Integrating user preferences and decomposition methods for many-objective optimization

Mohammadi, Asad; Omidvar, Mohammad Nabi; Li, Xiaodong; Deb, Kalyanmoy

doi:10.1109/cec.2014.6900595

Cited by 40 publications

(39 citation statements)

References 27 publications

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“…Therefore, a fixed setting of direction lines may not efficiently solve the problem. A popular and commonly used approach for dealing with this issue in the literature is to dynamically adjust the direction vectors so that the resultant optimal solutions can approximate the PF well [12]- [14], [21], [22]. However, adjusting or resetting all the direction vectors at the same time is not always an easy task.…”

Section: Motivationsmentioning

confidence: 99%

On the use of two reference points in decomposition based multiobjective evolutionary algorithms

Wang

Zhang

et al. 2017

Swarm and Evolutionary Computation

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Decomposition based multiobjective evolutionary algorithms approximate the Pareto front of a multiobjective optimization problem by optimizing a set of subproblems in a collaborative manner. Often, each subproblem is associated with a direction vector and a reference point. The settings of these parameters have a very critical impact on convergence and diversity of the algorithm. Some work has been done to study how to set and adjust direction vectors to enhance algorithm performance for particular problems. In contrast, little effort has been made to study how to use reference points for controlling diversity in decomposition based algorithms. In this paper, we first study the impact of the reference point setting on selection in decomposition based algorithms. To balance the diversity and convergence, a new variant of the multiobjective evolutionary algorithm based on decomposition with both the ideal point and the nadir point is then proposed. This new variant also employs an improved global replacement strategy for performance enhancement. Comparison of our proposed algorithm with some other state-of-theart algorithms is conducted on a set of multiobjective test problems. Experimental results show that our proposed algorithm is promising.

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Section: Motivationsmentioning

confidence: 99%

On the use of two reference points in decomposition based multiobjective evolutionary algorithms

Wang

Zhang

et al. 2017

Swarm and Evolutionary Computation

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“…In [62], Ma et al have proposed to apply the light beam search (LBS) [63] in MOEA/D to incorporate user preferences, where the preference information is specified by an aspiration point and a reservation point, together with a preference neighborhood parameter. Most recently, Mohammadi et al have also proposed to integrate user preferences for manyobjective optimization [64], where the preferred region is specified by a hypercube. These methods try to define some preferred regions and generate weight vectors inside them to guide the search of the MOEAs.…”

Section: A Decomposition Based Moeasmentioning

confidence: 99%

A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

Cheng

Jin

Olhofer

et al. 2016

IEEE Trans. Evol. Computat.

1,355

697

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Abstract-In evolutionary multi-objective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms. In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto optimal solutions using a limited population size as the number of objectives increases. This paper proposes a reference vector guided evolutionary algorithm for many-objective optimization. The reference vectors can not only be used to decompose the original multiobjective optimization problem into a number of single-objective sub-problems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front. In the proposed algorithm, a scalarization approach, termed angle penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the reference vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art evolutionary algorithms for many-objective optimization. In addition, we show that reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization. Furthermore, a reference vector regeneration strategy is proposed for handling irregular Pareto fronts. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.

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“…The MACE-gD, designed in the framework of generalized decomposition [62], is able to guide the search towards specified ROIs according to DM's preferences, where the preference information is articulated with a set of weight vectors obtained by solving an inverse problem. In [60], a reference point based MOEA (R-MEAD2) has been proposed. In R-MEAD2, a reference point is initialized together with a set of uniformly distributed weight vectors over the whole objective space.…”

Section: A Related Workmentioning

confidence: 99%

Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

Cheng

Rodemann

Fischer

et al. 2017

IEEE Trans. Emerg. Top. Comput. Intell.

106

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Abstract-Many real-world optimization problems have more than three objectives, which has triggered increasing research interest in developing efficient and effective evolutionary algorithms for solving many-objective optimization problems. However, most many-objective evolutionary algorithms have only been evaluated on benchmark test functions and few applied to real-world optimization problems. To move a step forward, this paper presents a case study of solving a many-objective hybrid electric vehicle controller design problem using three state-of-the-art algorithms, namely, a decomposition based evolutionary algorithm (MOEA/D), a non-dominated sorting based genetic algorithm (NSGA-III), and a reference vector guided evolutionary algorithm (RVEA). We start with a typical setting aiming at approximating the Pareto front without introducing any user preferences. Based on the analyses of the approximated Pareto front, we introduce a preference articulation method and embed it in the three evolutionary algorithms for identifying solutions that the decision-maker prefers. Our experimental results demonstrate that by incorporating user preferences into many-objective evolutionary algorithms, we are not only able to gain deep insight into the trade-off relationships between the objectives, but also to achieve high-quality solutions reflecting the decision-maker's preferences. In addition, our experimental results indicate that each of the three algorithms examined in this work has its unique advantages that can be exploited when applied to the optimization of real-world problems.

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Integrating user preferences and decomposition methods for many-objective optimization

Cited by 40 publications

References 27 publications

On the use of two reference points in decomposition based multiobjective evolutionary algorithms

On the use of two reference points in decomposition based multiobjective evolutionary algorithms

A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

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