Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach

Allmendinger, Richard; Li, Xiaodong; Branke, Jürgen

doi:10.1007/978-3-540-89694-4_21

Cited by 21 publications

(11 citation statements)

References 15 publications

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“…[29] developed a method to convert the fuzzy linguistic preference information into an interval-based weighting scheme where the weights is perturbed between the pre-defined upper and lower bounds. By transforming the MOP into a series of single-objective aggregation functions, e.g., weighted sum, it guides the population [38] objective functions region × [39] quality indicator distribution × × [40] quality indicator region × Aspiration level vector [41] dominance region × × × [42,43] leader selection strategy region × [17,44,45] density estimation region [46] dominance region × × [47] dominance region [27,[48][49][50] weight vector region towards the ROI. It is worth noting that the weight-based methods become ineffective when facing a large number of objectives.…”

Section: Related Workmentioning

confidence: 99%

Integration of Preferences in Decomposition Multiobjective Optimization

Chen

Min

et al. 2018

IEEE Trans. Cybern.

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Rather than a whole Pareto-optimal front, which demands too many points (especially in a high-dimensional space), the decision maker (DM) may only be interested in a partial region, called the region of interest (ROI). In this case, solutions outside this region can be noisy to the decision-making procedure. Even worse, there is no guarantee that we can find the preferred solutions when tackling problems with complicated properties or many objectives. In this paper, we develop a systematic way to incorporate the DM's preference information into the decomposition-based evolutionary multiobjective optimization methods. Generally speaking, our basic idea is a nonuniform mapping scheme by which the originally evenly distributed reference points on a canonical simplex can be mapped to new positions close to the aspiration-level vector supplied by the DM. By this means, we are able to steer the search process toward the ROI either directly or interactively and also handle many objectives. Meanwhile, solutions lying on the boundary can be approximated as well given the DM's requirements. Furthermore, the extent of the ROI is intuitively understandable and controllable in a closed form. Extensive experiments on a variety of benchmark problems with 2 to 10 objectives, fully demonstrate the effectiveness of our proposed method for approximating the preferred solutions in the ROI.

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Section: Related Workmentioning

confidence: 99%

Integration of Preferences in Decomposition Multiobjective Optimization

Chen

Min

et al. 2018

IEEE Trans. Cybern.

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“…4.Those based on goals or aspiration levels to be achieved by each objective (reference point) (e.g. [36,50,51,52,53,54]). 5.Those in which the DM identifies acceptable trade-offs between objective functions (e.g.…”

Section: A Brief Outline and Some Criticisms Of Previous Approachesmentioning

confidence: 99%

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

Cruz¹,

Fernández²,

Gómez³

et al. 2014

Appl. Math. Inf. Sci.

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Project portfolio selection is one of the most important problems faced by any organization. The decision process involves multiple conflicting criteria, and has been commonly addressed by implementing a two-phase procedure. The first step identifies the efficient solution set; the second step supports the decision maker in selecting only one portfolio solution from the efficient set. However, several recent studies show the advantages gained by optimizing towards a region of interest (according to the decision maker's preferences) instead of approximating the complete Pareto set. However, these works have not faced synergism and its variants, such as cannibalization and redundancy. In this paper we introduce a new approach called Non-Outranked Ant Colony Optimization, which optimizes interdependent project portfolios with a priori articulation of decision-maker preferences based on an outranking model. Several experimental tests show the advantages of our proposal over the two-phase approach, providing reasonable evidence of its potential for solving real-world high-scale problems with many objectives.

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“…In the work of Allmendinger et al . (), a multiobjective particle swarm optimization algorithm is merged with a reference point‐based approach as well. The main idea of this approach is to integrate the preferences when choosing the leader.…”

Section: State Of the Artmentioning

confidence: 99%

“…Instead of using the minimum distance, the objective variance of the solutions is tracked, and the EMOA is terminated when this variance drops below a specified threshold. In the work of Allmendinger et al (2008), a multiobjective particle swarm optimization algorithm is merged with a reference point-based approach as well. The main idea of this approach is to integrate the preferences when choosing the leader.…”

Section: State Of the Artmentioning

confidence: 99%

Indicator‐based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index

Trautmann

Wagner

Biermann

et al. 2013

Multi Criteria Decision Anal

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In multiobjective optimization, the identification of practically relevant solutions on the Pareto‐optimal front is an important research topic. Desirability functions (DFs) allow the preferences of the decision maker to be specified in an intuitive way. Recently, it has been shown for continuous optimization problems that an a priori transformation of the objectives by means of DFs can be used to focus the search of a hypervolume‐based evolutionary algorithm on the desired part of the front. In many‐objective optimization, however, the computational complexity of the hypervolume can become a crucial part. Thus, an alternative to this approach will be presented in this paper. The new algorithm operates in the untransformed objective space, but the desirability index (DI), that is, a DF‐based scalarization, will be used as the second‐level selection criterion in the non‐dominated sorting. The diversity and uniform distribution of the resulting approximation are ensured by the use of an external archive. In the experiments, different preferences are specified as DFs, and their effects are investigated. It is shown that trade‐off solutions are generated in the desired regions of the Pareto‐optimal front and with a density adaptive to the DI. The efficiency of the approach with respect to increasing objective space dimension is also analysed using scalable test functions. The convergence speed is superior to other set‐based and preference‐based evolutionary multiobjective algorithms while the approach is of low computational complexity due to cheap DI evaluations. Copyright © 2013 John Wiley & Sons, Ltd.

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Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach

Cited by 21 publications

References 15 publications

Integration of Preferences in Decomposition Multiobjective Optimization

Integration of Preferences in Decomposition Multiobjective Optimization

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

Indicator‐based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index

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