Escaping from Local Optima and Convergence Mechanisms Based on Search History in Evolutionary Multi-criterion Optimization

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Karush-Kuhn-Tucker Optimality Based Local Search for Enhanced Convergence of Evolutionary Multi-Criterion Optimization Methods

Recent studies have used Karush-Kuhn-Tucker (KKT) optimality conditions to develop a KKT ProximityMeasure (KKTPM) for terminating amulti-objective optimization simulation run based on theoretical convergence of solutions. In addition to determining a suitable termination condition and due to their ability to provide a single measure for convergence to Pareto-optimal solutions, the developed KKT...

متن کامل

Balance Between Genetic Search And Local Search In Hybrid Evolutionary Multi-criterion Optimization Algorithms

The aim of this paper is to clearly demonstrate the importance of finding a good balance between genetic search and local search in the implementation of hybrid evolutionary multicriterion optimization (EMO) algorithms. We first modify the local search part of an existing multi-objective genetic local search (MOGLS) algorithm. In the modified MOGLS algorithm, the computation time spent by local...

متن کامل

Escaping Local Optima in Multi-Agent Oriented Constraint Satisfaction

We present a multi-agent approach to constraint satisfaction where feedback and reinforcement are used in order to avoid local optima and, consequently, to improve the overall solution. Our approach, FeReRA, is based on the fact that an agent’s local best performance does not necessarily contribute to the system’s best performance. Thus, agents may be rewarded for improving the system’s perform...

متن کامل

Escaping Local Optima via Parallelization and Migration

We present a new nature-inspired algorithm, mt − GA, which is a parallelized version of a simple GA, where subpopulations evolve independently from each other and on different threads. The overall goal is to develop a population-based algorithm capable to escape from local optima. In doing so, we used complex trap functions, and we provide experimental answers to some crucial implementation dec...

متن کامل

Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search

Local search algorithms often get trapped in local optima. Algorithms such as tabu search and simulated annealing ’escape’ local optima by accepting nonimproving moves. Another possibility is to dynamically change between representations; a local optimum under one representation may not be a local optimum under another. Shifting is a mechanism which dynamically switches between Gray code repres...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Transactions of the Japanese Society for Artificial Intelligence

سال: 2017

ISSN: 1346-0714,1346-8030

DOI: 10.1527/tjsai.e-gb1