NOVEL REMINISCENCE INSPIRED AND APPROXIMATION BASED MEASUREMENT OF MOUNT KAILASH OPTIMIZATION ALGORITHMS

Lenin Kanagasabai
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Abstract

Reminiscence inspired optimization algorithm (RIA) and Approximation based Measurement of Mount Kailash optimization (MMK) algorithm are applied to solve the true power loss reduction problem. Reminiscence inspired optimization algorithm is scientifically designed based on the reminiscence of human beings around the globe. In the procedure poor solutions are replaced by the present solutions consecutively. In the Approximation Measurement of the Mount Kailash optimization algorithm, the measurement of Mount Kailash is done by approximation of comparison with triangles in the four faces of the Mount Kailash. The first measurements on the east face of Mount Kailash are done through the approximation of the triangle and from this population created in the search space. The key objectives of the paper are true power loss reduction, voltage deviation minimization, and voltage stability enhancement. Validity RIA and MMK are verified in 23 Benchmarking functions and IEEE 30, 354 systems.
受记忆启发、基于近似测量的新颖开拉什山优化算法
受记忆启发的优化算法(RIA)和基于近似的开拉什山测量优化算法(MMK)被用于解决真正的电能损耗降低问题。记忆启发优化算法是基于全球人类的记忆而科学设计的。在这个过程中,差的解决方案会被现在的解决方案连续替换。在 "开拉什山近似测量 "优化算法中,开拉什山的测量是通过与开拉什山四个面上的三角形进行近似比较来完成的。对冈仁波齐峰东面的首次测量是通过三角形的近似来完成的,并由此在搜索空间中创建了一个群体。本文的主要目标是降低真实功率损耗、最小化电压偏差和增强电压稳定性。在 23 个基准函数和 IEEE 30,354 系统中验证了 RIA 和 MMK 的有效性。
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