Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems

Abstract

A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational concepts of the MGA. For numerical investigations purposes, 10 constrained optimization problems in different dimensions of 10, 30, 50, and 100, which have been benchmarked by the Competitions on Evolutionary Computation (CEC), are selected as test examples while 15 of the well-known engineering design problems are also determined to evaluate the overall performance of the proposed method. The best results of different classical and new metaheuristic optimization algorithms in dealing with the selected problems were taken from the recent literature for comparison with MGA. Additionally, the statistical values of the MGA algorithm, consisting of the mean, worst, and standard deviation, were calculated and compared to the results of other metaheuristic algorithms. Overall, this work demonstrates that the proposed MGA is able provide very competitive, and even outstanding, results and mostly outperforms other metaheuristics.

Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches

“…The material generation algorithm (MGA) is a revolutionary approach that was developed and is used to assist engineers in generating the best solutions to engineering problems [46]. Some complex and essential aspects of material chemistry, such as the arrangement of chemical molecules and the chemical processes involved in the production of new materials, are thought to be ideas inspired by the MGA.…”

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

“…It is worth mentioning that there are other recently developed metaheuristic algorithms motivated by physicsbased techniques including Atom Search Optimization (ASO) [305] that mimics the nature of atomic motion model, Algorithm of the Innovative Gunner (AIG) [306] that inspired the choice of artillery parameters, Projectiles Optimization (PRO) algorithm [307] that inspired by models in kinematics, Gradient-Based Optimizer (GBO) [308] that inspired by the gradient-based Newton's method, Dynamic Differential Annealed Optimization (DDAO) [309] that mimics the current technique in producing high-quality steel, Lévy Flight Distribution (LFD) [310] that inspired from the Lévy flight, Solar System Algorithm (SSA) [311] that mimics the natural behavior of some objects around solar system, Archimedes Optimization Algorithm (AOA) [312] that inspired the law of Archimedes' Principle in physics, Material Generation Algorithm (MGA) [313] that mimics the basic aspects of material chemistry, Crystal Structure Algorithm (CryStAl) [314] that inspired by the basic principles of crystal structures formation, Gamma Ray Interactions Based Optimization (GRIBO) [315] that mimics various energy loss processes of gamma ray. However, these newly physic-based techniques are yet to be adopted in t-way testing problem.…”

Multiobjective Atomic Orbital Search (MOAOS) for Global and Engineering Design Optimization

“…Particle Swarm Optimizer (PSO) [7], Atomic Orbital Search (AOS) [8], Dynamic Water Strider Algorithm (DWSA) [9], Flow Direction Algorithm (FDA) [10], Crystal Structure Algorithm (CryStAl) [11], and Material Generation Algorithm (MGA) [12] are some of the well-formulated metaheuristic optimization algorithms which have contributed to solving a wide range of problems across different fields of science and technology [13]- [25].…”