Evolutionary multitasking in bi-level optimization

Gupta, Abhishek; Mańdziuk, Jacek; Ong, Yew-Soon

doi:10.1007/s40747-016-0011-y

Cited by 92 publications

(36 citation statements)

References 27 publications

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“…The superiority of ENS-SS over the generalized Jensen's sort in runtime may be partly attributed to the fact that ENS-SS holds a space complexity of O (1), whereas the space complexity of generalized Jensen's sort is O(N ). It is worth noting that deductive sort also achieves a competitive efficiency on random populations in terms of runtime despite that it uses more objective comparisons.…”

Section: Efficient Non-dominated Sorting For Multi-objective Optimizamentioning

confidence: 99%

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Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Tian

Wang

Zhang

et al. 2017

Complex Intell. Syst.

104

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Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi-and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of nondominated sorting in multi-and many-objective evolutionary algorithms. The effectiveness of non-dominated sorting is verified by considering two popular evolutionary algorithms, NSGA-II and KnEA, which were designed for solving multiand many-objective optimization problems, respectively. The efficiency of non-dominated sorting is evaluated by comparing several state-of-the-art non-dominated sorting algorithms for multi-and many-objective optimization problems. These results provide important insights to adopt non-dominated sorting in developing novel multi-and many-objective evolutionary algorithms.

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Section: Efficient Non-dominated Sorting For Multi-objective Optimizamentioning

confidence: 99%

“…Multi-objective optimization problems (MOPs) refer to those consisting of multiple contradictory objectives to be optimized simultaneously, which widely exist in real-world applications [1][2][3][4][5]. MOPs with more than three objectives are also called many-objective optimization problems (MaOPs) [6].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Tian

Wang

Zhang

et al. 2017

Complex Intell. Syst.

104

View full text Add to dashboard Cite

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“…Based on the Darwinian theorem of "Survival of the Fittest" (Dawkins, 2006;Ma et al, 2014a), the population-based evolutionary algorithms (EAs) have been successfully used to solve a wide range of optimization problems (Deb, 2001;Qi et al, 2014;Ma et al, 2018). Multitasking optimization (MTO) problems have emerged as a new interest in the area of evolutionary computation Gupta et al, 2016a;Ong and Gupta, 2016;Yuan et al, 2016). Inspired by the ability of human beings to process multiple tasks at the same time, MTO aims at dealing with different optimization tasks simultaneously within a single solution framework.…”

Section: Introductionmentioning

confidence: 99%

A Two-Level Transfer Learning Algorithm for Evolutionary Multitasking

Chen

et al. 2020

Front. Neurosci.

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Different from conventional single-task optimization, the recently proposed multitasking optimization (MTO) simultaneously deals with multiple optimization tasks with different types of decision variables. MTO explores the underlying similarity and complementarity among the component tasks to improve the optimization process. The well-known multifactorial evolutionary algorithm (MFEA) has been successfully introduced to solve MTO problems based on transfer learning. However, it uses a simple and random inter-task transfer learning strategy, thereby resulting in slow convergence. To deal with this issue, this paper presents a two-level transfer learning (TLTL) algorithm, in which the upper-level implements inter-task transfer learning via chromosome crossover and elite individual learning, and the lower-level introduces intra-task transfer learning based on information transfer of decision variables for an across-dimension optimization. The proposed algorithm fully uses the correlation and similarity among the component tasks to improve the efficiency and effectiveness of MTO. Experimental studies demonstrate the proposed algorithm has outstanding ability of global search and fast convergence rate.

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“…By transferring positive knowledge across tasks, MFEAs is effectiveness in exploring superior solutions of MFO problems due to problems are seldom isolated and implicitly related to each other. MFEAs have been successfully applied to many real-world problems, e.g., knapsack problems [30], rigid-tool liquid composite molding processes [31], capacitate vehicle routing problem [32], [33], bi-level optimization problems [34], expensive computational problems [35].…”

Section: Introductionmentioning

confidence: 99%

“…Thereafter, these two types of variables undergo assortative mating independently by using different random mating probability to generate the integral offspring to enhance both the convergence and diversity in solving multi-objective MFO problems. In the literature of multi-tasking optimization, all tasks in MFO problems are often treated equally [30]- [34]. However, the developed multifactorial operational indices optimization problem involves three optimization tasks, and only the accurate complex model receives the most attention among the models.…”

Section: Introductionmentioning

confidence: 99%

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Yang

Ding

Jin

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Abstract-Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multi-tasking multi-objective evolutionary method to solve operational indices optimization, which involves a formulated multi-objective multifactorial operational indices optimization problem (MO-MFO) and a proposed multi-objective multifactorial optimization algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a dataset collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the multifactorial optimization algorithm, the assistant models are alternatively in multi-tasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multi-objective multifactorial optimization algorithm is applied to transfer knowledge among multi-tasking tasks. The proposed multi-tasking framework for operational indices optimization has conducted on 10 different production Conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem.Note to Practitioners−Operational indices optimization is a typical method to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multi-objective multi-tasking framework is developed to address the operational indices optimization, which includes a multi-taking multi-objective operational indices optimization problem formulation and a multi-taking multiobjective evolutionary optimization to solve the above formulated optimization problem. The proposed approach can achieve a solution set for the decision making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associate with a higher grade and yield of the product.

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Evolutionary multitasking in bi-level optimization

Cited by 92 publications

References 27 publications

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

A Two-Level Transfer Learning Algorithm for Evolutionary Multitasking

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

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