Earticle

고객의 요구에 대한 빠른 대응과 유연하고 효율적으로 새로운 제품을 적기에 개발하기 위해서는 제품 플랫폼에 기초한 대량 맞춤이 절실히 요구된다. 이러한 목적을 달성하기 위하여 기업들은 상대적으로 생산비용을 낮게 유지하면서 대량생산의 이점을 유지하고 동시에 고객의 요구사항을 만족시키기 위해, product family를 도입하고 가능하면 작은 변화를 통하여 제품의 다양성을 유지하고자 한다. Product family를 설계할 때 중요한 이슈 중에 하나는 제품의 공통성과 차별성간의 절충점을 찾아내는 것인데, 본 연구에서는 설계자들이 product family 재설계를 용이하게 하기 위한 방법론을 제안한다. 이를 위하여 본 연구에서는 ant colony 알고리즘과 product family의 공통성 평가지수를 이용하여 product family 재설계 방법론을 개발한다. 제안한 방법론은 복잡하고 반복적인 많은 계산과정을 가지고 있는 다른 방법과 달리 메타 휴리스틱 알고리즘을 적용하여 인간의 간섭을 줄이고, 실험결과의 정확도, 반복성 및 강건성을 향상시킨다. 본 연구에서는 컴퓨터 마우스 제품군을 대상으로 제안한 방법의 타당성을 검증하였고, 추가적으로 product family 레벨과 부품 레벨의 product family 재설계 추천방안도 제시하였다.

The project of ant colony algorithm optimization neural network combining blind equalization algorithm is proposed. The better initial weights of neural networks are provided because of the randomness, ergodicity and positive feedback of the ant colony algorithm. And then, a combination of optimal weights are found through BP algorithm, which is fast local search speed. Thus blind equalization performance is improved. Computer simulation show that, the novel blind equalization algorithm speeds up the convergence rate, reduces the remaining steady-state error and bit error rate, which is compared with the Neural Network Blind Equalization Algorithm(NNBE) and Genetic Algorithm optimization Neural Network Blind Equalization Algorithm(GA-NNBE) .

Since the ant colony algorithm is proposed, it has achieved the remarkable achievements in many fields. With the development of the times, the traditional ant colony algorithm exposes its limitations for solving the questions. In this paper, we improve the ant colony algorithm. And we combine the ant colony algorithm with the genetic algorithm. Then, we propose the GAPSPAC algorithm. The algorithm combines the advantages of the genetic algorithm and the ant colony algorithm. And it overcomes the disadvantages to improve the efficiency of solving the questions. In the last experiment, we can see the algorithm has the better problem solving ability and the stability.

Ant colony algorithm since it has a better convergence and parallelism, widely used in the data retrieval, however, is not high, due to the characteristics of retrieval object use seriously affect the accuracy of retrieval, and according to this problem, this paper proposed an improved ant colony algorithm, this algorithm will retrieve objects comprehensive characteristics into the ant colony algorithm, and solve the convergence speed and computational complexity of the algorithm, obtained good results in image retrieval.

Aiming at solving the NP-hard workshop production scheduling problems, proposed one kind based on mind evolutionary algorithm. The algorithm in the traditional ant colony algorithm is established, and the combination of evolutionary thought and local optimization idea overcomes the basic ant colony algorithm is easy to fall into local optimal defects, the improved state transition rules, defining a pheromone range, improve the pheromone update strategy, and the increase of neighborhood search. Experimental results show that, for a typical production scheduling problems, based on mind evolutionary ant colony algorithm can obtain the optimal solution in theory, optimal solution, the solution and average three indicators are better than the basic ant colony algorithm, showed good performance.

Edge extraction is an important part in the detection of infrared thermal images. Ant colony algorithm has the characteristics of high efficiency, high noise suppression, and comprehensive information of edge information. The basic principle of ant colony algorithm is analyzed. An ant colony optimization algorithm for image edge detection is established. And to have defective parts for analysis of infrared thermography The ant colony algorithm and the classical Canny operator are compared and analyzed. The experimental results show that the algorithm has high efficiency, comprehensive information and high computational efficiency.

In order to effectively address the lack of basic ant colony algorithm in terms of parameters, we use four-step method instead of the popular three-step, based on a large number of experiments of the parameters setting, this paper summed up an effective selection method for m, α, β, ρ and Q parameters to select the best combination of parameters. Applying the improved ant colony algorithms including optimal retention policy ant system, max-min ant system, ant-based sorting systems and best-worst ant system, performance comparison analysis was conducted with the same TSP problems, and experiments proved that the proposed method of parameter combinations greatly improves the speed of convergence.

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.

In building environment energy management is still a challenging and big problem. Several methods and proposals exist in the literature for energy management, but the tradeoff between occupants’ comfort level and energy consumption reduction is still required more attention to improve occupants comfort index and minimized energy consumption. In this paper, we proposed efficient optimization method for Simultaneous Comfortable and Energy Saving using ACO (Ant Colony Optimization) algorithm in Building Environment. We have given focus in two directions. First is to maximize the occupants’ comfort level and second is to control the usage of power. At the end, we compared results with optimization method using ACO method using GA (Genetic Algorithm). The results show that amount of consumed energy of system using ACO algorithm is consumed less power as compare to the optimization method using GA.

