Multi-objective Optimization with Dynamic Constraints and Objectives

Azzouz, Radhia; Bechikh, Slim; Saïd, Lamjed Ben

doi:10.1145/2739480.2754708

Cited by 48 publications

(23 citation statements)

References 31 publications

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“…Consequently, the objective functions and constraints on these time-varying parameters make the optimal cooperative relay selection a challenging problem. The research work reported in [118] can be helpful for researchers while designing optimal cooperative relay selection solutions.…”

Section: Open Research Challengesmentioning

confidence: 99%

Cooperative Vehicular Networking: A Survey

Ahmed

Gharavi

2018

IEEE Trans. Intell. Transport. Syst.

223

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With the remarkable progress of cooperative communication technology in recent years, its transformation to vehicular networking is gaining momentum. Such a transformation has brought a new research challenge in facing the realization of cooperative vehicular networking (CVN). This paper presents a comprehensive survey of recent advances in the field of CVN. We cover important aspects of CVN research, including physical, medium access control, and routing protocols, as well as link scheduling and security. We also classify these research efforts in a taxonomy of cooperative vehicular networks. A set of key requirements for realizing the vision of cooperative vehicular networks is then identified and discussed. We also discuss open research challenges in enabling CVN. Lastly, the paper concludes by highlighting key points of research and future directions in the domain of CVN.

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Section: Open Research Challengesmentioning

confidence: 99%

Cooperative Vehicular Networking: A Survey

Ahmed

Gharavi

2018

IEEE Trans. Intell. Transport. Syst.

223

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“…Finally, for performance indicator‐based multicriteria evolutionary algorithms, surrogates have been trained to approximate the hypervolume contribution of a newly created individual in a steady‐state multicriteria evolutionary algorithm using an artificial neural network (Azzouz, Bechikh, & Ben Said, ).…”

Section: State‐of‐the‐art In Surrogate‐assisted Multicriteria Optimizmentioning

confidence: 99%

“…The EI criteria can be used in the selection of evolutionary algorithms (Giannakoglou, ; Emmerich, Beume, & Naujoks, , ; Azzouz, Bechikh & Ben Said, ) or as infill criterion in EGO (Knowles, ; Ponweiser, Wagner, Biermann, & Vincze, ; Shimoyama, Sato, Jeong, & Obayashi, ; Zaefferer et al, ). Efficient exact computation algorithms for the multivariate EI are discussed in the works of Couckuyt, Deschrijver, and Dhaene (); Hupkens, Deutz, Yang and Emmerich ().…”

Section: State‐of‐the‐art In Surrogate‐assisted Multicriteria Optimizmentioning

confidence: 99%

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Allmendinger

Emmerich

Hakanen

et al. 2017

J. Multi-Crit. Decis. Anal.

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Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization.For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers to approximate efficiently multiple objectives simultaneously. Thinking out of the box, complexity can also be shifted from approximating the individual objective functions to approximating the entire Pareto front. This leads to further complexities, namely, how to validate statistically and apply the techniques developed to real-world problems. In this paper, we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in nonsurrogate-assisted single-objective optimization. These complexities are motivated using several real-world problems in which the authors were involved. We then discuss several promising future research directions and prospective solutions to tackle emerging complexities in surrogate-assisted multicriteria optimization. Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment to tackle real-world problems.

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“…Nevertheless, both procedures are sensitive to the choice of the population ratio ζ and the change frequency. A new version of this algorithm was proposed in Azzouz et al (2015) to deal with dynamic constraints by replacing the used constraint-handling mechanism by a more elaborated and self-adaptive penalty function. Moreover, authors proposed a set of test problems that extended a suite of static constrained multi-objective problems.…”

Section: Diversity-based Approachesmentioning

confidence: 99%

A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

2015

Self Cite

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In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.Keywords Change severity sensing · Dynamic multiobjective optimization · Time-changing objective functions · Adaptive population management · Memory-based strategy · Local search-based strategy · Change severity-based strategy

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Multi-objective Optimization with Dynamic Constraints and Objectives

Cited by 48 publications

References 31 publications

Cooperative Vehicular Networking: A Survey

Cooperative Vehicular Networking: A Survey

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

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