Solving redundancy optimization problem with a new stochastic algorithm

  • Authors:

  • Chun-Xia Yang;Zhi-Hua Cui

  • Affiliations:

  • Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, China

  • Venue:
  • SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
  • Year:
  • 2011

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Abstract

In order to solve the real-world problem which named Cleveland heart disease classification problem, we used a new stochastic optimization algorithm that simulate the plant growing process. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomenons, we call it briefly with APPM algorithm. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction. In this algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Phototropism operator is designed to introduce the fitness function value, as well as phototropism operator is used to decide the growing direction. Up to date, there is little applications. Therefore, in this paper, APPM is successfully applied to the redundancy optimization problem. The objective of the redundancy allocation problem is to select from available components and to determine an optimal design configuration to maximize system reliability. BP neural network is trained to calculate the objective fitness, while APPM is applied to check the best choice of feasibility of solution. One example is used to illustrate the effectiveness of APPM.