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Region Encoding Helps Evolutionary Computation Evolve Faster: A New Solution Encoding Scheme in Particle Swarm for Large-Scale Optimization
IEEE Transactions on Evolutionary Computation ( IF 14.3 ) Pub Date : 2021-03-12 , DOI: 10.1109/tevc.2021.3065659
Jun-Rong Jian , Zong-Gan Chen , Zhi-Hui Zhan , Jun Zhang

In the last decade, many evolutionary computation (EC) algorithms with diversity enhancement have been proposed to solve large-scale optimization problems in big data era. Among them, the social learning particle swarm optimization (SLPSO) has shown good performance. However, as SLPSO uses different guidance information for different particles to maintain the diversity, it often results in slow convergence speed. Therefore, this article proposes a new region encoding scheme (RES) to extend the solution representation from a single point to a region, which can help EC algorithms evolve faster. The RES is generic for EC algorithms and is applied to SLPSO. Based on RES, a novel adaptive region search (ARS) is designed to on the one hand keep the diversity of SLPSO and on the other hand accelerate the convergence speed, forming the SLPSO with ARS (SLPSO-ARS). In SLPSO-ARS, each particle is encoded as a region so that some of the best (e.g., the top ${P}$ ) particles can carry out region search to search for better solutions near their current positions. The ARS strategy offers the particle a greater chance to discover the nearby optimal solutions and helps to accelerate the convergence speed of the whole population. Moreover, the region radius is adaptively controlled based on the search information. Comprehensive experiments on all the problems in both IEEE Congress on Evolutionary Computation 2010 (CEC 2010) and 2013 (CEC 2013) competitions are conducted to validate the effectiveness and efficiency of SLPSO-ARS and to investigate its important parameters and components. The experimental results show that SLPSO-ARS can achieve generally better performance than the compared algorithms.

中文翻译:

区域编码帮助进化计算更快进化:粒子群中用于大规模优化的新解决方案编码方案

在过去的十年中,已经提出了许多具有多样性增强的进化计算(EC)算法来解决大数据时代的大规模优化问题。其中,社会学习粒子群优化(SLPSO)表现出良好的性能。然而,由于SLPSO对不同的粒子使用不同的引导信息来保持多样性,往往会导致收敛速度变慢。因此,本文提出了一种新的区域编码方案(RES),将解表示从单点扩展到区域,可以帮助EC算法更快地进化。RES 对于 EC 算法是通用的,适用于 SLPSO。基于RES,设计了一种新颖的自适应区域搜索(ARS),一方面保持SLPSO的多样性,另一方面加快收敛速度​​,与 ARS (SLPSO-ARS) 形成 SLPSO。在 SLPSO-ARS 中,每个粒子都被编码为一个区域,以便一些最好的(例如,顶部 ${P}$ ) 粒子可以进行区域搜索以在其当前位置附近搜索更好的解。ARS 策略为粒子提供了更大的机会发现附近的最优解,并有助于加快整个种群的收敛速度。此外,基于搜索信息自适应地控制区域半径。对 2010 年 IEEE 进化计算大会 (CEC 2010) 和 2013 年 (CEC 2013) 竞赛中的所有问题进行了综合实验,以验证 SLPSO-ARS 的有效性和效率,并研究其重要参数和组件。实验结果表明,SLPSO-ARS 的性能总体上优于对比算法。
更新日期:2021-03-12
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