Browsing by Author "Krasnogor, Natalio"

Now showing 1 - 5 of 5
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    AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation
    (Elsevier, 2019-12-05) Jiang, Shouyong; Li, Hongru; Guo, Jinglei; Zhong, Mingjun; Yang, Shengxiang; Kaiser, Marcus; Krasnogor, Natalio
    Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, the performance of these algorithms depends largely on problem characteristics. There is a need to improve these algorithms for wide applicability. References, often specified by the decision maker’s preference in different forms, are very effective to boost the performance of algorithms. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state-of-the-arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper.
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    Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization
    (Newcastle University, 2018-01) Jiang, Shouyong; Yang, Shengxiang; Yao, Xin; Tan, Kay Chen; Kaiser, Marcus; Krasnogor, Natalio
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    An empirical study of dynamic triobjective optimisation problems
    (IEEE Press, 2018-07) Jiang, Shouyong; Kaiser, Marcus; Wan, Shuzhen; Guo, Jinglei; Yang, Shengxiang; Krasnogor, Natalio
    Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.
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    Less detectable environmental changes in dynamic multiobjective optimisation
    (ACM Press, 2018-05-04) Jiang, Shouyong; Kaiser, Marcus; Guo, Jinglei; Yang, Shengxiang; Krasnogor, Natalio
    Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms.
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    A scalable test suite for dynamic multiobjective optimization
    (IEEE, 2019-02-15) Jiang, Shouyong; Kaiser, Marcus; Yang, Shengxiang; Kollias, Stefanos; Krasnogor, Natalio
    Dynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more important, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features is then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite is more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot.