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A Novel Memetic Algorithm for Unconstrained Optimization

机译：一种新的无约束优化模因算法

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摘要

This paper describes a novel Memetic Algorithm for unconstrained optimization. The proposed approach aims to add a probabilistic procedure to determine if employing pattern search during a specific Particle Swarm Optimization generation. To verify the effectiveness of the proposed approach, several continuous functions are selected to test the proposed approach in comparison to conventional pattern search and the conventional PSO. Moreover, two kinds of integration schema for pattern search and PSO are also compared with the proposed approach. Experimental results demonstrate that the proposed approach is extremely effective and efficient at locating global optimal solutions for unconstrained optimization.
机译：本文介绍了一种新颖的无约束优化Memetic算法。提出的方法旨在添加一个概率过程，以确定在特定的粒子群优化生成过程中是否采用模式搜索。为了验证该方法的有效性，与常规模式搜索和常规PSO相比，选择了几个连续函数来测试该方法。此外，还将两种用于模式搜索和PSO的集成方案与所提出的方法进行了比较。实验结果表明，该方法在寻找无约束优化的全局最优解时非常有效。

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