Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm

Pérez, Jonathan; Valdez, Fevrier; Castillo, Oscar; Melín, Patricia; González, Claudia I.; Martínez, Gonzalo

doi:10.1007/s00500-016-2469-3

Cited by 42 publications

(18 citation statements)

References 19 publications

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“…Some existing optimization algorithms that incorporate fuzzy-based mechanisms for dynamic parameter control include: (i) PSO, [40][41][42] (ii) ACO, 39,[43][44][45] (iii) bat algorithm, [46][47][48] and (iv) BCO. 38,[49][50][51] In the existing publications on dynamic parameter control of BCO using fuzzy logic, 38,49-51 various fuzzy-based systems have been introduced to dynamically control the 2 parameters employed in the solution construction mechanism, ie, alpha and beta.…”

Section: Fuzzy Logicmentioning

confidence: 99%

A dynamic fuzzy‐based dance mechanism for the bee colony optimization algorithm

Choong

Wong

Lim

2018

Computational Intelligence

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The bee colony optimization (BCO) algorithm with a linear dance function (denoted as the BCO-Linear algorithm) is inspired by the bees' foraging behaviors, in which waggle dances are modeled as a communication medium among bees. Through these informative waggle dances, more bees are recruited toward exploring more profitable search regions. In the BCO-Linear algorithm, a fitter bee is allowed to dance longer, and the dance duration is determined by a linear function with a scaling parameter that requires manual tuning. This article presents a dynamic fuzzy-based dance mechanism, ie, the BCO-Fuzzy algorithm, to solve the manual tuning problem. A fuzzy-based approach is applied to regulate the duration of waggle dances instead of regulating the dance duration using a linear function.The proposed BCO-Fuzzy algorithm comprises parameters that are dynamically controlled based on the feedback of the search process, therefore overcoming the limitation of manual parameter tuning of the BCO-Linear algorithm. The BCO-Fuzzy algorithm is evaluated comprehensively using a set of benchmark traveling salesman problems. The experimental results show that the performance of the BCO-Fuzzy algorithm is comparable with that of the BCO-Linear algorithm. Specifically, the dynamic fuzzy-based dance mechanism improves the BCO algorithm in terms of rewarding dance instances near the inflection point. Performance comparison with other Computational Intelligence. 2018;34:999-1024. wileyonlinelibrary.com/journal/coin

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Section: Fuzzy Logicmentioning

confidence: 99%

A dynamic fuzzy‐based dance mechanism for the bee colony optimization algorithm

Choong

Wong

Lim

2018

Computational Intelligence

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“…There is multiple works using Type-2 FLS applied to different optimization problems, the use of this technique significantly improves the results, consult in [2,5,20,25]. The principles of Type-2 FLS can be consulted in [17,[26][27][28][29]. We decided to combine fuzzy logic with our proposal based on works found in the literature, where it is shown that type 2 fuzzy controllers offer a higher performance when applied to robust problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…It has two driving wheels that are fixed to the axis that passes through the center of mass "C" represented by {C, Xm, Ym}, and one passive wheel that prevents the robot from tipping over as it moves on a plane [26,27]. The dynamics of the mobile robot is represented by the following set of Equations (6) and (7), [5,29,36]. ( ) ( ) ( ) …”

Section: Problem To Be Optimizedmentioning

confidence: 99%

“…The case selected to apply the proposed algorithm with Type-2 fuzzy approach is the problem of the autonomous robot [1,5,8,29,36], the dynamics of this problem is that the robot is able to follow an assigned trajectory with a minimum error value. In the proposed method an FLS is used to move the parameters of the membership functions of the fuzzy controller.…”

Section: Problem To Be Optimizedmentioning

confidence: 99%

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A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

2017

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Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

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“…Table 2 contains the fuzzy rules used for the fuzzy systems in Figures 3 and 4; these rules were designed based on several experiments to create knowledge of the parameters of PSO and how to control its behavior. The implementation of the bat algorithm integrated with a type-1 and interval type-2 fuzzy systems, in this case fuzzy systems aim to control the Beta and Pulse Rate parameters from BA, Figures 5 and 6 illustrate the fuzzy systems used for parameter adaptation, a type-1 fuzzy system and an interval type-2 fuzzy system, respectively; both fuzzy systems are Mamdani type, and the construction of these fuzzy systems is based in previous works [64]. The implementation of the bat algorithm integrated with a type-1 and interval type-2 fuzzy systems, in this case fuzzy systems aim to control the Beta and Pulse Rate parameters from BA, Figures 5 and 6 illustrate the fuzzy systems used for parameter adaptation, a type-1 fuzzy system and an interval type-2 fuzzy system, respectively; both fuzzy systems are Mamdani type, and the construction of these fuzzy systems is based in previous works [64].…”

Section: Bio-inspired Methods With Parameter Adaptationmentioning

confidence: 99%

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

et al. 2017

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Abstract:In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithms (original and modified versions) are compared with the design of fuzzy systems used for controlling the trajectory of an autonomous mobile robot. Simulation results reveal that PSO algorithm outperforms the results of the BCO and BA algorithms.

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Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm

Cited by 42 publications

References 19 publications

A dynamic fuzzy‐based dance mechanism for the bee colony optimization algorithm

A dynamic fuzzy‐based dance mechanism for the bee colony optimization algorithm

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

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

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