Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

Olivas, Frumen; Amador-Angulo, Leticia; Pérez, Jonathan; Caraveo, Camilo; Valdez, Fevrier; Castillo, Oscar

doi:10.3390/a10030101

Cited by 54 publications

(14 citation statements)

References 59 publications

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“…The first output variable is τ1 (torque 1), the second output variable is τ2 (torque 2); for the output variables, three triangular membership functions labeled N, Z and P are used. The design of the fuzzy controller of the autonomous mobile robot can be found in Figure 9 [4,33]. The fuzzy controller rules used to control the dynamics of the plant, in this case the autonomous mobile robot, are shown in Figure 10.…”

Section: Fuzzy Controllermentioning

confidence: 99%

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Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Bernal

Castillo

Soria

et al. 2019

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Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobile robot. First, the galactic swarm optimization is tested for fuzzy controller optimization. Second, the GSO algorithm with the dynamic adjustment of parameters using T1 fuzzy systems is used for the optimization of the fuzzy controller of an autonomous mobile robot. Finally, the GSO algorithm with the dynamic adjustment of parameters using the IT2 fuzzy systems is applied to the optimization of the fuzzy controller. In the proposed approaches, perturbation (noise) was added to the plant in order to find out if our approach behaves well under perturbation to the autonomous mobile robot plant; additionally, we consider our ability to compare the results obtained with the approaches when no perturbation is considered.

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Section: Fuzzy Controllermentioning

confidence: 99%

“…Due to this, fuzzy controllers are used to deal with complex plants; however, the advantages offered by fuzzy logic and fuzzy sets, along with the appearance of the type-2 fuzzy logic, amplifies the probability of designing better fuzzy controllers [3,4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Bernal

Castillo

Soria

et al. 2019

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“…In addition to the applications of animal-inspired EAs to the water management area there have in-depth investigations that evaluated the efficiency of the animal-inspired EAs in other fields, specifically when coupling EAs with the fuzzy logic system (Olivas et al, 2017a;2017b;Perez et al, 2016;Sanchez et al, 2017;Valdez et al, 2017). Fuzzy systems can efficiently facilitate the process of parameter adaptation through controlling the solution diversity of EAs, such as PSO, BA, bee colony optimization (BCO).…”

Section: Introductionmentioning

confidence: 99%

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2020

Water & Environment J

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Successful operation of reservoir systems to guarantee the optimal use of available water resources has been the subject of many studies. The advent and applications of evolutionary algorithms (EAs) in the field of reservoir operation have led to significant advances in our capacity to improve the planning and management of complex reservoir systems. This study reports a review of the applications of animal‐inspired EAs to reservoir operation optimization selected among a large number of available papers in this area of research. The animal‐inspired EAs herein identified concern algorithms that mimic biologic traits of animal (wild) species. Among the animal‐inspired EAs ant colony optimization (ACO), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), honey bee mating optimization (HBMO), firefly algorithm (FA), cuckoo search (CS) and the bat algorithm (BA) are the best‐known ones selected for this review. This paper presents a brief description of the algorithmic characteristics and various employed improved versions or varieties thereof of each of the stated EAs. Furthermore, the differences between the proposed animal‐inspired EAs and their improved versions are identified by comparing the performance of the implemented animal‐inspired EAs in the reviewed literature. PSO and its varieties have the largest number of reported applications. Our comparison results revealed that constrained, discrete and randomized varieties of the animal‐inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time. Moreover, all the animal‐inspired EAs outperformed traditional methods of reservoir optimization, such as nonlinear programming (NLP) and dynamic programming (DP).

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“…For example, in [18], the firefly algorithm is used for the optimization of a fuzzy controller of a standalone mobile robot, which performs dynamic adjustment of the randomness parameter of the method. Also in [19], fuzzy dynamic adjustment is performed for optimization using galactic swarm optimization, among others, and [20][21][22] also describe applications of the method. This article proposes the development of a multi-metaheuristic competitive model using the firefly algorithm (FA), the wind-driven particle optimization algorithm (WDO), and the drone squad optimization (DSO) algorithm, where each of them competes with each other to demonstrate which of them is the best at solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers

et al. 2019

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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.

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Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

Cited by 54 publications

References 59 publications

Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers

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