A Novel Hybrid Global Optimization Algorithm Based on Particle Swarm Optimization and Differential Evolution

提出一种基于粒子群算法(PSO)和差分进化算法(DE)相结合的新型混合全局优化算法——PSODE.该算法基于一种双种群进化策略,一个种群中的个体由粒子群算法进化而来,另一种群的个体由差分操作进化而来.此外,通过采用一种信息分享机制,在算法执行过程中两个种群中的个体可以实现协同进化.为了进一步提高PSODE算法的性能,摆脱陷入局部最优点,还采用了一种变异机制.通过4个标准测试函数的测试并与PSO和DE算法进行比较,证明本文提出的PSODE算法是一种收敛速度快、求解精度高、鲁棒性较强的全局优化算法

Logistics Distribution Center Location Using MCPCO Algorithm

Logistics distribution center location is an optimization problem that selects a certain number of locations as distribution centers in a logistics system so as to minimize the total cost of the whole logistics networks. A new approach is presented to solve this problem based on our previous propose..

A new algorithm for TSP based on swarm intelligence

Inspired by the behavior of people, a new algorithm for the combinatorial optimization is proposed. This is a heuristic approach based on swarm intelligence, which is firstly introduced as the theoretical background in this paper. It is also a parallel algorithm, in which individuals of the swarm search the state space independently and simultaneously. When one encounters another in the process, they would communicate with each other, and utilize the more valuable experiences to improve their own fitness. A positive feedback mechanism is designed to avoid vibrations. Ten benchmarks of the TSPLIB are tested in the experiments. The results indicate that the algorithm can quickly converge to the optimal solution with quite low cost. Some conclusions about the algorithm are summarized finally

Robot Path Planning Using Bacterial Foraging Algorithm

The goal of the robot path planning problem is to determine an optimal collision-free path for a mobile robot between a start and a target point in an environment surrounded by obstacles. Optimal collision-free trajectory planning for mobile robot is always a major issue in robotics due to the necessity for the robots' course of movement. In recent years, as the emergence of another member of the swarm intelligence family bacterial foraging optimization (BFO), the bacterial foraging strategy has attracted a great deal of interests. In this work, the path planning problem is approached by the mobile robot that mimics the foraging strategy of BFO algorithm. The objective is to minimize the path length and the number of turns without colliding with an obstacle. In the simulation studies, two test scenarios of static environment with different obstacle distribution are adopted to evaluate the performance of the proposed method. Simulation results show that our method is able to generate a collision-free path in complex environment