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Democracy-inspired particle swarm optimizer with the concept of peer groups

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Abstract

This article proposes to integrate the concept of governance in human society with the nature-inspired particle swarm optimization (PSO) algorithm. A population-based iterative global optimization algorithm, called Democracy-inspired particle swarm optimization with the concept of peer groups (DPG-PSO) has been developed for solving multidimensional, non-linear, non-convex, and multimodal optimization problems by exploiting the concept of the new peer-influenced topology. Here the particles, each of which model a candidate solution of the problem under consideration, are given a choice to follow two possible leaders who have been selected on the basis of a voting mechanism. The leader and the opposition have their influences proportional to the total number of votes polled in their favor. A detailed empirical study comprising tuning of DPG-PSO parameters and its optimizing mechanism has been presented in the paper. The algorithm is tested in a standard benchmark suite consisting of unimodal, multimodal, shifted and rotated functions. DPG-PSO is found to statistically outperform seven other well-known PSO variants in terms of final accuracy and robustness over majority of the test cases, thus, proving itself as an efficient algorithm.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, India

    Ritambhar Burman & Soumyadeep Chakrabarti

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, 700108, India

    Swagatam Das

Authors
  1. Ritambhar Burman
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  2. Soumyadeep Chakrabarti
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  3. Swagatam Das
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Correspondence to Swagatam Das.

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Communicated by V. Loia.

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Burman, R., Chakrabarti, S. & Das, S. Democracy-inspired particle swarm optimizer with the concept of peer groups. Soft Comput 21, 3267–3286 (2017). https://doi.org/10.1007/s00500-015-2007-8

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