recent decades, the optimal operation of dams has been one of the most interesting issues considered by water resources planners in the country. Due to the complexities of the typical optimization methods, employing an evolutionary algorithm is regarded...
The traveling salesman problem (TSP) is one of the most important combinational optimization problems that have nowadays received much attention because of its practical applications in industrial and service problems. In this paper, a hybrid two-phase meta-heuristic algorithm called MACSGA used for solving the TSP is presented. At the first stage, the TSP is solved by the modified ant colony s...
water conveyance systems (wcss) are costly infrastructures in terms of materials, construction, maintenance and energy requirements. much attention has been given to the application of optimization methods to minimize the costs associated with such infrastructures. historically, traditional optimization techniques have been used, such as linear and non-linear programming. in this paper, applica...
The fixed-charge Capacitated Multi-commodity Network Design (CMND) is a well-known problem of both practical and theoretical significance. Network design models represent a wide variety of planning and operation management issues in transportation telecommunication, logistics, production and distribution. In this paper, Ant Colony Optimization (ACO) based neighborhoods are proposed for CMND pro...
Ant colony optimization and artificial potential field were used respectively as global path planning and local path planning methods in this paper. Some modifications were made to accommodate ant colony optimization to path planning. Pheromone generated by ant colony optimization was also utilized to prevent artificial potential field from getting local minimum. Simulation results showed that ...
Ant Colony Optimization (ACO) algorithm is a novel metaheuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation o...
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