Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

Abstract

In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. e ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. ese two phases are tied with a bridging mechanism, which plays an important role. is paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. is experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). e performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

“…The effectiveness of NBGOA was evaluated using 20 datasets with various sizes taken from the UCI datasets repository in comparison with five well-regarded optimization techniques in the feature selection field. Simulation results revealed that BGOA [78] NBGOA [79] BGOA [67] ECGOAs [80] LMGOA [81] ECGOA [82] CGOA [83] CGOA [84] SFECGOAs [85] OLCGOA [86] ECGOAs [87] ECAGOA [88] IGOA [70] EGOA [89] PGOA [90] LGOA [91] IGOA [92] AGOA [93] MI-LFGOA [94] LGOA [95] GOA_EPD [65] DJGOA [96] DQBGOA_MR [97] Fuzzy GOA [98] GO-FLC [99] EGOA-FC [100] AGOA [69] AGOA [101] GHO [102] self-adaptive GOA [103] OGOA [104] OBLGOA [105] IGOA [106] MOGOA [75] MOGOA [76] MOGOA [66] MOGOA [107] MOGOA [108] MOGOA [109] LWSGOA [110] MGOA [111] GOFS [112] PCA-GOA [113] OGOA [114] IGOA [115] Fractional-GOA…”

A Maximum Power Point Tracking Method Based on a Modified Grasshopper Algorithm Combined with Incremental Conductance

“…Hence, the value of c decreases with increasing iteration. To improve GOA's accuracy in finding GMPP, Akash Saxena et al [27] added some randomness in GOA. The authors referred to their method as enhanced chaotic GOA (ECGOA).…”

“…The authors referred to their method as enhanced chaotic GOA (ECGOA). This random parameter x l is added to c, and, according to [27], the value of x l is within different ranges for the following four cases:…”

Enhanced Chaotic Grasshopper Optimization Algorithm Based PID Controller for Automatic Voltage Regulator System

“…The Enhanced chaotic grasshopper optimization algorithm (ECGOA)are used to find the parameter and it reduces speedily to reach the comfort zone. The different chaotic sequences [28] [29] are used and the chaotic Logistic map is quite exciting to implement for AVR system [3]. The Logistic chaotic map expression is given in Eqn.…”