The ant colony algorithm is an algorithm which is used to find the optimal path. As a kind of bionic evolutionary algorithm, the ant colony algorithm is inspired by the real ant colony foraging mechanisms. Firstly, this paper introduces the basic model of the ant colony algorithm. Then, aiming at the shortcomings of the ant colony algorithm, we propose a new probability formula of the optimal path and the new formula of the pheromone update. In addition, we combine the traditional ant colony algorithm with the local search algorithm and propose the improved ant colony algorithm. It is the LSACA algorithm. In the experimental analysis, we set and analyze the parameters of the algorithm. Then, we compare with the traditional algorithm to prove the feasibility and the effectiveness of the algorithm.

An improved ant colony algorithm based on the three constraint conditions that aiming at the problem of resource scheduling in cloud computing is proposed in this paper. this method is divided into three steps: Firstly, we describe the state transition probability by using the information heuristic factor and the expected heuristic factor. Secondly, the pheromone update strategy is used to design the scheduling process. Finally, the optimal path is based on the expected execution time, network delay and network bandwidth of three constraints. Experimental results show that the proposed method has faster execution speed than the traditional ant colony algorithm, and the load balancing of the results is more satisfactory.

Ant colony algorithm (ACA) is a new heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. An Granular ACA algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature convergence problem of the basic ACA algorithm on TSP. Proposesing a Granular computing adaptive ant pheromones mechanism base on researching on ant colony algorithm model, pheromones update and pheromones selection had been improved. Make up the traditional ant colony algorithm for the calculation of distribution network planning that is slow and easy to fall into local optimal solution. And improved the convergence of the optimal solution. The validity of the GACA has been verified using a testing function. In addition, a satisfactory optimum solution for a Power Distribution Network Planning that has 73 users has been obtained.

Multiple Unmanned Underwater Vehicles Is a typical combinatorial optimization problem, to achieve multiple AUV, coordinated, collaborative tasks to complete complex jobs subsea. Through analyzing the ant colony optimization algorithm, the paper proposed An Meta-heuristic ant colony optimization algorithm the Implementation to solve the multi AUVs to achieve the task allocation problem, and had simulation test based on the consolidated analyze the advantages of multiple unmanned underwater vehicle .results show that the ant colony optimization algorithms in solving multi-task allocation problem of multiple unmanned underwater vehicle showed a good performance.

This paper analyzes the present situation of the current network security problems and points out the research and development of intrusion detection system has very important significance on the basis of comparative analysis of the traditional static security model and PPDR dynamic security model, and according to this model, using ant colony algorithm is a distributed computing network intrusion of metrics, the determination of index contrast and invasion route, increase the accuracy of testing operation and calculation results show that the effectiveness of the solution and the convergence speed. For distributed network intrusion is put forward a new kind of means.

Using ant colony algorithm to solve TSP (traveling salesman problem) has some disadvantages as easily plunging into local minimum, slow convergence speed and so on. In order to find the optimal path accurately and rapidly, an improved ant colony algorithm is proposed. Experimental results show that the improved ant colony algorithm has better effectiveness for TSP problems solutions.

Scientific workflow scheduling problem is a combinatorial optimization problem. In the real application, the scientific workflow generally has thousands of task nodes. Scheduling large-scale workflow has huge computational overhead. In this paper, a parallel algorithm for scientific workflow scheduling is proposed so that the computing speed can be improved greatly. Our method used ant colony optimization approaches on the GPU. Thousands of GPU threads can parallel construct solutions. The parallel ant colony algorithm for workflow scheduling was implemented with CUDA C language. Scheduling problem instances with different scales were tested both in our parallel algorithm and CPU sequential algorithm. The experimental results on NVIDIA Tesla M2070 GPU show that our implementation for 1000 task nodes runs in 5 seconds, while a conventional sequential algorithm implementation runs in 104 seconds on Intel Xeon X5650 CPU. Thus, our GPU-based parallel algorithm implementation attains a speed-up factor of 20.7.

Path planning for the mobile robot is to find a shortest obstacle free path from the starting position to the target position. Ant colony optimization method is frequently used to obtain the optimal path in the static known environment. But local minimum and slow convergence are the main problems of ant colony algorithm. This paper proposes a modified ant colony algorithm for path planning of the mobile robot in a known static environment. The modified ant colony algorithm can enlarge the searching range so that the local minimum problem can be weakened, while the algorithm can also converge quickly. And in the optimal path searching process, the turning factor is considered, too. The obtained optimal path has not only short distance, but also few big turning positions. Simulations are done to verify the proposed modified ant colony algorithm.

This research considers a low carbon scheduling problem in unrelated parallel machines. To solve this problem, we first establish a low carbon scheduling mathematical model. Then an iterative ant optimization algorithm is presented. Furthermore, parameters of proposed iterative ant optimization algorithm are selected by Taguchi methods on generating test dataset. Finally, comparative experiments indicate the proposed iterative ant optimization algorithm has better performance on minimizing energy consumption as well as total tardiness.

The extension of ant colony algorithm was proposed by Dorigo, the founder of ant colony algorithm, which is the latest ant colony algorithm for solving a continuous space optimization problem. Considering the blindness of man-made choice of initial solution and initial parameters of the algorithm, and according to the algorithm converging slowly and easily falling into local optimum, this paper has provided improvement strategy for this optimization. It has introduced quantum computing and genetic algorithm, chaos optimization to carry out combination and comparison, and it has carried out improvement on the weight internally solved by memory in the algorithm. The effectiveness of various combined algorithms was determined through the optimization of numerous multi-dimensional continuous functions.