https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42405 Does optimizing the implementation of an optimization algorithm pay off? Do bugs matter? and Is using more complicated but also more efficient data structures worth the effort? The intuitive answer to all of these questions is yes, but there is little published evidence. To bridge this gap, we use one of the most studied combinatorial optimization problems-the Traveling Salesman Problem-as a test bed and implement two state-of-the-art approaches for solving it-the Lin-Kernighan Heuristic and an Ejection Chain Method. We investigate implementation effort and performance gain, in order to provide further insights to the above questions.]]> Tue 23 Aug 2022 08:24:02 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27852 TSPLib to investigate the performance of these algorithms. Our study is different from previous work along this line of research in that we consider the entire runtime behavior of the algorithms rather than just their end results. This leads to one of the most comprehensive comparisons of these algorithms using the TSP instances. We then introduce an improved S&C-ECM (named FSM**) that can outperform LK, TS, and MNS. In order to further boost the performance, we develop new hybrid versions of our ECM implementations by combining them with Evolutionary Algorithms and Population-based Ant Colony Optimization. We compare them to similar hybrids of LK, TS, and MNS. Our results show that hybrid algorithms of S&C-ECM, LK, TS and MNS are all very efficient for solving the TSP. We also find that the full runtime behavior comparison provides deeper and clearer insights, while focusing on end results only could have led to a misleading conclusion.]]> Tue 06 Jun 2017 12:36:12 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23347 Sat 24 Mar 2018 07:13:33 AEDT ]]>