Seed Disperser Ant Algorithm: An Evolutionary Approach for Optimization

  • Wen Liang Chang
  • Jeevan KanesanEmail author
  • Anand Jayant Kulkarni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)


The Seed Disperser Ant Algorithm (SDAA) is inspired from the evolution of Seed Disperser Ant (Aphaenogaster senilis) colony. The ants in the colony are highly related siblings sharing average 75 % similarity in genotype. Hence, the genotype of every ant represents variables in binary form that are used to locally search for optimum solution. Once the colony matures, in other words a local optimum solution reached, nuptial flights take place where female genotype copies the male genotype originating from another colony. Once all colonies saturate new young queen emerges to establish new colonies. This diversifies the search for global optimum. The SDAA is validated by solving four 30 dimensional classical benchmark problems and six composite benchmark functions from CEC 2005 special session. The optimal results are found to be better than the selected state-of-the-art swarm intelligence based optimization.


Seed disperser ant algorithm Evolutionary computation Optimization 



This work is supported by ER011-2013A, Ministry of Science, Technology and Innovation, Malaysia (MOSTI).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wen Liang Chang
    • 1
  • Jeevan Kanesan
    • 1
    Email author
  • Anand Jayant Kulkarni
    • 2
  1. 1.Nature Inspired Meta-heuristic Group, Department of Electrical Engineering, Faculty of EngineeringUniversity MalayaKuala LumpurMalaysia
  2. 2.Department of Mechanical Engineering, Symbiosis Institute of TechnologySymbiosis International UniversityPuneIndia

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