Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers

Abstract

This article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem using different performance metrics depending on the problem. The algorithms used in the preliminary tests are: the firefly algorithm (FA), which is inspired by blinking fireflies; wind-driven optimization (WDO), which is inspired by the movement of the wind in the atmosphere, and in which the positions and velocities of the wind packages are updated; and finally, drone squadron optimization (DSO)—the inspiration for this method is new and interesting—based on artifacts, where drones have a command center that sends information to individual drones and updates their software to optimize the objective function. The proposed model helps discover the best method to solve a specific problem, and also reduces the time that it takes to search for methods before finding the one that obtains the most satisfactory results. The main idea is that with this competitiveness approach, methods are tested at the same time until the best one to solve the problem in question is found. As preliminary tests of the model, the optimization of the benchmark mathematical functions and membership functions of a fuzzy controller of an autonomous mobile robot was used.

FUZZY TOPSIS Application in Materials Analysis for Economic Production of Cashew Juice Extractor

“…The FST is the most important tool in modelling uncertainty and the area of production management it has aided research [24]. The tool has also made life easier today due to its ability in using the linguistic variables to model human reasoning thereby providing the solution to the problem in the past without a satisfactory solution [25]. In most multi-criteria problems, the criteria on the basis decision are made are often of incompatible dimensions which may create a challenge in the evaluation process and to avoid such difficulty, there is a need for the fuzzy system [26].…”

Using Harris hawk optimization towards support vector regression to ozone prediction

“…HHO is a metaheuristic algorithm that is used in parameter optimization problems (Lagunes et al 2019). HHO is built on the hunting pattern of Harris hawks and prey behaviour which is formulated mathematically (Heidari et al 2019).…”

Research on UBI Auto Insurance Pricing Model Based on Parameter Adaptive SAPSO Optimal Fuzzy Controller

“…Lagunes et al adopt a variety of meta-heuristic algorithms to test algorithms through competitive methods until they find the best way to solve the problem. In the model check, the benchmark mathematical function and membership function of the fuzzy controller of the autonomous mobile robot are optimized [19]. There are many improvements to the particle swarm algorithm [20,21,22], and the pursuit of particle swarm parameter adaptation is one of the main directions of improvement.…”

“…, a m ], as shown in Figure 4. The former fuzzy number plus the parameter interval equals the latter fuzzy number, and the formulas are shown in Equations ( 18) and (19).…”