Hybrid Genetic Manta Ray Foraging Optimization and Its Application to Interval Type 2 Fuzzy Logic Control of An Inverted Pendulum System

Abstract

This paper presents an improvised version of Manta-Ray Foraging Optimization (MRFO) by using components in Genetic Algorithm (GA). MRFO is a recent proposed algorithm which based on the behaviour of manta rays. The algorithm imitates three foraging strategies of this cartilaginous fish, which are chain foraging, cyclone foraging and somersault foraging to find foods. However, this optimization algorithm can be improved in its strategy which increases its accuracy. Thus, in this proposed improvement, mutation and crossover strategy from GA were adopted into MRFO. Crossover operation is a convergence action which is purposely to pull the agents towards an optimum point. At the meanwhile, mutation operation is a divergence action which purposely to spread out the agents throughout wider feasible region. Later, the algorithms were performed on several benchmark functions and statically tested by using Wilcoxon signed-rank test to know their performances. To test the algorithm with a real application, the algorithms were applied to an interval type 2 fuzzy logic controller (IT2FLC) of an inverted pendulum system. Result of the test on benchmark functions shows that GMRFO outperformed MRFO and GA and it shows that it provides a better parameter of the control system for a better response.

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

“…Ahmad et al [35] incorporated crossover and mutation mechanisms into MRFO to enhance its divergence and convergence actions. Several touchstone functions and an interval Type-2 fuzzy-logic controller of an inverted pendulum model were used to test the proposed modified MRFO.…”

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

“…This is because the growth of type-2 FLS uncertainty can be directly integrated into fuzzy sets, as described in Section 6. Furthermore, in the last three years of studies on higher-order types of FLS in particular, the designed and developed applications of interval type-2 fuzzy logic have increased significantly [48][49][50][51][52][53][54]. These type-2-based FLS applications have been identified in artificial intelligence (AI) [55][56][57][58][59], adaptive control [60][61][62][63][64][65][66], electric motor control [67][68][69][70][71][72], Internet of Things (IoT) [73][74][75][76][77], digital image processing [78][79][80][81][82][83][84] and other areas [85][86][87].…”

On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

“…This version was tested on CEC 2014 and CEC 2017 benchmark problems [ 58 ]. While Abdul Razak et al adopted the GA’s mutation and crossover to improve MRFO’s convergence action, where the proposed genetic MRFO (GMRFO) was optimized an interval type 2 fuzzy logic for inverted pendulum system [ 59 ]. Also, GMRFO was tested on some composite natures of the test functions.…”