Differential Evolution with Fuzzy Logic for Dynamic Adaptation of Parameters in Mathematical Function Optimization

2015 ◽  
pp. 361-374 ◽  
Author(s):  
Oscar Castillo ◽  
Patricia Ochoa ◽  
José Soria
Keyword(s):  
Fuzzy Logic ◽  
Differential Evolution ◽  
Function Optimization ◽  
Dynamic Adaptation ◽  
Mathematical Function

scholarly journals DIFFERENTIAL EVOLUTION WITH DYNAMIC ADAPTATION OF PARAMETERS BASED ON A FUZZY LOGIC AUGMENTATION APPROACH

2019 ◽  
Vol 2 (2) ◽  
pp. 183-207
Author(s):  
Oscar Castillo ◽  
Patricia Ochoa ◽  
José Soria
Keyword(s):  
Fuzzy Logic ◽  
Differential Evolution ◽  
Dynamic Adaptation

FUZZY DYNAMIC LOGIC

2006 ◽  
Vol 02 (01) ◽  
pp. 43-55 ◽  
Author(s):  
LEONID I. PERLOVSKY
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Logic ◽  
Learning System ◽  
Dynamic Adaptation ◽  
Fuzzy Models ◽  
Uncertain Knowledge ◽  
Model Improvement ◽  
The Mind ◽  
Fuzzy State ◽  
Theory Of Forms

Fuzzy logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as a joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an image recognition example of patterns below clutter. The paper discusses relationships to formal logic, fuzzy logic, complexity and draws tentative connections to Aristotelian theory of forms and working of the mind.

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

An Efficient Differential Evolution Algorithm For Function Optimization

2013 ◽  
Vol 12 (3) ◽  
pp. 444-448 ◽  
Author(s):  
Chao-Xue Wang ◽  
Chang-Hua Li ◽  
Hui Dong ◽  
Fan Zhang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Evolution Algorithm
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