The Improved Ant Colony Optimization Algorithm for MLP considering the Advantage from Relationship

An improved ant colony optimization (ACO) is presented to solve the machine layout problem (MLP), and the concept is categorized as follows: firstly, an ideology on “advantage from quantity” and “advantage from relationship” is proposed and an example is demonstrated. In addition, the strategy of attached variables under local polar coordinate systems is employed to maintain search efficiency, that is, “advantage from relationship”; thus, a mathematical model is formulated under a single rectangular coordinate system in which the relative distance and azimuth between machines are taken as attached design variables. Further, the aforementioned strategies are adopted into the ant colony optimization (ACO) algorithm, thereby employing the inverse feedback mechanism for dissemination of pheromone and the positive feedback mechanism for pheromone concentration. Finally, the effectiveness of the proposed improved ACO is tested through comparative experiments, in which the results have shown both the reliability of convergence and the improvement in optimization degree of solutions.

• Publication year

  2017

• Venue

  Mathematical Problems in Engineering

• Publication date

  2017-07-31

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1155/2017/3920327
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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