Stepping ahead Firefly Algorithm and hybridization with evolution strategy for global optimization problems

Nand, Ravneil; Sharma, Bibhya N.; Chaudhary, Kaylash

doi:10.1016/j.asoc.2021.107517

Cited by 29 publications

(13 citation statements)

References 33 publications

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“…Algorithm 1 is a modified version of the original firefly algorithm that has been used in this study. Algorithm 1 is modified to discrete domain with preference mechanism from the one given in the previous work of the authors ( Nand, Chaudhary & Sharma, 2020b ; Nand, Sharma & Chaudhary, 2021 ). The modification of the algorithm is from continuous domain to discrete to suit uncapacitated examination timetabling problem.…”

Section: Proposed Algorithm: Preference Based Stepping Ahead Firefly ...mentioning

confidence: 99%

“…In step 3, if the solution of firefly i with respect to firefly j does not improve in terms of the fitness function over 10 generations, the algorithm looks for a new solution further in the space area of firefly j . The term coined to for the algorithm is Preference based Stepping Ahead ( Nand, Chaudhary & Sharma, 2020b ; Nand, Sharma & Chaudhary, 2021 ). The mechanism uses new solution as input of next move to find newer solution.…”

Section: Proposed Algorithm: Preference Based Stepping Ahead Firefly ...mentioning

confidence: 99%

“…The discrete modified FA is coined the term Preference-based Stepping ahead firefly algorithm. Discrete Stepping ahead mechanism is an extension of earlier work on the continuous domain by the authors ( Nand, Sharma & Chaudhary, 2021 ; Nand, Chaudhary & Sharma, 2020b ). The implementation first improves the proposed algorithm by providing better exploration through the neighbourhood searches and subsequently uses Stepping Ahead mechanism based on preference.…”

Section: Introductionmentioning

confidence: 99%

“… Preference-based Stepping ahead Model: A novel method for searching a larger search space in terms of exploration in order to locate better candidate solutions. Here, the algorithm is modified from authors previous work on continuous domain ( Nand, Chaudhary & Sharma, 2020b ; Nand, Sharma & Chaudhary, 2021 ) to discrete, to be used with examination timetabling problem. There was a limitation on when to initiate or activate the stepping ahead criteria, therefore, preference mechanism is utilized.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling

Nand¹,

Sharma²,

Chaudhary³

2022

PeerJ Computer Science

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In recent times, there has been a growing attention to intelligent optimization algorithms centred on swarm principles such as the firefly algorithm (FA). It was proposed for the continuous domain that mimics the attraction of fireflies to flashing light and has been used in discrete domains via modification. A discrete domain that is a major challenge in most higher education institutes (HEI) is examination timetabling. This article presents a new methodology based on FA for uncapacitated examination timetabling problems (UETP) where the proposed method is an extension of earlier work by the authors on the continuous domain. UETP is considered in this article as it is a university examination timetabling problem, which is still an active research area and has not been solved by FA algorithm as per authors knowledge. The proposed method concentrates on solving the initial solution using discrete FA where it consolidates the reordering of examinations and slots through a heuristic ordering known as neighborhood search. Three neighborhoods are employed in this research, where one is used during the initialization phase while two are utilized during solution improvement phase. Later, through preference parameters, a novel stepping ahead mechanism is used, which employs neighborhood searches built on previous searches. The proposed method is tested with 12 UETP problems where the preference based stepping ahead FA creates comparative results to the best ones available in the literature for the Toronto exam timetabling dataset. The results obtained are proof of concept at the preliminary stage and require further experiments on other educational datasets such as the second international timetable competition benchmark sets. The newly introduced preference based stepping ahead mechanism takes advantage of the current best solution space where it exploits the solution space for better solutions. This paves the way for researchers to utilize the mechanism in other domains such as robotics, etc.

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Section: Proposed Algorithm: Preference Based Stepping Ahead Firefly ...mentioning

confidence: 99%

Section: Proposed Algorithm: Preference Based Stepping Ahead Firefly ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling

Nand¹,

Sharma²,

Chaudhary³

2022

PeerJ Computer Science

Self Cite

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“…To improve the accuracy of the NMS-CS algorithm and avoid the NMS-CS algorithm falling into local optimization, a new movement strategy is proposed to modify the step size of cuckoo in position update. In order to further improve the performance of FA in global optimization problems and obtain better results, Nand et al proposed an improved firefly algorithm called FA-CMAES [ 35 ]. In FA-CMAES, a new step parameter is proposed to improve the exploitation ability of the algorithm, and the covariance matrix adaptation evolution strategy is embedded to improve the diversity of the population.…”

Section: Introductionmentioning

confidence: 99%

An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

Fan

Wang

2021

PLoS ONE

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Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a new metaheuristic algorithm, an improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism (TAVOA), is proposed. First, a tent chaotic map is introduced for population initialization. Second, the individual’s historical optimal position is recorded and applied to individual location updating. Third, a time-varying mechanism is designed to balance the exploration ability and exploitation ability. To verify the effectiveness and efficiency of TAVOA, TAVOA is tested on 23 basic benchmark functions, 28 CEC 2013 benchmark functions and 3 common real-world engineering design problems, and compared with AVOA and 5 other state-of-the-art metaheuristic optimization algorithms. According to the results of the Wilcoxon rank-sum test with 5%, among the 23 basic benchmark functions, the performance of TAVOA has significantly better than that of AVOA on 13 functions. Among the 28 CEC 2013 benchmark functions, the performance of TAVOA on 9 functions is significantly better than AVOA, and on 17 functions is similar to AVOA. Besides, compared with the six metaheuristic optimization algorithms, TAVOA also shows good performance in real-world engineering design problems.

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Convolutional Neural Network Design Using a Particle Swarm Optimization for Face Recognition

Melín

Sánchez

Pulido

et al. 2022

Lecture Notes in Networks and Systems

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Stepping ahead Firefly Algorithm and hybridization with evolution strategy for global optimization problems

Cited by 29 publications

References 33 publications

An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling

An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling

An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

Convolutional Neural Network Design Using a Particle Swarm Optimization for Face Recognition

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