Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization
Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization
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DOI：
10.1109/tevc.2009.2017517
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发表时间：
2010-02
影响因子：
14.3
通讯作者：
Jie Yao;N. Kharma;P. Grogono
中科院分区：
文献类型：
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作者：
Jie Yao;N. Kharma;P. Grogono
本文描述了一种双目标多种群遗传算法(BMPGA)的最新版本，其目标是在实值可微多模式景观上定位所有全局和局部最优解。将BMPGA与四种多峰遗传算法在五个多峰函数上的性能进行了比较。BMPGA的独特之处在于它使用了两个独立但互补的适应度目标，旨在增强总体种群的多样性和搜索空间的探索。这与多种群和集群方案相结合，该方案侧重于在不同的子种群内进行选择，并导致有效地识别和保留目标函数的最优值，以及改进在有希望的区域内的开发。实证比较的结果提供了明确的证据，支持了BMPGA在总体有效性、适用性和可靠性方面优于其他遗传算法的结论。BMPGA在显微图像的多椭圆和椭圆目标检测中的应用已经证明了其实用价值。
This paper describes the latest version of a bi-objective multipopulation genetic algorithm (BMPGA) aiming to locate all global and local optima on a real-valued differentiable multimodal landscape. The performance of BMPGA is compared against four multimodal GAs on five multimodal functions. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multipopulation and clustering scheme, which focuses selection within the various sub-populations and results in effective identification and retention of the optima of the target functions as well as improved exploitation within promising areas. The results of the empirical comparison provide clear evidence that supports the conclusion that BMPGA is better than the other GAs in terms of overall effectiveness, applicability, and reliability. The practical value of BMPGA has already been demonstrated in applications to multiple ellipses and elliptic objects detection in microscopic imagery.