Browsing by Author "Luo, Wenjian"

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    Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization
    (2024-02) Luo, Wenjian; Xu, Peilan; Yang, Shengxiang; Shi, Yuhui
    The competition focuses on Multiparty Multiobjective Optimization Problems (MPMOPs), where multiple decision makers have conflicting objectives, as seen in applications like UAV path planning. Despite their importance, MPMOPs remain understudied in comparison to conventional multiobjective optimization. The competition aims to address this gap by encouraging researchers to explore tailored modeling approaches. The test suite comprises two parts: problems with common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning (BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the first part are evaluated using Multiparty Inverted Generational Distance (MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV) metrics. The average algorithm ranking across all problems serves as a performance benchmark.
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    Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments
    (2022-01) Luo, Wenjian; Lin, Xin; Li, Changhe; Yang, Shengxiang; Shi, Yuhui
    Dynamic and multimodal features are two important properties and widely existed in many real-world optimization problems. The former illustrates that the objectives and/or constraints of the problems change over time, while the latter means there is more than one optimal solution (sometimes including the accepted local solutions) in each environment. The dynamic multimodal optimization problems (DMMOPs) have both of these characteristics, which have been studied in the field of evolu tionary computation and swarm intelligence for years, and attract more and more attention. Solving such problems requires optimization algorithms to simultaneously track multiple optima in the changing environments. So that the decision makers can pick out one optimal solution in each environment according to their experiences and preferences, or quickly turn to other solutions when the current one cannot work well. This is very helpful for the decision makers, especially when facing changing environments. In this competition, a test suit about DMMOPs is given, which models the real-world applications. Specifically, this test suit adopts 8 multimodal functions and 8 change modes to construct 24 typical dynamic multimodal optimization problems. Meanwhile, the metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments. This competition will be very helpful to promote the development of dynamic multimodal optimization algorithms.
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    Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
    (2023-12) Yazdani, Danial; Mavrovouniotis, Michalis; Li, Changhe; Luo, Wenjian; Omidvar, Mohammad Nabi; Gandomi, Amir H.; Nguyen, Trung Thanh; Branke, Juergen; Li, Xiaodong; Yang, Shengxiang; Yao, Xin
    This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization. GMPB is adept at generating landscapes with a broad spectrum of characteristics, offering everything from unimodal to highly multimodal landscapes and ranging from symmetric to highly asymmetric configurations. The landscapes also vary in texture, from smooth to highly irregular surfaces, encompassing diverse degrees of variable interaction and conditioning. This document delves into the intricacies of GMPB, detailing the myriad ways in which its parameters can be tuned to produce these diverse landscape characteristics. GMPB's MATLAB implementation is available on the EDOLAB Platform.
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    An evolutionary approach to multiparty multiobjective optimization problems with common Pareto optimal solutions
    (IEEE Press, 2020-07-19) Liu, Wenjie; Luo, Wenjian; Lin, Xin; Li, Miqing; Yang, Shengxiang
    Somereal-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multiparty multiobjective optimization problems (MPMOPs). Although evolutionary multiobjective optimization has been widely studied for many years, little attention has been paid to multiparty multiobjective optimization in the field of evolutionary computation. In this paper, a class of MPMOPs, that is, MPMOPs having common Pareto optimal solutions, is addressed. A benchmark for MPMOPs, obtained by modifying an existing dynamic multiobjective optimization benchmark, is provided, and a multiparty multiobjective evolutionary algorithm to find the common Pareto optimal set is proposed. The results of experiments conducted using the benchmark show that the proposed multiparty multiobjective evolutionary algorithm is effective.
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    PopDMMO: A general framework of population-based stochastic search algorithms for dynamic multimodal optimization
    (Elsevier, 2021-11-12) Lin, Xin; Luo, Wenjian; Xu, Peilan; Qiao, Yingying; Yang, Shengxiang
    Dynamic multimodal optimization problems (DMMOPs) are a class of problems consisting of two characteristics, i.e., dynamic and multimodal natures. The former characteristic reveals that the properties of DMMOPs change over time, which is derived from dynamic optimization problems (DOPs). The latter one indicates that there exist multiple global or acceptable local optima, which comes from the multimodal optimization problems (MMOPs). Although there has been much attention to both DOPs and MMOPs in the field of meta-heuristics, there is little work devoting to the connection between the dynamic and multimodal natures in DMMOPs. To solve DMMOPs, the strategies dealing with dynamic and multimodal natures in the algorithms should cooperate with each other. Before looking deeply into the connections between two natures, there is necessary to measure the performances of the methods dealing with two natures in DMMOPs. In this paper, first, considering the dynamic and multimodal natures of DMMOPs, we design a set of benchmark problems to simulate various dynamic and multimodal environments. Then, we propose the optimization framework called PopDMMO containing several popular algorithms and methods to test and compare the performances of these algorithms, which gives a general view of solving DMMOPs.