A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
نویسندگان
چکیده
منابع مشابه
A New Differential Evolution Algorithm with Alopex-Based Local Search
Differential evolution (DE), as a class of biologically inspired and meta-heuristic techniques, has attained increasing popularity in solving many real world optimization problems. However, DE is not always successful. It can easily get stuck in a local optimum or an undesired stagnation condition. This paper proposes a new DE algorithm Differential Evolution with Alopex-Based Local Search (DEA...
متن کاملA novel local search method for microaggregation
In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k_1 records, such that the sum of the within-group squ...
متن کاملA Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network
Abstract Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...
متن کاملMemetic Search in Differential Evolution Algorithm
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing...
متن کاملA Framework for Memetic Algorithms
Many optimization problems are fundamentally hard. Essentially, a ‘hard’ problem is one for which we cannot guarantee to find the best solution in a reasonable amount of time. In practice, however, the quest to solve hard problems is not quite so hopeless as this definition suggests. This is due to the use of approximate methods. An approximate method is an algorithm that we use to try to find ...
متن کاملذخیره 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: International Journal of Computational Intelligence Systems
سال: 2019
ISSN: 1875-6883
DOI: 10.2991/ijcis.d.190711.001