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E2RGWO: Exploration Enhanced Robotic GWO for Cooperative Multiple Target Search for Robotic Swarms

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Abstract

The Grey Wolf Optimizer (GWO) is a novel population-based optimization algorithm. It has become quite popular among researchers in the robotic domain recently. Target searching is an application of robotics. This paper proposes an exploration enhanced robotic grey wolf optimizer (E2RGWO) algorithm based on Robotic Grey Wolf Optimizer for swarm-based target searching in an unknown environment. The position update equation is discussed using a random individual from the population, which guides the search to enhance the exploration of the grey wolf. It also uses nonlinear control of parameter \(\vec {a}\) to balance the exploration and exploitation, making it suitable for multi-target search applications. It also uses an adaptive inertia weight coefficient depending on the aggregation degree and evolutionary speed to enhance exploration and improves diversity. The comparison with the existing methodology for target search shows that the E2RGWO algorithm significantly improves the detection rate and search latency.

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Authors and Affiliations

  1. Department of Information and Communications Technology, ABV Indian Institute of Information Technology and Management, Morena Link Road, Gwalior, Madhya Pradesh, 474015, India

    Vikram Garg

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  1. Vikram Garg
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Vikram Garg was involved in conceptualization of this study, methodology, software, writing—original draft preparation.

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Correspondence to Vikram Garg.

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Garg, V. E2RGWO: Exploration Enhanced Robotic GWO for Cooperative Multiple Target Search for Robotic Swarms. Arab J Sci Eng 48, 9887–9903 (2023). https://doi.org/10.1007/s13369-022-07438-5

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