A hybrid of electromagnetism-like mechanism and back-propagation algorithms for recurrent neural fuzzy systems design

Abstract

This article introduces a novel hybrid evolutionary algorithm for recurrent fuzzy neural systems design in applications of nonlinear systems. The hybrid learning algorithm, IEMBP-improved electromagnetism-like (EM) with back-propagation (BP) technique, combines the advantages of EM and BP algorithms which provides high-speed convergence, higher accuracy and less computational complexity (computation time in seconds). In addition, the IEMBP needs only a small population to outperform the standard EM that uses a larger population. For a recurrent neural fuzzy system, IEMBP simulates the 'attraction' and 'repulsion' of charged particles by considering each neural system parameters as a charged particle. The EM algorithm is modified in such a way that the competition selection is adopted and the random neighbourhood local search is replaced by BP without evaluations. Thus, the IEMBP algorithm combines the advantages of multi-point search, global optimisation and faster convergence. Finally, several illustration examples for nonlinear systems are shown to demonstrate the performance and effectiveness of IEMBP.

Multipath Routing Algorithm Using an Electromagnetic-Like Mechanism with Threshold Acceptance for Mobile Ad Hoc Networks

“…EM optimization simulates the behavior of electrically charged particles and utilizes an attractionrepulsion mechanism to move a population of points toward optimality. EM showed substantially improved performance compared with other well-known optimization methods [44,45]. The method also exhibited a high rate of global convergence, which simultaneously makes all points converge to the highly attractive valley and move further away from the steeper hills.…”

Kablosuz algılayıcı ağlarda hedef kapsama problemi için algılayıcı dağıtımı ile ağın yaşam süresinin optimizasyonu

“…Eğer y en iyi f(x i ) değerine ulaşır ise, x i parçacığı y ile yer değiştirir. Fakat bu süreç genellikle fazla zaman harcadığından dolayı bu alt program gerekli değildir ve göz ardı edilebilir [21,26].…”

Physics-Inspired Optimization Algorithms: A Survey

“…Numerous applications are developed on the basis of this algorithm such as scheduling problems [147][148][149][150], course timetabling [151], PID controller [152], fuzzy system [153][154][155], vehicle routing problem [156], networking [157], inventory control [158], neural network [159,160], TSP [161,162], feature selection [163], antenna application [164], robotics application [165], flow path designing [166], and vehicle routing [167].…”