Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms

Hybrid Methods for Multi-objective Evolutionary Algorithms

Hybrid methods of using evolutionary algorithms with a local search method are often used in the context of singleobjective real-world optimization. In this paper, we discuss a couple of hybrid methods for multi-objective realworld optimization. In the posteriori approach, the obtained non-dominated solutions of a multi-objective evolutionary algorithm (MOEA) run are modified using a local sear...

Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms(QMOEA) and gave one of its sufficient convergence conditions to Pareto optimal set. In this paper, two Q-gate operators, H2 gate and R&N2 gate, are experimentally validated as two Q-gate paradigms meeting to the convergence condition. The former is a modified rotation gate, and the la...

Convergence Properties of Some Multi-Objective Evolutionary Algorithms

We present four abstract evolutionary algorithms for multiobjective optimization and theoretical results that characterize their convergence behavior. Thanks to these results it is easy to verify whether or not a particular instantiation of these abstract evolutionary algorithms offers the desired limit behavior. Several examples are given.

islanding detection methods for microgrids

امروزه استفاده از منابع انرژی پراکنده کاربرد وسیعی یافته است . اگر چه این منابع بسیاری از مشکلات شبکه را حل می کنند اما زیاد شدن آنها مسائل فراوانی برای سیستم قدرت به همراه دارد . استفاده از میکروشبکه راه حلی است که علاوه بر استفاده از مزایای منابع انرژی پراکنده برخی از مشکلات ایجاد شده توسط آنها را نیز منتفی می کند . همچنین میکروشبکه ها کیفیت برق و قابلیت اطمینان تامین انرژی مشترکان را افزایش ...

Multi-Objective Evolutionary Algorithms