Edinburgh Napier University

  We present a novel diversity method named Grid Diversity
Operator (GDO) that can be incorporated into multiple
population-based optimization algorithms that guides the
containing algorithm in creating new individuals in sparsely
visited areas of the search space. Experimental tests on a set
of unimodal and multimodal benchmark functions from the
literature using GDO in conjunction with opt-aiNet algorithm
show that GDO maintains better diversity in most
cases, leading to an order-of-magnitude reduction in the
number of objective function evaluations needed to converge
while finding similar numbers of peaks in the majority of
benchmarks.