A New Path Planning Method Based on Firefly Algorithm

Liu, Chang; Gao, Zhongqiang; Zhao, Wei

doi:10.1109/cso.2012.174

Cited by 49 publications

(27 citation statements)

References 2 publications

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“…This new method can be applied to a large number of real-time applications. Liu et al [11] proposed a path planning method based on firefly algorithm. After the detailed study of the algorithm some random parameters and absorption parameters were designed.…”

Section: Literature Reviewmentioning

confidence: 99%

Cuckoo Search in Test Case Generation and Conforming Optimality Using Firefly Algorithm

Choudhary

Gigras

Shilpa

et al. 2015

Advances in Intelligent Systems and Computing

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To accomplish the effectual software testing there is a requirement for optimization of test cases. The most challenging task in software testing is the generation of optimal test cases. There are various methods that are being used for generation of test cases and the test case optimization. The paper manifests the two different algorithms for test case generation and optimization of those test cases. The algorithms discussed are based on multi-objective optimization technique and successfully shows the desired results. The Cuckoo search algorithm based on the breeding behavior of Cuckoo bird is used here for the generation of test cases for a discussed problem and another algorithm based on the flashing phenomenon of fireflies is used for the optimization of the generated test cases. The second algorithm used verifies if every node in the given control flow graph is covered by given test cases.

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Section: Literature Reviewmentioning

confidence: 99%

Cuckoo Search in Test Case Generation and Conforming Optimality Using Firefly Algorithm

Choudhary

Gigras

Shilpa

et al. 2015

Advances in Intelligent Systems and Computing

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“…The firefly search algorithm and its variants have been applied to trajectory optimization [56][57][58], control parameter optimization [59,60], and dynamics [ [61][62][63] in what can be considered as an introductory investigation by looking for initial successes in applying the FA toward these astronautical research areas.…”

Section: Firefly Search Algorithmmentioning

confidence: 99%

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Cooper¹,

Smeresky²

2020

Advances in Spacecraft Attitude Control

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Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

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“…In short, 5 years since it is presented, FA has been successfully applied in many areas, such as vector quantization in image compression [41], traveling salesman problem [42], path planning [43,44], PID controller tuning [45], nonlinear optimization problem [46], job shop, Flowshop and project scheduling problems [47][48][49], data clustering [50] and so on. These research demonstrates the high efficiency of FA to solve the NP-hard and combinatorial optimization problem.…”

Section: Introductionmentioning

confidence: 99%

The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

Zhang

Zeng

et al. 2015

Int J Adv Manuf Technol

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This paper proposes a modified discrete firefly algorithm (MDFA) to solve the problem of assembly sequence planning. Firstly, to improve the performance of the firefly algorithm (FA), we proposed a MDFA by endowing the fireflies with the capability of changeable visual range. The computing case shows the proposed algorithm is more effective and robust than standard FA, genetic algorithm and particle swarm optimization algorithm. Secondly, a method of how to set parameters for FA and MDFA is proposed. This method is practical in the application of FA to solve discrete problem. Thirdly, to make the sequences more closer to real industrial requirements, a so called process precedence relations (PPR) evaluation function is presented, which not only considering the interference between parts, assembly tools and clamps, but also regarding the assembly order between parts and their reference parts. Finally, the evolution performance of the MDFA is investigated, and the performance of the proposed approach to solve ASP is verified through two cases study.

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A New Path Planning Method Based on Firefly Algorithm

Cited by 49 publications

References 2 publications

Cuckoo Search in Test Case Generation and Conforming Optimality Using Firefly Algorithm

Cuckoo Search in Test Case Generation and Conforming Optimality Using Firefly Algorithm

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

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