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The optimization model of utilization scheme of railway passenger station arrival and departure tracks is set up by analysis the basic principle of the utilization of arrival and departure tracks in this paper. It is proved that this problem is NP-hard, so an improved ant colony optimization algorithm is given to solve this model. In this algorithm, an improved state transition rules and a mutatio...Show More
Combined with the idea of the Bean Optimization algorithm (BOA), the ant colony optimization (ACO) algorithm is presented to solve the well known traveling salesman problem (TSP). The core of this algorithm is using BOA to optimize the control parameters of ACO which consist of heuristic factor, pheromone evaporation factor and random selection threshold, and applying ant colony system...Show More
In response to the problems of low diagnostic accuracy and low recovery efficiency in current research on fault diagnosis and recovery in transmission networks, this paper used an improved ant colony optimization algorithm to study it and improve the accuracy of fault diagnosis and recovery efficiency. It updated the pheromones of traditional ant algorithms and calculated the transition probabilit...Show More
Aiming at the problem of route optimization, a multi-objective optimized route distribution model is constructed with the goal of the smallest total transportation cost and the shortest total delivery route. In order to overcome the shortcomings of ant colony optimization, such as too long search time and easy to fall into local optimum, the ant colony pheromone volatilization coefficient is impro...Show More
An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to c...Show More
Ant colony optimization is a successful swarm intelligence method for solving various combinatorial optimization problems. It uses a population-based meta-heuristic that is based on the foraging behavior of real ant colonies, and these ants use pheromones to communicate indirectly with others. While the scale of problem increases, ACO necessitates much more time and resource to solve the opt...Show More
Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. Many strategies for ACO have been studied, but fewer tuning methodologies have been done on ACO's parameters which influence the algorithm directly. The setting of ACO's parameters is considered as a combinational optimization problem in this paper. The par...Show More
The 0ߝ1 Knapsack Problem is of a class of typical combinational optimization problems and is NP-hard. It has important practical significance to study it. Based on the characteristics of the 0ߝ1 Knapsack Problem, we design a binary coding directed graph which makes the Ant Colony algorithm suitable for the Knapsack Problem. In addition, we also adopt the concept of backtracking from the Nested Par...Show More
The Travelling Salesman Problem (TSP) is a very popular combinatorial optimization problem of real world. The objective is to find out a shortest possible path travelled by a salesman while visited every city once and returned to the origin city. TSP is one of the NP hard problems and several attempts have been done to solve it by traditional methods. Computational methods give better solution for...Show More
The ever-increasing population and high mobility impact the massive number of vehicles that affect the development of public transportation and the determination of effective routes. These factors make it very important to optimize the route because it will impact operational costs and the punctuality of picking up passengers. Determining the optimal route can be categorized as a Traveling Salesma...Show More
QoS multicast routing problem is a nonlinear combination optimization problem, which has been proved to be a NP complete problem. a hybrid algorithm with ant colony optimization algorithm(ACO) and particle a warm optimization algorithm(PSO) is presented. The ACO-GA algorithm absorbs the merits of ACO and GA respectively. In hybrid algorithm, the position update of PSO is used to ...Show More
ACO (Ant Colony Optimization) is popular swarm intelligence with stochastic nature meta-heuristic algorithm applied to solve many combinatorial optimization problems. The characteristics of ACO includes robust, positive feedback, distributed computing and easy fusing with other algorithms makes ACO simpler and efficient in searching optimal solutions. But the ACO algorithm ...Show More
Continuous demand in power transmission network caused by reactive power has been highlighted as the main factor in voltage depreciation and also increase of total transmission loss. Various studies involving reactive power planning (RPP); such as optimal reactive power dispatch (ORPD), optimal capacitor placement, static VAR compensator and installation of flexible AC transmission system (FACTs) ...Show More
For several decades the remote sensing image classification methods for depicting land cover have gained a great achievements, but with the more multi-source and multidimensional data, the conventional remote sensing image classification methods based on statistical theory have exposed some limitation. So in recent years, artificial intelligence techniques have being applied to remote sensing imag...Show More
As a combinatorial optimization problem, vehicle routing problem (VRP) is a typical NP-hard problem; an assumption that the demand of customers can not be split is given to the traditional VRP formulation. However, the transportation cost can be reduced by means of splitting the demand of customers in practical logistics operation. This paper solved the split delivery vehicle routing problem (SDVR...Show More
In order to realize rapid processing and accurate interpretation of big data, and to achieve reasonable scheduling and application of cloud computing resources, this paper proposes an integrated artificial intelligence processing and analysis model, combining Map-Reduce and the improved ant colony algorithm based on PSO. Firstly, it uses Map-Reduce to divide cloud computing tasks into several subt...Show More
In order to increase solution speed for nodal admittance matrix of mathematical model of modern power network, a node code scheme with minimal new-adding nonzero injection elements is needed. To this point, a new algorithm for node code optimization based on the parallel ant colony optimization (ACO) algorithm is designed in this paper. Compared with traditional node code optimization method...Show More
To solve the problem of resource allocation among the sub-enterprises in the management of construction enterprise, an improved Ant Colony Optimization (ACO) algorithm based on entropy was introduced. After analyzing the feasibility of the usage of the algorithm, the allocation model was set up. Engineering practice shows that the method can realize the resource allocation efficiently with l...Show More
On the basis of the analyses of ant colony optimization (ACO) and particle swarm optimization (PSO), continuous ant-particle swarm optimization (CA-PSO) applied in continuous function optimization is proposed. After the space partition is properly employed, ACO is applied to search the sensitive areas through the whole solution space. And then PSO is initialized according to ACO ...Show More
An improved ant colony optimization (ACO) algorithm is proposed in this paper for improving the accuracy of path planning. The main idea of this paper is to avoid local minima by continuously tuning a setting parameter and the establishment of novel mechanisms for updating partial pheromone and opposite pheromone. As a result, the global search of the proposed ACO algorithm can be sign...Show More
Aiming at the optimum allocation problem of redundancy for fighter with multiple effectors and considering the advantage of ant colony optimization (ACO) in optimization, a new control allocation (CA) strategy based on ACO is proposed. According to the mathematical model for CA problem, optimization goals and constraints are provided for continuous space optimization. By extending A...Show More
This study proposes a new strategy combining with the SVM(support vector machine) classifier for features selection that retains sufficient information for classification purpose. Our proposed approach uses F-score models to optimize feature space by removing both irrelevant and redundant features. To improve classification accuracy, the parameters optimization of the penalty constant C and the ba...Show More
Electric utilities are the companies responsible for ensuring energy supply meets their customers' requirement. While ensuring the energy is generated in the right amount, they have to guarantee that the energy is generated within feasible cost. Economic Load Dispatch (ELD) problem involves the scheduling of generating unit outputs that can satisfy load demand at minimum operating cost. Several ap...Show More
This paper presents a comparison between swarm intelligence (SI) techniques; namely Particle Swarm Optimization and Ant Colony Optimization, to solve analog circuit sizing problems. Performances in terms of optimum quality and computing time of both algorithms are checked via two applications that consist of optimizing performances of a CMOS second generation current conveyor (CCII), and an operat...Show More
Recently, the attraction of meta-heuristic techniques has increased due to their capability for solving complex optimization problems in various areas. This paper present a comparative study between two meta-heuristics techniques namely Ant Colony Optimization (ACO) and Differential Evolution (DE). In order to determine the best way to combine these two techniques in view of a successful hyb...Show More