Enhancing the Harris’ Hawk optimiser for single- and multi-objective optimisation

Harris’s Hawk Multi-Objective Optimizer for Reference Point Problems

This paper proposes a novel approach called the Harris’s Hawk Multi-Objective Optimizer (HHMO), which is used for solving reference point multi-objective problems. This algorithm is based on the grey wolf multi-objective optimization algorithm and motivated by the cooperative hunting behaviors of the Harris’s Hawk. These hawks are known as the wolf pack of the sky. The hunting party consists of...

Full Elite Sets for Multi-objective Optimisation

Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of fronta...

Multi-Objective Optimisation for Information Access Tasks

Information access tasks involve setting a large number of model parameters (such as term weighting functions or the particular terms from the document collection to use in the model); in addition, information access tasks frequently attempt to satisfy multiple competing objectives, such as precision and recall. We investigate the use of multi-objective evolutionary algorithms for finding model...

Multi-objective optimisation for regression testing

Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-obje...

Multi-objective Binary Search Optimisation