Clarifying the Difference in Local Optima Network Sampling Algorithms

We conduct the first ever statistical comparison between two Local Optima Network (LON) sampling algorithms. These methodologies attempt to capture the connectivity in the local optima space of a fitness landscape. One sampling algorithm is based on a random-walk snowballing procedure, while the other is centred around multiple traced runs of an Iterated Local Search. Both of these are proposed for the Quadratic Assignment Problem (QAP), making this the focus of our study. It is important to note the sampling algorithm frameworks could easily be modified for other domains. In our study descriptive statistics for the obtained search space samples are contrasted and commented on. The LON features are also used in linear mixed models and random forest regression for predicting heuristic optimisation performance of two prominent heuristics for the QAP on the underlying combinatorial problems. The model results are then used to make deductions about the sampling algorithms’ utility. We also propose a specific set of LON metrics for use in future predictive models alongside previously-proposed network metrics, demonstrating the payoff in doing so

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

pharmACOphore: multiple flexible ligand alignment based on ant colony optimization

info:eu-repo/semantics/publishe

Program trace optimization

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Paper to be presented at the Fifteenth International Conference on Parallel Problem Solving from Nature (PPSN XV), Coimbra, Portugal on 8-12 September 2018.We introduce Program Trace Optimization (PTO), a system for `universal heuristic optimization made easy'. This is achieved by strictly separating the problem from the search algorithm. New problem definitions and new generic search algorithms can be added to PTO easily and independently, and any algorithm can be used on any problem. PTO automatically extracts knowledge from the problem specifi cation and designs search operators for the problem. The operators designed by PTO for standard representations coincide with existing ones, but PTO automatically designs operators for arbitrary representations

Perturbation strength and the global structure of qap fitness landscapes

We study the effect of increasing the perturbation strength on the global structure of QAP fitness landscapes induced by Iterated Local Search (ILS). The global structure is captured with Local Optima Networks. Our analysis concentrates on the number, characteristics and distribution of funnels in the landscape, and how they change with increasing perturbation strengths. Well-known QAP instance types are considered. Our results confirm the multi-funnel structure of QAP fitness landscapes and clearly explain, visually and quantitatively, why ILS with large perturbation strengths produces better results. Moreover, we found striking differences between randomly generated and real-world instances, which warns about using synthetic benchmarks for (manual or automatic) algorithm design and tuning

Ownership and control in a competitive industry

We study a differentiated product market in which an investor initially owns a controlling stake in one of two competing firms and may acquire a non-controlling or a controlling stake in a competitor, either directly using her own assets, or indirectly via the controlled firm. While industry profits are maximized within a symmetric two product monopoly, the investor attains this only in exceptional cases. Instead, she sometimes acquires a noncontrolling stake. Or she invests asymmetrically rather than pursuing a full takeover if she acquires a controlling one. Generally, she invests indirectly if she only wants to affect the product market outcome, and directly if acquiring shares is profitable per se. --differentiated products,separation of ownership and control,private benefits of control

Pre-scheduled colony size variation in dynamic environments

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The performance of the MAX -MIN ant system (MMAS) in dynamic optimization problems (DOPs) is sensitive to the colony size. In particular, a large colony size may waste computational resources whereas a small colony size may restrict the searching capabilities of the algorithm. There is a trade off in the behaviour of the algorithm between the early and later stages of the optimization process. A smaller colony size leads to better performance on shorter runs whereas a larger colony size leads to better performance on longer runs. In this paper, pre-scheduling of varying the colony size of MMAS is investigated in dynamic environments