Initial Pheromone Distribution Research Articles

Research for the Task Scheduling Algorithm Optimization based on Hybrid PSO and ACO for Cloud Computing

In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.

A Novel Collaborative Optimization Algorithm for Solving TSP

For the premature convergence and initial pheromone distribution problem of the basic ACO algorithm, PSO algorithm and chaos optimizing strategy are introduced into the ant colony algorithm in order to propose a novel collaborative optimization (CPACO) algorithm based on the collaboration theory. The first, the CPACO algorithm divides the ant colony into several subgroups, and the parameters of the subgroup are regarded as the particles. Then these advantages of PSO algorithm and chaos optimization strategy are fully utilized to optimize these parameters of the ACO algorithm in order to obtain the optimal values of these parameters. And the pheromone exchange operation is introduced into the subgroup. In order to validate the performance of the CPACO algorithm, the TSP problems are selected in here. The simulation results show that the proposed CPACO algorithm has better optimization performance than the traditional ACO algorithm.

A Novel Hybrid Stochastic Searching Algorithm Based on ACO and PSO: A Case Study of LDR Optimal Design

With the rapid development of electronic commerce, the logistics distribution system brings to the widespread attention. And the logistics distribution routing (LDR) optimization is playing the very important role as one of core technologies in the logistics distribution system. This paper proposed a novel hybrid stochastic searching algorithm to solve the LDR optimization design problem, the algorithm unified the ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm effectively, which uses the randomness, the rapidity and the global characteristics of PSO to obtain the initial pheromone distribution firstly, then uses the ACO advantages of the concurrency, the positive feedback and the higher solving precision to find the exact solution. The results of simulation experiment show that the hybrid algorithm has superior global seeking optimization ability and the rapid convergence rate. The method is quick and effective to optimize the LDR problem, and can obtain the optimal solution or approximate optimal solution.

First Order Deceptive Problem of ACO and Its Performance Analysis

Ant colony optimization(ACO), which is one of the intelligential optimization algorithm, has been widely used to solve combinational optimization problems. Deceptive problems have been considered difficult for ant colony optimization. It was believed that ACO will fail to converge to global optima of deceptive problems. This paper proves that the first order deceptive problem of ant colony algorithm satisfies value convergence under certain initial pheromone distribution, but does not satisfy solution convergence. We also present a first attempt towards the value-convergence time complexity analysis of ACO on the first-order deceptive systems taking the n -bit trap problem as the test instance. We prove that time complexity of MMAS, which is an ACO with limitations of the pheromone on each edge, on n -bit trap problem is O( n 2 m. log n ), here n is the size of the problem and m is the number of artificial ants. Our experimental results confirm the correctness of our analysis.

Hardware/Software Partitioning Based on Dynamic Combination of Genetic Algorithm and Ant Algorithm

Genetic algorithm can do colony global searching quickly and stochastically, but can’t efficiently get to optimal results, since it slows down when solving to certain scope. On the other hand, ant algorithm gets to optimal results efficiently, but lacks initial pheromone at the beginning. To solve the hardware/software bi-partitioning problem in embedded system and system-on-a-chip design, the authors put forward a new algorithm based on dynamic combination of genetic algorithm and ant algorithm. The basic idea is: (1) using genetic algorithm to generate preliminary partitioning results, converting them into initial pheromone distribution for ant algorithm, and then using ant algorithm to search for optimal partitioning scheme; (2) while running genetic algorithm, dynamically determining the best combination time of genetic algorithm and ant algorithm to avoid too early or too late termination of the genetic algorithm. The algorithm utilizes the advantages of the two algorithms and overcomes their disadvantages, and it introduces a dynamic combination strategy between them. Experimental results show the algorithm excels genetic algorithm and ant algorithm in performance, and it is discovered that the bigger the partitioning problem is concerned, the better the algorithm performs.