A hyper-heuristic methodology for coordinating swarms of robots in target search. (October 2021)

• Record Type: Journal Article
• Authors: Cimino, Mario G.C.A.; Minici, Domenico; Monaco, Manilo; Petrocchi, Stefano; Vaglini, Gigliola
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Target search -- Swarm robotics -- Bio-inspired heuristics -- Evolutionary optimization; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107420
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Biologically inspired heuristics for target search lack modularity and modifiability. The proposed hyper-heuristic is based on modular and adaptive swarm behavior. Combining stigmergy, flocking and evolution outperforms bio-inspired heuristics. Abstract: Target search aims to discover elements of various complexity in a physical environment, by minimizing the overall discovery time. Different swarm intelligence algorithms have been proposed in the literature, inspired by biological species. Despite the success of bio-inspired techniques (bio-heuristics), there are relevant algorithm selection and parameterization costs associated with every new type of mission and with new instances of known missions. In this paper, evolutionary optimization is proposed for achieving significant improvements of the mission performance. Although adaptive, the logic of bio-heuristics is nevertheless constrained by models of biological species. To generate more adaptable logics, a novel design approach based on hyper-heuristics is proposed, in which the differential evolution optimizes the aggregation and tuning of modular heuristics for a given application domain. A modeling and optimization testbed has been developed and publicly released. Experimental results on real-world scenarios show that the hyper-heuristics based on stigmergy and flocking significantly outperform the adaptive bio-heuristics. Graphical abstract: Image, graphical abstract