Bat algorithm with parameter adaptation using Interval Type-2 fuzzy logic for benchmark mathematical functions

Pérez, Jonathan; Valdez, Fevrier; Castillo, Oscar; Roeva, Olympia

doi:10.1109/is.2016.7737409

Cited by 20 publications

(6 citation statements)

References 12 publications

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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%

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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Section: Proposed Methodsmentioning

confidence: 99%

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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“…The author introduced a new methodology in [44] of BAT Algorithm, in which strategy adopts a dynamic behavior of BAT parameters. The author is used an Interval Type-2 Fuzzy Logic to implement the introduced strategy.…”

Section: Mutation Strategiesmentioning

confidence: 99%

An Overview of Mutation Strategies in Bat Algorithm

Bangyal¹,

Ahmad²,

Tayyab³

et al. 2018

ijacsa

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Abstract-Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. BA has been mostly used to resolve diverse kind of optimization problems and one of major issue faced by BA is frequently captured in local optima meanwhile handling the complex real world problems. Many authors improved the standard BA with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This paper aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of BA. It has been tried to provide guidelines for the researchers who are active in the area of BA and its mutation strategies. The objective of this study is divided in two sections: primarily to display the improvement of BA with mutation strategies that may enhance the performance of standard BA up to great extent and secondly, to motivate the researchers and developers for using BA to solve the complex real world problems. This study presents a comprehensive survey of the various BA algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the BA algorithm in detail for the researcher.

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“…However, it also has some defects, such as excessively rapid convergence and a tendency to get trapped in local minima. To overcome these shortcomings, [11] uses interval type-2 fuzzy logic to change the parameters of BA and help the algorithm to jump out of local minimum cycles. Ref.…”

Section: Introductionmentioning

confidence: 99%

Suppression of Peak Sidelobe Level in Linear Symmetric Antenna Arrays Using Hybrid Grey Wolf and Improved Bat Algorithm

Zhang,

Chu,

Liu

et al. 2024

PIER Letters

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In this paper, the Hybrid Grey Wolf and Improved Bat Optimization Algorithm (HWIBO) is proposed to reduce the peak sidelobe level (PSLL) of linear symmetric array synthesis with aperture and element spacing constraints. The HWIBO utilizes both the Grey Wolf Optimization (GWO) and Improved Bat Algorithms (IBA) simultaneously to optimize PSLL. Each iteration generates two sets of results, and the optimal result is chosen for the next loop. Compared to other algorithms used in simulation of antenna sidelobe suppression, the HWIBO not only inherits the fast convergence advantage of the IBA which enhances population diversity but also possesses the strong global search capability of the GWO. This helps the IBA escape local optima and strengthens the global search capability during the later stages of algorithm iterations. Finally, the simulation results demonstrate the successful reduction of PSLL under various constraints, confirming the effectiveness of the hybrid algorithm.

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Bat algorithm with parameter adaptation using Interval Type-2 fuzzy logic for benchmark mathematical functions

Cited by 20 publications

References 12 publications

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

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

An Overview of Mutation Strategies in Bat Algorithm

Suppression of Peak Sidelobe Level in Linear Symmetric Antenna Arrays Using Hybrid Grey Wolf and Improved Bat Algorithm

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