A Tabu Search hyper-heuristic strategy for t-way test suite generation

This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks

Visualising Evolution History in Multi- and Many-Objective Optimisation

Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III

GALAXY: A new hybrid MOEA for the Optimal Design of Water Distribution Systems

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.The first author would like to appreciate the financial support given by both the University of Exeter and the China Scholarship Council (CSC) toward the PhD research. We also appreciate the three anonymous reviewers, who help improve the quality of this paper substantially. The source code of the latest versions of NSGA-II and ε-MOEA can be downloaded from the official website of Kanpur Genetic Algorithms Laboratory via http://www.iitk.ac.in/kangal/codes.shtml. The description of each benchmark problem used in this paper, including the input file of EPANET and the associated best-known Pareto front, can be accessed from the following link to the Centre for Water Systems (http://tinyurl.com/cwsbenchmarks/). GALAXY can be accessed via http://tinyurl.com/cws-galaxy

Heat waves occurrence and outdoor workers’ self-assessment of heat stress in slovenia and greece

Changing patterns of heat waves are part of the global warming effect and the importance of changes is reinforced by their negative impact on society. Firstly, heat waves were analyzed in Brnik (Slovenia) and Larisa (Greece) in the period 1981–2017 to reflect the environment which workers are exposed to. Secondly, outdoor workers (70 from Greece, 216 from Slovenia) provided a self-assessment of heat stress. The heat wave timeline is presented as an effective way of illustrating long-term changes in heat waves’ characteristics for various stakeholders. In both countries, workers assessed as significant the heat stress impact on productivity (Greece 69%, Slovenia 71%; p > 0.05), and in Slovenia also on well-being (74%; p 0.05) and headache (44%, 53%; p > 0.05). The most common way to reduce heat stress was drinking more water (Greece 64%, Slovenia 82%; p = 0.001). Among the informed workers, the prevalent source of information was discussions. Therefore, educational campaigns are recommended, together with the testing of the efficiency of mitigation measures that will be proposed on the Heat-Shield project portal. © 2019 by the authors. Licensee MDPI, Basel, Switzerland