Multi-objective evolution strategy for multimodal multi-objective optimization

Zhang, Kai; Chen, Minshi; Xu, Xin; Yen, Gary G.

doi:10.1016/j.asoc.2020.107004

Cited by 43 publications

(7 citation statements)

References 24 publications

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“…Song et al [131] suggested strategies, such as adapting the size of subpopulations, that effectively solved problems with many features. In the reference [132], population diversity is preserved through the simulation of magnetic particles, which rapidly gravitate toward the Pareto front within the existing environment. Typically, strategies for maintaining diversity employ the Pareto-optimal solution set from the optimization issue in the previous environment as the initial population within the present environment.…”

Section: Strategy Methods Yearmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

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Section: Strategy Methods Yearmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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“…Optimal solution backtracking: After obtaining the value of the final state, the decision path of the optimal solution can be obtained by backtracking. [7]The backtracking process will be selected based on previously recorded state transitions. [8] Fig.…”

Section: Data Preparation and Environment Constructionmentioning

confidence: 99%

Research on University Budget Performance Evaluation Based on Dynamic Programming Algorithm

Wang,

Huang

2023

Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8–10, 20

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With the continuous development and reform of higher education system, budget management and performance evaluation of colleges and universities become increasingly important. The purpose of this study is to explore and propose a method of university budget performance evaluation based on dynamic programming algorithm in order to optimize resource allocation and improve performance management effect. Firstly, this paper summarizes the current situation of budget management and performance evaluation in colleges and universities, and emphasizes its key role in ensuring the sustainable development of colleges and universities and improving the quality of education.Then, this paper introduces the principle and application of dynamic programming algorithm in detail, and expounds its potential advantages in university budget performance evaluation. In the case of limited resources, the dynamic programming algorithm can find the optimal decision path and realize the reasonable allocation of budget, so as to improve the performance of universities to the greatest extent. Aiming at the uncertainty and multi-objective optimization of budget allocation, a performance evaluation model based on dynamic programming is proposed to comprehensively consider the indicators of resource utilization efficiency, education quality and economic benefit.

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“…Recently, The study [162] has introduced an eicient self-adaptive precision controllable mutation operator to either exploit or explore the search space, and a niching technique imitating isotopic magnetic particles to maintain diversity. Encouraging results are obtained on DMOPs when these new techniques are well coordinated [162].…”

Section: Diversity Increase/maintenancementioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This paper presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

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Multi-objective evolution strategy for multimodal multi-objective optimization

Cited by 43 publications

References 24 publications

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Research on University Budget Performance Evaluation Based on Dynamic Programming Algorithm

Evolutionary Dynamic Multi-objective Optimisation: A Survey

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