A Multiobjective Framework for Many-Objective Optimization

Liu, Sichen; Zhan, Zhi-Hui; Tan, Kay Chen; Zhang, Mengjie

doi:10.1109/tcyb.2021.3082200

Cited by 36 publications

(18 citation statements)

References 66 publications

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“…For future work, the proposed algorithm will be further extended to solving more difficult and complex MTOPs, such as not only in complex continuous space [54]- [56], but also in complex discrete [57]- [60], combinational [61]- [64], and mix-variable space [65]- [67]. Furthermore, as the MKT is a generic idea, further exploration of other kinds of meta-knowledge and other meta-knowledge transfer methods and utilization methods are worthy studied to obtain more powerful EMTO algorithms.…”

Section: Discussionmentioning

confidence: 99%

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

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Abstract-Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge such as the high-quality solution from one task to other tasks to enhance the optimization ability, which however may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this paper proposes a meta-knowledge transfer-based differential evolution (MKTDE) algorithm by using a more general meta-knowledge transfer (MKT) method to solve MTOPs more efficiently. The meta-knowledge defined in this paper refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of "knowledge of knowledge", which denotes the knowledge of "how to solve problem via evolution" and "the feature of evolving high-quality solution". The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multi-source data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is a multiple populations for multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely-used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms.

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Section: Discussionmentioning

confidence: 99%

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

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“…The experimental results have shown the great effectiveness and efficiency of the proposed MO-MCEA, showing that treating MO-MTOP as MO-MCOP can be a potential way for solving MO-MTOP more efficiently. For future work, the MO-MCEA, including the PCSS and APL, will be further improved and extended to solve more difficult MO-MTOPs (e.g., MO-MTOPs with different similarities and intersections) and more complex real-world MO-MTOPs, such as those are also with other difficult characteristics in data-driven optimization problems [54,55], expensive optimization problems [56][57][58], multi-modal optimization problems [59], large-scale optimization problems [60,61], and many-objective optimization problems [62,63].…”

Section: Discussionmentioning

confidence: 99%

Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization

Wang

et al. 2022

Complex Intell. Syst.

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Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problem (MO-MTOP) using evolutionary computation. However, most existing methods tend to directly treat the multiple multi-objective tasks as different problems and optimize them by different populations, which face the difficulty in designing good knowledge transferring strategy among the tasks/populations. Different from existing methods that suffer from the difficult knowledge transfer, this paper proposes to treat the MO-MTOP as a multi-objective multi-criteria optimization problem (MO-MCOP), so that the knowledge of all the tasks can be inherited in a same population to be fully utilized for solving the MO-MTOP more efficiently. To be specific, the fitness evaluation function of each task in the MO-MTOP is treated as an evaluation criterion in the corresponding MO-MCOP, and therefore, the MO-MCOP has multiple relevant evaluation criteria to help the individual selection and evolution in different evolutionary stages. Furthermore, a probability-based criterion selection strategy and an adaptive parameter learning method are also proposed to better select the fitness evaluation function as the criterion. By doing so, the algorithm can use suitable evaluation criteria from different tasks at different evolutionary stages to guide the individual selection and population evolution, so as to find out the Pareto optimal solutions of all tasks. By integrating the above, this paper develops a multi-objective multi-criteria evolutionary algorithm framework for solving MO-MTOP. To investigate the proposed algorithm, extensive experiments are conducted on widely used MO-MTOPs to compare with some state-of-the-art and well-performing algorithms, which have verified the great effectiveness and efficiency of the proposed algorithm. Therefore, treating MO-MTOP as MO-MCOP is a potential and promising direction for solving MO-MTOP.

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“…In recent years, MaOPs have attracted wide attention [34]- [36]. In general, a minimization MaOP can be formulated as…”

Section: A Maopmentioning

confidence: 99%

Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Liu

Chen

Zhan

et al. 2023

IEEE Trans. Cybern.

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The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) frameworkbased genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy

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A Multiobjective Framework for Many-Objective Optimization

Cited by 36 publications

References 66 publications

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization

Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

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