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.
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Grid Diversity Operator for Some Population-Based Optimization Algorithms
Semantic Scholar · Computer Science · 2015
Abstract
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.