Solving Speed Reducer Design Problem by Memorized Differential Evolution
Raghav Prasad Parouha · 2022 IEEE World Conference on Applied Intelligence and Computing (AIC) · 2022
Real-life optimization problems (RLOPs) are common in numerous research disciplines. In literature, evolutionary algorithms (EAs) have been deemed an emerging field to solve RLOPs. Among popular EAs, Differential Evolution (DE) is clever algorithm to solve RLOPs. DE and its varied alternatives are highly influenced by improper operators like mutation and crossover. Mostly, DE doesn’t compel to learn the optimum properties achieved in primary phase of the former aristocrats. In this article, a new DE (named asmbDE) created on memory mechanism is offered to solve a renowned RLOP, specifically speed reducer design. It enclosed different crossover & mutation (swarm, crossover and mutation) made through particle swarm optimization (PSO) context. Experimental outcomes endorse the competency ofmbDE over various procedures.