Garra Rufa‐inspired optimization technique

Abstract

Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well‐ developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular “fish massage session.” A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

Multiobjective optimization model for renewable energy sources and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems

“…The Garra Rufa fish optimization (GRFO) depends on Garra Rufa movements, the particles have been divided as groups, the better optimum value leads every group 35 . This is a new metaheuristic optimization approach depending on psychology of students who are annoying harder to improve its exam performance becoming the better student in class 36 …”

“…35 This is a new metaheuristic optimization approach depending on psychology of students who are annoying harder to improve its exam performance becoming the better student in class. 36…”

Comparison of electric motors used in electric vehicle propulsion system

“…Since the artificial intelligent applications started to have a real application, the intelligent control and optimization strategies are getting very fast development and modifications to be applied for all engineering, medicine, and economic challenges [35]- [40]. The intelligent systems improve the EVs operation and efficiency by several strategies, such as control the driver, optimizing the batteries, and develop a smart power system.…”

Evolutionary algorithm solution for economic dispatch problems

“…The study by Touma [5] proposed the whale optimization approach of finding a solution to the ED problem. The proposed system was evaluated in a standard IEEE 30-Bus testbed and the performance was compared with those of other optimization algorithms, such as elephant heart optimization (EHO), Garra Rufa optimization (GRO), particle swarm optimization (PSO), and genetic algorithm [6]. Another study by Victoire and Jeyakumar [7] reported the use of PSO to solve different forms of ED, including the two-area ED with tie line limits, ED of generators with forbidden operation zones, and ED with different fuel choices.…”