Robust Optimization Over Time by Learning Problem Space Characteristics

Yazdani, Danial; Nguyễn, Trung Thành; Branke, Jürgen

doi:10.1109/tevc.2018.2843566

Cited by 40 publications

(28 citation statements)

References 30 publications

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“…In many real-world applications, changing the production solution introduces additional cost [64]. Thus, we will borrow the ideas proposed in [57] to design a general framework that can find robust solutions. In addition, there are other types of changes in the true POF and the feasible region of objective functions, which we will study in the future.…”

Section: Discussionmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

Chen

Ding

Yang

et al. 2020

IEEE Trans. Evol. Computat.

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Abstract-To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. Experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms. Index Terms-Dynamic constrained multiobjective optimization, test problems, population selection, change response I. INTRODUCTION ANY real-world optimization problems, particularly online optimization problems or optimal control problems, involve multiple objectives and constraints that may change over time throughout the optimization [1]; such problems can be called dynamic constrained multiobjective optimization problems (DCMOPs). Without loss of generality,

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Section: Discussionmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

Chen

Ding

Yang

et al. 2020

IEEE Trans. Evol. Computat.

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“…Thus, it is necessary to detect environmental changes, or get informed about them. Since detecting environmental changes is a separate issue, in this paper, it is assumed that the algorithms are informed about environmental changes which is the case of many real-world DOPs [10], [38]. Section S-V of the supplementary document discusses the change detection mechanisms and incorporates a representative one into the ACF.…”

Section: E Environmental Changes and Acf Reactionsmentioning

confidence: 99%

Adaptive Control of Subpopulations in Evolutionary Dynamic Optimization

Yazdani

Cheng

et al. 2022

IEEE Trans. Cybern.

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“…The tracking moving optima with adaptive number of subpopulations and the exclusion mechanisms used in this paper are based on AMQSO. For diversification however, we use a simple random sampling mechanism around the best solution immediately prior to an environment change [40], [58], [59].…”

Section: Tracking Moving Optimummentioning

confidence: 99%

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Yazdani

Omidvar

Branke

et al. 2020

IEEE Trans. Evol. Computat.

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Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of largescale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decompositionbased coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bilevel resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems.

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Robust Optimization Over Time by Learning Problem Space Characteristics

Cited by 40 publications

References 30 publications

A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

Adaptive Control of Subpopulations in Evolutionary Dynamic Optimization

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

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