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Hisao Ishibuchi - One of the best experts on this subject based on the ideXlab platform.

  • a decomposition based large scale multi modal multi Objective Optimization algorithm
    Congress on Evolutionary Computation, 2020
    Co-Authors: Yiming Peng, Hisao Ishibuchi
    Abstract:

    A multi-modal multi-Objective Optimization problem is a special kind of multi-Objective Optimization problem with multiple Pareto subsets. In this paper, we propose an efficient multi-modal multi-Objective Optimization algorithm based on the widely used MOEA/D algorithm. In our proposed algorithm, each weight vector has its own sub-population. With a clearing mechanism and a greedy removal strategy, our proposed algorithm can effectively preserve equivalent Pareto optimal solutions (i.e., different Pareto optimal solutions with same Objective values). Experimental results show that our proposed algorithm can effectively preserve the diversity of solutions in the decision space when handling large-scale multi-modal multi-Objective Optimization problems.

  • A Review of Evolutionary Multi-modal Multi-Objective Optimization
    IEEE Transactions on Evolutionary Computation, 2020
    Co-Authors: Ryoji Tanabe, Hisao Ishibuchi
    Abstract:

    Multi-modal multi-Objective Optimization aims to find all Pareto optimal solutions including overlapping solutions in the Objective space. Multi-modal multi-Objective Optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term "multi-modal multi-Objective Optimization". To address this issue, this paper reviews existing studies of evolutionary multi-modal multi-Objective Optimization, including studies published under names that are different from "multi-modal multi-Objective Optimization". Our review also clarifies open issues in this research area.

Ryoji Tanabe - One of the best experts on this subject based on the ideXlab platform.

  • A Review of Evolutionary Multi-modal Multi-Objective Optimization
    IEEE Transactions on Evolutionary Computation, 2020
    Co-Authors: Ryoji Tanabe, Hisao Ishibuchi
    Abstract:

    Multi-modal multi-Objective Optimization aims to find all Pareto optimal solutions including overlapping solutions in the Objective space. Multi-modal multi-Objective Optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term "multi-modal multi-Objective Optimization". To address this issue, this paper reviews existing studies of evolutionary multi-modal multi-Objective Optimization, including studies published under names that are different from "multi-modal multi-Objective Optimization". Our review also clarifies open issues in this research area.

Xiangyu Wang - One of the best experts on this subject based on the ideXlab platform.

  • A genetic algorithm for unconstrained multi-Objective Optimization
    Swarm and evolutionary computation, 2015
    Co-Authors: Qiang Long, Changzhi Wu, Tingwen Huang, Xiangyu Wang
    Abstract:

    Abstract In this paper, we propose a genetic algorithm for unconstrained multi-Objective Optimization. Multi-Objective genetic algorithm (MOGA) is a direct method for multi-Objective Optimization problems. Compared to the traditional multi-Objective Optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-Objective Optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-Objective benchmarks and compare its results with other MOGASs׳; the result show that the proposed method is robust and efficient.

Yiming Peng - One of the best experts on this subject based on the ideXlab platform.

  • a decomposition based large scale multi modal multi Objective Optimization algorithm
    Congress on Evolutionary Computation, 2020
    Co-Authors: Yiming Peng, Hisao Ishibuchi
    Abstract:

    A multi-modal multi-Objective Optimization problem is a special kind of multi-Objective Optimization problem with multiple Pareto subsets. In this paper, we propose an efficient multi-modal multi-Objective Optimization algorithm based on the widely used MOEA/D algorithm. In our proposed algorithm, each weight vector has its own sub-population. With a clearing mechanism and a greedy removal strategy, our proposed algorithm can effectively preserve equivalent Pareto optimal solutions (i.e., different Pareto optimal solutions with same Objective values). Experimental results show that our proposed algorithm can effectively preserve the diversity of solutions in the decision space when handling large-scale multi-modal multi-Objective Optimization problems.

Qiang Long - One of the best experts on this subject based on the ideXlab platform.

  • A genetic algorithm for unconstrained multi-Objective Optimization
    Swarm and evolutionary computation, 2015
    Co-Authors: Qiang Long, Changzhi Wu, Tingwen Huang, Xiangyu Wang
    Abstract:

    Abstract In this paper, we propose a genetic algorithm for unconstrained multi-Objective Optimization. Multi-Objective genetic algorithm (MOGA) is a direct method for multi-Objective Optimization problems. Compared to the traditional multi-Objective Optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-Objective Optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-Objective benchmarks and compare its results with other MOGASs׳; the result show that the proposed method is robust and efficient.