Evolutionary computation techniques have mostly been used to solve variousoptimization and learning problems successfully. Evolutionary algorithm is moreeffective to gain optimal solution(s) to solve complex problems thantraditional methods. In case of problems with large set of parameters,evolutionary computation technique incurs a huge computational burden for asingle processing unit. Taking this limitation into account, this paperpresents a new distributed evolutionary computation technique, which decomposesdecision vectors into smaller components and achieves optimal solution in ashort time. In this technique, a Jacobi-based Time Variant Adaptive (JBTVA)Hybrid Evolutionary Algorithm is distributed incorporating cluster computation.Moreover, two new selection methods named Best All Selection (BAS) and TwinSelection (TS) are introduced for selecting best fit solution vector.Experimental results show that optimal solution is achieved for different kindsof problems having huge parameters and a considerable speedup is obtained inproposed distributed system.
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机译：进化计算技术主要用于成功解决各种优化和学习问题。进化算法比传统方法更有效地获得用于解决复杂问题的最优解。在存在大量参数问题的情况下，进化计算技术给单个处理单元带来了巨大的计算负担。考虑到这一局限性，本文提出了一种新的分布式进化计算技术，它将决策向量分解为较小的分量，并在短时间内获得最佳解。在该技术中，结合聚类计算的方法，基于Jacobi的时变自适应（JBTVA）混合进化算法得以分发。此外，引入了两种名为Best All Selection（BAS）和TwinSelection（TS）的新选择方法来选择最佳拟合解向量。实验结果表明，该方法可以解决各种参数大的问题，并且在分布式系统中可以显着提高速度。
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