Distance-Based Analysis of Crossover Operators for Many-Objective Knapsack Problems

Ishibuchi, Hisao; Tanigaki, Yuki; Masuda, Hiroyuki; Nojima, Yusuke

doi:10.1007/978-3-319-10762-2_59

Cited by 25 publications

(12 citation statements)

References 15 publications

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“…Therefore, two nearly converged solutions may generate offspring far from the true Pareto front. This issue has also been confirmed in [6][7]. Furthermore, in the most common condition, when MOEAs are applied to solve many-objective optimization problems, they can only explore a small region compared with the large search space and restrictly focus on a limited area of the Pareto front because of the ineffective evolutionary operator.…”

Section: Introductionmentioning

confidence: 76%

Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement

Yen

2016

IEEE Trans. Evol. Computat.

139

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Evolutionary algorithms have been successfully applied for exploring both converged and diversified approximate Pareto-optimal fronts in multiobjective optimization problems, two-or three-objective in general. However, when solving problems with many objectives, nearly all algorithms perform poorly due to the loss of selection pressure in fitness evaluation. An extremely large objective space could inadvertently deteriorate the effect of an evolutionary operator. In this paper, we propose a new approach to directly handle the challenges to solve many-objective optimization problems. This novel design includes two stages: first, the whole population quickly approaches a small number of "target" points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and distribute well. As a case study, the proposed algorithm based on this design is compared with five state-of-the-art algorithms. Experimental results show that the proposed method exhibits improved performance in both convergence and diversity for solving many-objective optimization problems. IndexTerms-Objective space reduction, diversity improvement strategy, many-objective optimization problems, many-objective evolutionary algorithms 1089-778X (c)

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Section: Introductionmentioning

confidence: 76%

Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement

Yen

2016

IEEE Trans. Evol. Computat.

139

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“…Based on the analysis, the authors further suggested Controlling the maximum number of Crossed Genes (C-CG) in crossover operators, which could significantly improve the search performance of several MOEAs on MaOPs. Ishibuchi et al [17] observed that the clear performance improvement on many-objective knapsack problems is obtained when the parent-offspring distance is small. To further verify this observation, the authors implemented a distancebased crossover operator where the parent-offspring distance is specified as a user-defined parameter.…”

Section: Variation Operators In Multi and Manyobjective Optimizationmentioning

confidence: 98%

“…However, almost all the related algorithms in this regard adopted the conventional genetic operators to reproduce solutions, leaving room for the investigation of the other alternative variation operators, e.g., differential evolution (DE) [26]. On the other hand, several recent studies [24,14,17] have shown that the issue of performing an effective search in the decision space should be addressed more carefully when handling many objectives. Nevertheless, it is well known that pure genetic operators may not be powerful enough to achieve a good balance between exploration and exploitation in the decision space for some complex problems [30].…”

Section: Introductionmentioning

confidence: 99%

An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization

Yuan

Wang

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Reference-point based multi-objective evolutionary algorithms (MOEAs) have shown promising performance in manyobjective optimization. However, most of existing research within this area focused on improving the environmental selection procedure, and little work has been done on the effect of variation operators. In this paper, we conduct an experimental investigation of variation operators in a typical reference-point based MOEA, i.e., NSGA-III. First, we provide a new NSGA-III variant, i.e., NSGA-III-DE, which introduces differential evolution (DE) operator into NSGA-III, and we further examine the effect of two main control parameters in NSGA-III-DE. Second, we have an experimental analysis of the search behavior of NSGA-III-DE and NSGA-III. We observe that NSGA-III-DE is generally better at exploration whereas NSGA-III normally has advantages in exploitation. Third, based on this observation, we present two other NSGA-III variants, where DE operator and genetic operators are simply combined to reproduce solutions. Experimental results on several benchmark problems show that very encouraging performance can be achieved by three suggested new NSGA-III variants. Our work also indicates that the performance of NSGA-III is significantly bottlenecked by its variation operators, providing opportunities for the study of the other alternative ones.

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“…Second, the extensive search in a high-dimensional space would seriously undermine the efficiency of algorithmic operators, such as mating selection and variation [15]. As confirmed in [16,17], in a variation process, the new offspring produced by two nearly converged solutions, which are required to approach along its original direction, would contrarily move far away the true PF. This causes the failure of the final population to converge to the PF, despite spreading all over the objective space.…”

Section: Introductionmentioning

confidence: 99%

A novel many-objective evolutionary algorithm based on transfer matrix with Kriging model

Wang

Chen

et al. 2020

Information Sciences

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Due to the curse of dimensionality caused by the increasing number of objectives, it is very challenging to tackle many-objective optimization problems (MaOPs). Aiming to alleviate the loss of selection pressure in the fitness evaluation for MaOPs, this paper proposes a novel evolutionary optimization framework, called Tk-MaOEA, based on transfer learning assisted by Kriging model. In this approach, in order to achieve global space optimization, transfer learning is used as a map tool to reduce the objective space, i.e., devising transfer matrix to simplify the optimization process. For the objective optimization, the Kriging model is appropriately incorporated in order to further reduce computation cost. Accordingly, any EA-based paradigm or search strategy can be integrated into this framework. Fast non-dominated sorting and farthest-candidate selection (FCS) methods are used to guarantee the diversity of non-dominated solutions. Comprehensive evaluations on a set of benchmark functions have been conducted to show that the proposed Tk-MaOEA is efficietive for solving complex MaOPs.

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Distance-Based Analysis of Crossover Operators for Many-Objective Knapsack Problems

Cited by 25 publications

References 15 publications

Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement

Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement

An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization

A novel many-objective evolutionary algorithm based on transfer matrix with Kriging model

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