改进的细菌觅食优化算法求解0-1背包问题

细菌觅食优化算法作为一种新兴的智能优化算法，一般用来解决连续域的问题。为了解决离散域问题，提出了一种改进的细菌觅食优化算法。采用线性递减的思想和随机的游动长度代替固定步长和随机游动方向，改进了趋向性操作方案，并将其应用于解决0-1背包问题。将改进的细菌觅食优化算法与遗传算法、离散粒子群优化算法及基本的离散化细菌觅食优化算法分别在小规模和大规模的0-1背包问题上进行了仿真比较，表明了改进的细菌觅食优化算法能取得较好的效果，寻优能力强。

Improved Bacteria Foraging Optimization Algorithm for 0-1 Knapsack Problem

As a new intelligent optimization algorithm,Bacteria Foraging Optimization （ BFO） algorithm is generally used to solve contin-uous problems. In order to solve discrete problems,an improved BFO algorithm was proposed in this paper,which adopted linearly de-creasing thoughts and random steps of the bacterial tumble instead of fixed steps and random direction to promote chemotaxis solution, and applied to 0-1 knapsack problem. By contrast with Genetic Algorithms （GA）,Discrete Particle Swarm Optimization （DPSO） and basic BFO algorithm on both small-scale and large-scale 0-1 knapsack problems,the simulation results indicate that the improved BFO algorithm performs better with high search capability.