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Tind, Jørgen; Wiecek, Margaret M.

doi:10.1023/a:1008314306344

Cited by 30 publications

(1 citation statement)

References 22 publications

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“…Biomimetics 2023, 8, x FOR PEER REVIEW 2 of 23 MOEA/D, a set of evenly weighted vectors in the objective space is used to decompose one MOP into multiple subproblems. Each subproblem is treated as a scalar optimisation problem, where a scalarisation function (e.g., the weighted-sum or Tchebycheff approach proposed by Miettinen (1999)) [5] and weight vectors are utilised to coordinate the relationships among the objective functions. Therefore, it is reasonable to utilise genetic evolution algorithms originally designed to solve single-objective optimisation problems (SOPs), especially crossover (CX)-based algorithms such as the differential evolution (DE) and its variants [6][7][8][9], to generate new solutions.…”

Section: Introductionmentioning

confidence: 99%

A New Hyper-Heuristic Multi-Objective Optimisation Approach Based on MOEA/D Framework

Han,

Watanabe

2023

Biomimetics

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A multi-objective evolutionary algorithm based on decomposition (MOEA/D) serves as a robust framework for addressing multi-objective optimization problems (MOPs). However, it is widely recognized that the applicability of a fixed offspring-generating strategy in MOEA/D can be limited, despite its foundation in the MOEA/D methodology. Consequently, hybrid algorithms have gained popularity in recent years. This study proposes a novel hyper-heuristic approach that integrates the estimation of distribution (ED) and crossover (CX) strategies into the MOEA/D framework based on the view of successful replacement rate (SSR) and attempts to explain the potential reasons for the advantages of hybrid algorithms. The proposed approach dynamically switches from the differential evolution (DE) operator to the covariance matrix adaptation evolution strategy (CMA-ES) operator. Simultaneously, certain subproblems in the neighbourhood denoted as B(i) employ the Improved Differential Evolution (IDE) operator to generate new individuals for balancing the high evaluation costs associated with CMA-ES. Numerical experiments unequivocally demonstrate that the suggested approach offers distinct advantages when applied to a three-objective test suite. These experiments also validate a significant enhancement in the efficiency (SRR) of the DE operator within this context. The perspectives and experimental findings, with a focus on the Success Rate Ratio (SRR), have the potential to provide valuable insights and inspire further research in related domains.

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Section: Introductionmentioning

confidence: 99%

A New Hyper-Heuristic Multi-Objective Optimisation Approach Based on MOEA/D Framework

Han,

Watanabe

2023

Biomimetics

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Piecewise quadratic approximation of the non‐dominated set for bi‐criteria programs

Wiecek

Chen

Zhang

2001

Multi Criteria Decision Anal

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A procedure to approximate the non-dominated set for general (continuous) bi-criteria programs is proposed. The piecewise approximation is composed of quadratic curves, each of which is developed locally in a neighbourhood of a non-dominated point of interest and based on primal-dual relationships associated with the weighted Tchebycheff scalarization of the original problem. The approximating quadratic functions, in which decision maker's preferences are represented, give a closed-form description of the non-dominated set. A numerical example is included.

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Detecting local convexity on the pareto surface

Fadel

Konda

et al. 2002

Multi Criteria Decision Anal

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Recent regain in interest in multi‐criteria optimization approaches to provide a designer with multiple solutions to select from and support decisions has led to various methodologies to generate such solutions and possibly approximate the Pareto set. This paper introduces the notions of H‐ and w‐convexities, and develops a simple method to identify local convexity of Pareto hyper‐surfaces since their shape can dictate the choice of the method to use to obtain Pareto solutions and possibly to build an approximation of that set. The method is based on comparing the results from the weighting method to those of the Tchebycheff method at any point on the Pareto hyper‐surface. If, under some conditions, the points obtained from the two methods are identical or not, a local convexity or its lack can be assumed at that location and in its immediate neighbourhood. A numerical example is included. Copyright © 2002 John Wiley & Sons, Ltd.

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Cited by 30 publications

References 22 publications

A New Hyper-Heuristic Multi-Objective Optimisation Approach Based on MOEA/D Framework

A New Hyper-Heuristic Multi-Objective Optimisation Approach Based on MOEA/D Framework

Piecewise quadratic approximation of the non‐dominated set for bi‐criteria programs

Detecting local convexity on the pareto surface

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