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Population-based meta-heuristics use particles represented by
d
-
dimensional points to encode candidate solutions to an optimization problem. The goal of this work is to introduce a new type of particle represented by a
d
-
dimensional hypercube. Our new representation offers a field of view to swarm particles, making them aware of their surroundings . This gives each particle its own coverage of the search space. We evaluated the effect of our approach in the performance of seven swarm-based algorithms, including artificial bee colony, cuckoo search optimization and particle swarm optimization. We used 30 well-known benchmark functions and six different numbers of dimensions. We compared the performances of the algorithms with
blind
and
sighted
particles using Mann-Whitney tests. The results of our extensive experiments show that
sighted
particles perform at least as well as
blind
ones in 93% of all scenarios, across different algorithms, benchmark functions and numbers of dimensions, showing significantly better results 51% of the time. We also proposed a new scale which measures an algorithm’s exploration and exploitation capabilities and we used it to explain our results. Finally, we followed a
sighted
swarm throughout the convergence process, showing its ability to cover large portions of the search space. Our take-home message is that any swarm algorithm can adopt our particle representation in order to improve its performance, because any method can benefit from improving its exploitation and exploration capabilities.