The ‘Light Beam Search’ approach – an overview of methodology applications

Jaszkiewicz, Andrzej; Słowiński, Roman

doi:10.1016/s0377-2217(98)00218-5

Cited by 158 publications

(67 citation statements)

References 22 publications

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“…This region is obtained by the notion of outranking (S) [10]. a outranks b (denoted by aSb) if a is considered to be at least as good as b within some threshold value.…”

Section: Light Beam Search Methodsmentioning

confidence: 99%

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

Wickramasinghe

2009

AI 2009: Advances in Artificial Intelligence

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Abstract. In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. In a user-preference based algorithm a decision maker indicates regions of the objective-space of interest, the algorithm then concentrates only on those regions to find solutions. Existing user-preference based evolutionary many-objective algorithms rely on the use of dominance comparisons to explore the search-space. Unfortunately, this is ineffective and computationally expensive for many-objective problems. The proposed distance metric allows an evolutionary many-objective algorithm's search to be focused on the preferred regions, saving substantial computational cost. We demonstrate how to incorporate the proposed distance metric with a user-preference based genetic algorithm, which implements the reference point and light beam search methods. Experimental results suggest that the distance metric based algorithm is effective and efficient, especially for difficult many-objective problems.

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“…This region is obtained by the notion of outranking (S) [10]. a outranks b (denoted by aSb) if a is considered to be at least as good as b within some threshold value.…”

Section: Light Beam Search Methodsmentioning

confidence: 99%

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

Wickramasinghe

2009

AI 2009: Advances in Artificial Intelligence

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“…[6,8,22,30]). Many interactive methods can be easily enhanced by adding such possibility and thereby be fit into the structure described above.…”

Section: Algorithmmentioning

confidence: 99%

“…Few interactive methods include this possibility (see e.g. [8,22]) and to our knowledge, none of the papers describe the selection process in detail. The issue to be addressed here is high cognitive load placed on the DM when dealing with many-objective problem and a large number of accumulated solutions.…”

Section: Introductionmentioning

confidence: 99%

A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

Filatovas

Podkopaev

Kurasova

2015

INT J COMPUT COMMUN

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Interactive methods of multiobjective optimization repetitively derive Pareto optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the Pareto optimal set and learning about the optimization problem. However, in the case of many objective functions, the accumulation of derived solutions makes accessing the solution pool cognitively difficult for the decision maker. We propose to enhance interactive methods with visualization of the set of solution outcomes using dimensionality reduction and interactive mechanisms for exploration of the solution pool. We describe a proposed visualization technique and demonstrate its usage with an example problem solved using the interactive method NIMBUS.

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“…The light beam search was first introduced by Jaszkiewicz and Slowinski [10]. The DM first needs to indicate two points in the objective-space, the Aspiration Point (AP), denoted by z r and the Reservation Point (RP), denoted by z v .…”

Section: The Light Beam Searchmentioning

confidence: 99%

“…To demonstrate the effectiveness of the proposed distance metric, in this paper we use two user-preference methods, one being the reference point method [18] and the other being the light beam search [10]. The distance metric is incorporated into an existing multi-objective PSO, the Multi-objective Differential Evolution and PSO (MDEPSO) algorithm [21].…”

Section: Introductionmentioning

confidence: 99%

Using a distance metric to guide PSO algorithms for many-objective optimization

Wickramasinghe

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for manyobjective problems. We use a particle swarm optimization (PSO) algorithm as a baseline to demonstrate the usefulness of this distance metric, though the metric can be used in conjunction with any evolutionary multi-objective (EMO) algorithm. Existing user-preference based EMO algorithms rely on the use of dominance comparisons to explore the searchspace. Unfortunately, this is ineffective and computationally expensive for many-objective problems. In the proposed distance metric based PSO, particles update their positions and velocities according to their closeness to preferred regions in the objective-space, as specified by the decision maker. The proposed distance metric allows an EMO algorithm's search to be more effective especially for many-objective problems, and to be more focused on the preferred regions, saving substantial computational cost. We demonstrate how to use a distance metric with two user-preference based PSO algorithms, which implement the reference point and light beam search methods. These algorithms are compared to a userpreference based PSO algorithm relying on the conventional dominance comparisons. Experimental results suggest that the distance metric based algorithms are more effective and efficient especially for difficult many-objective problems.

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The ‘Light Beam Search’ approach – an overview of methodology applications

Cited by 158 publications

References 22 publications

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

Using a distance metric to guide PSO algorithms for many-objective optimization

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