Enhanced <i>Innovized</i> Progress Operator for Evolutionary Multi- and Many-Objective Optimization

Mittal, Sukrit; Saxena, Dhish Kumar; Deb, Kalyanmoy; Goodman, Erik D.

doi:10.1109/tevc.2021.3131952

Cited by 23 publications

(4 citation statements)

References 54 publications

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“…A learning-assisted optimization method called innovized progress is an alternative approach that uses machine-learning methods, such as an artificial neural network and a random forest [89,90]. This approach builds a model based on the search history and progresses the offspring solutions using the model.…”

Section: Approachesmentioning

confidence: 99%

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Satō

Ishibuchi

2023

IEEJ Transactions Elec Engng

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Population‐based evolutionary algorithms are suitable for solving multi‐objective optimization problems involving multiple conflicting objectives. This is because a set of well‐distributed solutions can be obtained by a single run, which approximate the optimal tradeoff among the objectives. Over the past three decades, evolutionary multi‐objective optimization has been intensively studied and used in various real‐world applications. However, evolutionary multi‐objective optimization faces various difficulties as the number of objectives increases. The simultaneous optimization of more than three objectives, which is called many‐objective optimization, has attracted considerable research attention. This paper explains various difficulties in evolutionary many‐objective optimization, reviews representative approaches, and discusses their effects and limitations. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Approachesmentioning

confidence: 99%

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Satō

Ishibuchi

2023

IEEJ Transactions Elec Engng

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“…However, the selective pressure of population is directly related to the number of nondominant solutions in a population. 40 In general, in the target space, the proportion of the nondominated region is expressed…”

Section: Challenges Facing Many-objective Optimization Algorithmmentioning

confidence: 99%

“…Such algorithms need to maintain certain population selection pressure in evolutionary process to promote population search toward PF. However, the selective pressure of population is directly related to the number of nondominant solutions in a population 40 . In general, in the target space, the proportion of the nondominated region is expressed as

k=\left({2}^m-2\right)/{2}^m

.…”

Section: Problem Descriptionmentioning

confidence: 99%

A many‐objective optimization algorithm with dual criteria and mixed distribution correction strategy

Zhang

Cai

et al. 2023

Concurrency and Computation

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SummaryIn many‐objective optimization algorithms, it is very important to maintain significant convergence and diversity of the population. And with the increasing demand in various fields, the optimization problem also becomes gradually complicated. Some existing many‐objective optimization algorithms are faced with challenges such as domination resistance and dimensional crisis. To solve these challenges, a many‐objective optimization algorithm based on dual criteria and mixed distribution correction strategy (MaOEA‐CSMDC) is proposed in this paper. To be specific, a matching selection strategy based on dual criteria combined by pareto domination strategy and achievement scalar function, which alleviates the domination resistance phenomenon and enhances the selection pressure of the algorithm. After that, an environment selection strategy based on equal probability mixed distribution correction is designed to better balance convergence and diversity. In this strategy, normal distribution, exponential distribution, and Cauchy distribution are introduced to adjust the weight of convergence and diversity in evolution by means of equal probability, so as to alleviate the problem that the conflict between them is intensified in the later stage of the algorithm. The experimental results show that, MaOEA‐CSMDC not only has advantages in convergence and diversity indicators, but also is more competitive in solving many‐objective optimization problems.

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“…In both papers, the classifier was used before the solutions were evaluated, in order to save time by not evaluating poor solutions. Recently, approaches that use innovization online have also been proposed [30,31].…”

Section: Online Kdomentioning

confidence: 99%

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Smedberg

Díaz

Nourmohammadi

et al. 2022

MCA

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Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today’s manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

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Enhanced Innovized Progress Operator for Evolutionary Multi- and Many-Objective Optimization

Cited by 23 publications

References 54 publications

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

A many‐objective optimization algorithm with dual criteria and mixed distribution correction strategy

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

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